Abstract:
An information processing apparatus, information processing method and computer program product cooperate in detecting a first target object contained in image information. Image information of an image is received through an interface. A processing circuit determines whether a first target object in the image information has not yet been detected. A determination is also made regarding whether a second target object in the image information has been detected. An image quality parameter of the image is modified to assist subsequent detection attempts in recognizing the first target object when the first target object has not yet been detected but the second target object has been detected.

Description:
BACKGROUND 
     The present disclosure relates to an information processing device, method, and computer program product, and in particular, to an information processing device, method and a computer program product which more reliably recognizes a target object. 
     As shown in  FIG. 1 , operation of an electrical apparatus such as a television image reception device  12  is performed by a hand  2  of a user  1  being recognized using a camera  11  and using a gesture of a pattern of movement of the hand  2 . 
     In addition, the face of a user is recognized in a case of imaging using a camera and automatic controlling of parameters such as focus and balance of color is performed based on information on the recognized face. 
     An image recognition device for executing processing in such a manner recognizes, for example, people, faces, and hands (for example, refer to Unexamined Japanese Patent Application Publication 2010-108475). In addition, among image recognition devices, there are also devices which recognize postures of people (for example, refer to Unexamined Japanese Patent Application Publication 11-128535). 
     SUMMARY 
     However, in a case where an obtained image is not an appropriate image, an image recognition device is not able to recognition a target object. 
       FIGS. 2A to 3B  show examples of images which are not appropriate for recognition of a target object.  FIG. 2A  shows an example of an image which is too bright.  FIG. 2B  shows an example of an image which is too dark.  FIG. 3A  shows an example of an image which is out of focus.  FIG. 3B  shows an example where a recognized target object (for example, a hand  5  or a face  6 ) is small. 
     In such a case, it is difficult for the image recognition device to recognize target objects such as the hand  5  and the face  6 . 
     It is desirable that it is possible to more reliably recognize a target object. 
     According to one embodiment, an information processing apparatus includes an interface that receives image information of an image; and a processing circuit that determines whether a first target object in the image information has not yet been detected, determines that a second target object in the image information has been detected, and modifies an image quality parameter of the image to recognize the first target object in a subsequent detection attempt, when it is determined that the first target object is not detected but the second target object is detected. 
     According to one aspect of the embodiment, the first target object is related to the second target object by at least one of a relative size and the first target object being a part of the second target object. 
     According to another aspect of the embodiment the image quality parameter is one of a camera control parameter, and an image processing parameter. 
     According to another aspect of the embodiment the image quality parameter is the camera control parameter, and the camera control parameter is one of a zoom ratio, a focus position, an aperture value, a shutter time, a gain value, a color adjustment value, and a camera orientation direction. 
     According to another aspect of the embodiment the image quality parameter is the image processing parameter, and the image processing parameter is one of a brightness setting, a contrast setting, a color conversion, a sharpness value, a focus setting, and a digital zoom ratio. 
     According to another aspect of the embodiment the first target object is a part of a human body, and the second target object is the human body. 
     According to another aspect of the embodiment the first target object is a part of a machine, and the second target object is the machine. 
     According to another aspect of the embodiment the processing circuit modifies the image quality parameter and recognizes the first target object in the subsequent detection attempt in real time. 
     According to another aspect of the embodiment the processing circuit further detects a posture of at least one of the part of the human body and the human body. 
     According to another aspect of the embodiment a camera, the camera being one of a digital still camera and a television camera. 
     According to an image processing method embodiment, the method includes receiving image information of an image through an interface; determining with a processing circuit whether a first target object in the image information has not yet been detected; determining that a second target object in the image information has been detected; and modifying an image quality parameter of the image to recognize the first target object in a subsequent detection attempt, when it is determined that the first target object is not detected but the second target object is detected. 
     According to one aspect of this embodiment the first target object is related to the second target object by at least one of a relative size and the first target object being a part of the second target object. 
     According to another aspect of the embodiment the image quality parameter is one of a camera control parameter, and an image processing parameter. 
     According to another aspect of the embodiment the image quality parameter is the camera control parameter, and the camera control parameter is one of a zoom ratio, a focus position, an aperture value, a shutter time, a gain value, a color adjustment value, and a camera orientation direction. 
     According to another aspect of the embodiment the image quality parameter is the image processing parameter, and the image processing parameter is one of a brightness setting, a contrast setting, a color conversion, a sharpness value, a focus setting, and a digital zoom ratio. 
     According to another aspect of the embodiment the first target object is a part of a human body, and the second target object is the human body. 
     According to another aspect of the embodiment the first target object is a part of a machine, and the second target object is the machine. 
     According to another aspect of the embodiment the modifying and recognizing the first target object in the subsequent detection attempt are performed in real time. 
     According to another aspect of the embodiment detecting a posture of at least one of the part of the human body and the human body. 
     According to a non-transitory computer readable storage device embodiment, the storage device has instructions stored therein that when executed by a processing circuit perform a method, the method including receiving image information of an image; determining with the processing circuit whether a first target object in the image information has not yet been detected; determining that a second target object in the image information has been detected; and modifying an image quality parameter of the image to recognize the first target object in a subsequent detection attempt, when it is determined that the first target object is not detected but the second target object is detected. 
     According to the embodiments of the disclosure, it is possible to more reliably recognize a target object. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating an example of gesture recognition using a camera; 
         FIGS. 2A and 2B  are diagrams illustrating examples of images which are not appropriate for recognition; 
         FIGS. 3A and 3B  are diagrams illustrating examples of images which are not appropriate for recognition; 
         FIG. 4  is a block diagram illustrating a configuration example of hardware of a personal computer according to an embodiment of the disclosure; 
         FIG. 5  is a block diagram illustrating a functional configuration example of a CPU; 
         FIG. 6  is a block diagram illustrating a detailed configuration example of a second recognition section; 
         FIG. 7  is a block diagram illustrating a detailed configuration example of a contour feature quantity calculation section; 
         FIG. 8  is a diagram illustrating a result where filter processing has been performed on an image; 
         FIG. 9  is a flow chart describing a recognition process; 
         FIG. 10  is a diagram describing about extraction of target object feature points; 
         FIGS. 11A and 11B  are diagrams describing about extraction of contour feature points; 
         FIG. 12  is a flow chart describing a contour feature quantity calculation process; 
         FIG. 13  is a diagram illustrating an example of a recognition result; 
         FIG. 14  is a flow chart describing a target object recognition process; 
         FIGS. 15A to 15C  are diagrams illustrating examples of recognition results; 
         FIGS. 16A and 16B  are diagrams illustrating examples of relative position conditions; 
         FIGS. 17A and 17B  are diagrams illustrating examples of determination results of relative position conditions; 
         FIGS. 18A and 18B  are diagrams illustrating examples of relative size comparison conditions; 
         FIGS. 19A and 19B  are diagrams illustrating examples of determination results of relative size comparison conditions; 
         FIGS. 20A to 20C  are diagrams illustrating examples of recognition results; 
         FIG. 21  is a block diagram illustrating a detailed configuration example of a posture recognition section; 
         FIG. 22  is a flow chart describing an example of a target object recognition process; 
         FIGS. 23A to 23C  are diagrams illustrating examples of recognition results; 
         FIG. 24  is a flow chart describing an example of a posture recognition process; 
         FIG. 25  is a diagram illustrating an example of relative distance and relative angle conditions; 
         FIGS. 26A and 26B  are diagrams illustrating examples of a determination result of relative distance condition; 
         FIG. 27  is a block diagram illustrating a configuration example of a television image reception device control system according to an embodiment of the disclosure; and 
         FIG. 28  is a block diagram illustrating a configuration example of a digital camera control system according to an embodiment of the disclosure. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     The embodiments of the disclosure will be described below. Here, the description will be given in the following order.
     1. Configuration of Personal Computer   2. Configuration of Second Recognition Section   3. Configuration of Contour Feature Quantity Calculation Section   4. Recognition Process   5. Contour Feature Quantity Calculation Process   6. Target Object Recognition Process 1   7. Target Object Recognition Process 2   8. Configuration of Posture Recognition Section   9. Target Object Recognition Process 3   10. Posture Recognition Process   11. Configuration of Television Image Reception Device Control System   12. Configuration of Digital Camera Control System   13. Other
 
Configuration of Personal Computer
   

       FIG. 4  is a block diagram which shows a configuration example of hardware of a personal computer  21  according to an embodiment of the disclosure. 
     In the personal computer  21  which is an information processing device, a CPU (Central Processing Unit)  41 , a ROM (Read Only Memory)  42 , and a RAM (Random Access Memory)  43  are connected to each other by a bus  44 . 
     In the bus  44 , an input/output interface  45  is further connected. In the input/output interface  45 , an input section  46 , an output section  47 , a storage section  48 , a communication section  49 , and a drive  50  are connected. 
     Other than a keyboard, a mouse, a microphone, or the like, the input section  46  has a camera  46 A. Other than a speaker or the like, the output section  47  has a display  47 A. 
     The camera  46 A is arranged in a position where it is possible to capture images of a user who views the display  47 A. The display  47 A displays images which correspond to various applications, and when necessary, images captured using the camera  46 A. 
     The storage section  48  is formed by a hard disk, a nonvolatile memory, or the like. The communication section  49  is formed by a network interface or the like. The drive  50  operates a removable medium  51  such as a magnetic disc, an optical disc, a magneto-optical disc, or a semiconductor memory. 
     In the personal computer  21  configured in this manner, the CPU  41  performs various processes, for example, by a program stored in the storage section  48  being loaded into the RAM  43  via the input/output interface  45  and the bus  44  and executed. 
     The program executed by the CPU  41  is provided by, for example, being stored in the removable medium  51  which is a package medium or the like. 
     Here, as the package medium, a magnetic disc (including a floppy disc), an optical disc (a CD-ROM (Compact Disc-Read Only Memory), a DVD (Digital Versatile Disc), or the like), a magneto-optical disc, a semiconductor memory, or the like may be used. 
     In addition, it is possible for the program to be provided via a wired or a wireless transmission medium such as a local area network, the internet, or digital satellite broadcasting. 
     In the personal computer  21 , it is possible for the program to be installed in the storage section  48  via the input/output interface  45  by the removable medium  51  being mounted in the drive  50 . 
     In addition, it is possible for the program to be received by the communication section  49  via a wired or a wireless transmission medium and installed in the storage section  48 . Further, it is possible for the program to be installed in advance in the ROM  42  or the storage section  48 . 
       FIG. 5  is a block diagram which shows a functional configuration example of the CPU  41 . 
     The CPU  41  has function blocks of an acquisition section  61 , a first recognition section  62 , a second recognition section  63 , a determination section  64 , a control section  65 , an output section  66  and an application execution section  67 . Here, it is possible for each block of the CPU  41  to transfer signals and data to each other when necessary. 
     The acquisition section  61  acquires various types of information. The first recognition section  62  and the second recognition section  63  perform a recognition process of recognition target objects which are each different such as hands, faces and people. For example, the first recognition section  62  recognizes hands as the recognition target object and the second recognition section  63  recognizes people as the recognition target object. 
     The determination section  64  determines various types of information. The control section  65  performs various types of control processes. The output section  66  outputs various types of information. The application execution section  67  executes applications. 
     As applications, there are, for example, applications which recognize a hand gesture in an image and execute commands which correspond to the gesture and applications which recognize a face in an image and improve the image quality of a face region. 
     That is, the application execution section  67  executes an application which recognizes a target object in an image and performs a predetermined process using the recognition result. 
     Configuration of Second Recognition Section 
     The first recognition section  62  which recognizes hands has the same configuration to the second recognition section  63  which recognizes people. Here, the configuration of the second recognition section  63  will be described as a representative.  FIG. 6  is a block diagram which shows a more detailed configuration example of the second recognition section  63 . 
     The second recognition section  63  is configured from an image pyramid generating section  81 , a target object feature point extraction section  82 , a target object feature quantity calculation section  83 , a contour feature point extraction section  84 , a contour feature quantity calculation section  85 , a classification calculation section  86 , and a recognition result output section  87 . 
     It is possible for each section to transfer signals and data to each other when necessary. This is the same as in  FIGS. 6 ,  7 , and  21  which will be described later. 
     From an acquired image, the image pyramid generating section  81  generates a plurality of images which are each different in resolution as an image pyramid. 
     For example, an image pyramid with layers of eight resolutions from level  1  to level  8  is generated. Level  1  of the image pyramid has the highest resolution and the resolution of the image pyramid becomes lower in order from level  1  to level  8 . 
     The target object feature point extraction section  82  extracts several pixels of an image from each image, which constitutes the image pyramid generated by the image pyramid generating section  81 , as a target object feature point used when recognizing a target object. 
     With regard to each of the target object feature points extracted by the target object feature point extraction section  82 , the target object feature quantity calculation section  83  performs pairing where a target object feature point and another target object feature point are made to be one pair. 
     In addition, the target object feature quantity calculation section  83  calculates a target object feature quantity which indicates the distance of the texture of two arbitrary regions for each pair of target object feature points based on the image where the target object feature points have been extracted. 
     The contour feature point extraction section  84  extracts several pixels of an image from each image, which constitutes the image pyramid generated by the image pyramid generating section  81 , as a contour feature point used when recognizing a target object. 
     The contour feature quantity calculation section  85  calculates a contour feature quantity which indicates the extracted contour for each of the contour feature points by a filter process using, for example, a steerable filter, based on the image where the contour feature points have been extracted. 
     The classification calculation section  86  reads out the feature quantity for classification and the composite classifier stored in advance in the storage section  48 . In addition, as a substitute for the read-out composite classifier, the classification calculation section  86  performs calculation of a correspondence to the feature quantity for classification out of the target object feature quantity calculated by the target object feature quantity calculation section  83  or the contour feature quantity calculated by the contour feature quantity calculation section  85 . 
     Here, the composite classifier is generated by integrating a target object classifier and a contour classifier. 
     The target object classifier and the contour classifier are strong classifiers formed from a plurality of weak classifiers generated in advance using statistical learning and are used when identifying whether or not a region of a target object image is present in an input image using features of the target object (for example, a person). 
     In addition, the feature quantity for classification is the target object feature quantity of the pair of target object feature points used when performing recognition of a target object using the composite classifier and the contour feature quantity of the contour feature points. 
     The recognition result output section  87  outputs a classification result of whether or not a target object is recognized in an input image based on the calculation result of the classification calculation section  86 . 
     As described above, since the first recognition section  62  has the same configuration and performs the same processes as the second recognition section  63 , a detailed description thereof is omitted. 
     Configuration of Contour Feature Quantity Calculation Section 
       FIG. 7  is a block diagram which shows a detailed configuration example of a contour feature quantity calculation section  85  in  FIG. 6 . The contour feature quantity calculation section  85  is configured from a first filter processing section  101 , a second filter processing section  102 , a third filter processing section  103 , and a feature quantity generating section  104 . 
     The image where the contour feature points have been extracted by the contour feature point extraction section  84  is supplied to the first filter processing section  101  to the feature quantity generating section  104  and the contour feature points are supplied to the first filter processing section  101  to the third filter processing section  103 . 
     For each of the contour feature points extracted using the contour feature point extraction section  84 , the first filter processing section  101  extracts the feature quantity by executing a filter process using a first order differential function G 1  of a Gaussian function G with regard to the contour feature points. Here, the Gaussian function G and the first order differential function G 1  are shown by equation (1) and equation (2). 
     
       
         
           
             
               
                 
                   G 
                   = 
                   
                     ⅇ 
                     
                       - 
                       
                         
                           
                             x 
                             2 
                           
                           + 
                           
                             y 
                             2 
                           
                         
                         
                           2 
                           ⁢ 
                           
                             σ 
                             2 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       G 
                       1 
                     
                     ⁡ 
                     
                       ( 
                       θ 
                       ) 
                     
                   
                   = 
                   
                     
                       
                         cos 
                         ⁡ 
                         
                           ( 
                           θ 
                           ) 
                         
                       
                       ⁢ 
                       
                         
                           G 
                           1 
                         
                         ⁡ 
                         
                           ( 
                           
                             0 
                             ⁢ 
                             ° 
                           
                           ) 
                         
                       
                     
                     + 
                     
                       
                         sin 
                         ⁡ 
                         
                           ( 
                           θ 
                           ) 
                         
                       
                       ⁢ 
                       
                         
                           G 
                           1 
                         
                         ⁡ 
                         
                           ( 
                           
                             90 
                             ⁢ 
                             ° 
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     In equation (1), σ indicates Gaussian width. In equation (w), θ indicates an arbitrary angle. 
     For example, the first filter processing section  101  changes the Gaussian width a of the Gaussian function G into three predetermined values (for example, Gaussian widths σ 1 , σ 2 , σ 3 =1, 2, 4) and calculates equation (2) with regard to four predetermined directions (for example, θ=θ 1 , θ 2 , θ 3 , θ 4 ) for each of the Gaussian widths σ. 
     Here, the direction θ is not limited to four directions and may be eight directions such as each direction when π is equally divided into eight directions. In addition, in the past, processing was performed using a plurality of Gaussian widths, but in the embodiment, it is sufficient if only one Gaussian width is prepared as will be described later. 
     In other words, it is not necessary to change the Gaussian width. As such, in the description above, it is described that “the Gaussian width is changed into three predetermined values and equation (2) is calculated with regard to four predetermined directions for each of the Gaussian widths σ”, but in the embodiment, it is sufficient if only equation (2) is calculated with regard to four predetermined directions for the set Gaussian width σ. 
     As such, since it is not necessary to calculate for each of a plurality of Gaussian widths, it is possible to reduce the quantity of calculation. This is the same for other filters, for example, the second filter processing section  102  and the third filter processing section  103 . 
     For each of the extracted contour feature points, the second filter processing section  102  extracts the feature quantity by executing a filter process using a second order differential function G 2  of the Gaussian function G with regard to the contour feature points. Equation (3) shows the second order differential function G 2 , and in equation (3), θ indicates an arbitrary angle.
 
 G   2 (θ)= k   21 (θ) G   2 (0°)+ k   22 (θ) G   2 (60°)+ k   23 (θ) G   2 (120°)  (3)
 
     In addition, the coefficient k 2i (θ) (where i=1, 2, 3) in equation (3) is a function shown by equation (4). 
     
       
         
           
             
               
                 
                   
                     
                       k 
                       
                         2 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         i 
                       
                     
                     ⁡ 
                     
                       ( 
                       θ 
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       3 
                     
                     ⁢ 
                     
                       { 
                       
                         1 
                         + 
                         
                           2 
                           ⁢ 
                           
                             cos 
                             ⁡ 
                             
                               ( 
                               
                                 2 
                                 ⁢ 
                                 
                                   ( 
                                   
                                     θ 
                                     - 
                                     
                                       θ 
                                       ⁢ 
                                       
                                           
                                       
                                       ⁢ 
                                       i 
                                     
                                   
                                   ) 
                                 
                               
                               ) 
                             
                           
                         
                       
                       } 
                     
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
     For example, the second filter processing section  102  calculates equation (3) with regard to four predetermined directions (for example, θ=θ 1 , θ 2 , θ 3 , θ 4 ) for each of the predetermined Gaussian widths σ of the Gaussian function G. 
     For each of the extracted contour feature points, the third filter processing section  103  extracts the feature quantity by executing a filter process using a third order differential function G 3  of the Gaussian function G with regard to the contour feature points. 
     Equation (5) shows the third order differential function G 3 , and in equation (5), θ indicates an arbitrary angle.
 
 G   3 (θ)= k   31 (θ) G   3 (0°)+ k   32 (θ) G   3 (45°)+ k   33 (θ) G   3 (90°)+ k   34 (θ) G   3 (135°)  (5)
 
     In addition, the coefficient k 3i (θ) (where i=1, 2, 3) in equation (5) is a function shown by equation (6). 
     
       
         
           
             
               
                 
                   
                     
                       k 
                       
                         3 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         i 
                       
                     
                     ⁡ 
                     
                       ( 
                       θ 
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       4 
                     
                     ⁢ 
                     
                       { 
                       
                         
                           2 
                           ⁢ 
                           
                             cos 
                             ⁡ 
                             
                               ( 
                               
                                 θ 
                                 - 
                                 
                                   θ 
                                   ⁢ 
                                   
                                       
                                   
                                   ⁢ 
                                   i 
                                 
                               
                               ) 
                             
                           
                         
                         + 
                         
                           2 
                           ⁢ 
                           
                             cos 
                             ⁡ 
                             
                               ( 
                               
                                 3 
                                 ⁢ 
                                 
                                   ( 
                                   
                                     θ 
                                     - 
                                     
                                       θ 
                                       ⁢ 
                                       
                                           
                                       
                                       ⁢ 
                                       i 
                                     
                                   
                                   ) 
                                 
                               
                               ) 
                             
                           
                         
                       
                       } 
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     For example, the third filter processing section  103  calculates equation (5) with regard to four predetermined directions (for example, θ=θ 1 , θ 2 , θ 3 , θ 4 ) for each of the predetermined Gaussian widths σ of the Gaussian function G. 
     The feature quantity generating section  104  receives a supply of the feature quantities of each of the contour feature points calculated with regard to the four directions θ from each of the first filter processing section  101 , the second filter processing section  102  and the third filter processing section  103 , lines up a total of the 12 (=3 (orders)×4 (directions) supplied feature quantities, and sets the feature quantities as the contour feature quantities for the contour feature points. 
     In addition, in each of the filter processing section  101 ,  102  and  103 , since a plurality of images with different resolutions are supplied from the image pyramid generating section  81 , the feature quantities of each of the contour feature points calculated with regard to the four directions θ from each of the images are also supplied. 
     The supplied feature quantity depends on the number of image generated by the image pyramid generating section  81 , and for example, in a case where eight images from level  1  to level  8  are generated, the feature quantities of each of the contour feature points calculated with regard to four directions of eight images are supplied. 
     In this manner, in the contour feature quantity calculation section  85 , the feature quantity (contour) which is different for each order of differentiation is extracted using a filter (basis function), which is obtained by differentiating a Gaussian function and has an ability to selection the direction θ, and are set as the contour feature quantity. 
       FIG. 8  shows a result obtained by performing a filter process using a differential function of the Gaussian function G with regard to an image where a person is imaged. In  FIG. 8 , images which are targets of a filter process are shown on the left side, filters are shown in the center, and images after the filter process are shown on the right side. 
     The images shown on the left side of  FIG. 8  are two images which configure an image pyramid and are images with different resolutions. For example, the image pyramid generating section  81  generates images with different resolutions from level  1  to level  8 . Out of the images, for example, an image  131  which is level  1  and an image  132  which is level  2  are shown on the left side of  FIG. 8 . 
     The filters shown in the center of  FIG. 8  show an example of filters where the Gaussian width σ of the first order differential function G 1 , the second order differential function G 2 , and the third order differential function G 3  is equal to one. 
     The images shown on the right side of  FIG. 8  are generated in a case where, out of the filters shown in the center of  FIG. 8 , for example, the filter with the third order differential function G 3  is used and a filter process is performed with regard to the images  131  and  132  shown on the left side of  FIG. 8 . 
     That is, when a filter process using the filter with the third order differential function G 3  is performed with regard to the image  131 , images  141 - 1  to  141 - 4  are generated. In addition, when a filter process using the filter with third order differentiation is performed with regard to the image  132 , images  142 - 1  to  142 - 4  are generated. 
     Since the filter process was performed using the filter with the third order differential function G 3  where the Gaussian width σ is one (σ=1), the images  141 - 1  to  141 - 4  are each images when a filter process is performed using a filter where the Gaussian width σ is one. 
     In the same manner, since the filter process was performed using the filter with the third order differential function G 3  where the Gaussian width σ is one (σ=1), the images  142 - 1  to  142 - 4  are each images when a filter process is performed using a filter where the Gaussian width σ is one. 
     However, the images  142 - 1  to  142 - 4  are images where the filter process was performed on the image  132 . The image  132  is an image where the image  131  has been reduced in size. 
     In such a case, it is possible for the images  142 - 1  to  142 - 4  to be made to be images which correspond to images generated as a result of a filter process being performed using a filter with a third order differential function G 3  where the Gaussian width σ is two (σ=2). 
     In order words, the images  142 - 1  to  142 - 4  are images which correspond to images which are generated when a filter process is performed using a filter with a third order differential function G 3  where the Gaussian width σ is two (σ=2) with regard to the image  131 . 
     That is, by performing a filter process with regard to the image pyramid, it is possible to obtain the same images when the filter process has been performed using filters with different Gaussian widths. 
     For example, compared to the calculation quantity and processing burden when a filter is prepared in advance for each Gaussian width and a filter process are performed, the calculation quantity and processing burden are significantly reduced when a filter with one Gaussian width is prepared in advance and a filter process are performed on an image pyramid as described above. 
     That is, by generating an image pyramid and performing a filter process with one Gaussian width, it is possible to significantly reduce the processing time. 
     As such, using such a method, in a case where a target object such as a person is to be detected from an image, by reducing the processing time, it is possible to detect the target object in the image in real time. 
     In this manner, the contour feature quantity calculation section  85  generates an image obtained by further averaging a plurality of images which correspond to when a filter process has been performed using filters where the Gaussian widths σ are different. From the averaged image which is generated, it is possible to confirm contours of a person, and contours of a person are able to be appropriately extracted from an image using a filter process using each filter. 
     Recognition Process 
     Next, a recognition process of the first recognition section  62  and the second recognition section  63  where a target object is recognized will be described with reference to  FIGS. 9 to 13 . 
     Here, the recognition process of the first recognition section  62  where a hand is the recognition target object and the recognition process of the second recognition section  63  where a person is the recognition target object are only different in terms of their recognition target objects, and since the processes are the same, in order for simplification, only the recognition process of the second recognition section  63  where a person is the recognition target object will be described. 
       FIG. 9  is a flow chart describing a recognition process. The process in  FIG. 9  is executed when an image is obtained using the camera  46 A. 
     In step S 1 , the image pyramid generating section  81  in the second recognition section  63  generates an image pyramid from the image obtained using the camera  46 A. 
     As described above, the image pyramid generating section  81  generates the image pyramid, for example, with layers of eight resolutions from level  1  to level  8 . 
     Each of the target object feature point extraction section  82  and the contour feature point extraction section  84  execute the process of step S 2  onward with one image out of the supplied image pyramid (a plurality of images with different resolutions) as the process target image and executes the process of step S 2  onward repeatedly for each of the plurality of images. 
     In step S 2 , the target object feature point extraction section  82  extracts the target object feature points from the image which is the process target. 
     In step S 3 , the target object feature quantity calculation section  83  selects one of the target object feature points which have not yet been processed as a focus point from the target object feature points extracted in step S 2 . 
     In step S 4 , the target object feature quantity calculation section  83  performs pairing of the target object feature points with regard to each target object feature point based on the target object feature points extracted by the target object feature point extraction section  82  and the image. 
     In step S 5 , the target object feature quantity calculation section  83  calculates the target object feature quantity with regard to each pair of the target object feature points which were paired due to the pairing. 
     For example, in a case where the image shown in  FIG. 10  is the target of the process using the target object feature point extraction section  82 , the target object feature point extraction section  82  extracts target object feature points  145  from the image based on a margin determined in advance and a sampling skip number. 
     Here, in  FIG. 10 , the circles on the image show pixels which are target object feature points  145 . In addition, for convenience, the reference numeral is only attached to one circle on the image. 
     Here, the margin is the number of pixels in the image from the edge of the image to the target region for the extraction of the target object feature points  145 . In addition, the sampling skip number is an interval from pixel to pixel which are target object feature points  145  in the image. 
     Accordingly, for example, in a case where the margin is five pixels and the sampling skip number is five pixels, the target object feature point extraction section  82  excludes the region formed from pixels in positions five or less pixels from the edge of the image and a region E 11  which remains at the inner side thereof is the target for the extraction of the target object feature points. 
     Then, the target object feature point extraction section  82  extracts the pixels in positions, which are separated from each other by only five pixels, out of the pixels in the region E 11  as the target object feature points  145 . 
     That is, the distance between the target object feature points  145  adjacent to each other in the vertical direction or the horizontal direction in  FIG. 10  is five pixels, and each of the target object feature points  145  are pixels inside the region E 11  (processes of S 2  and S 3 ). 
     Next, the target object feature quantity calculation section  83  performs pairing of each of the target object feature points  145  based on a minimum radius and a maximum radius determined in advance. 
     For example, in a case where the minimum radius is R 11  and the maximum radius is R 12 , when focusing on a predetermined target object feature point KT 1 , the target object feature quantity calculation section  83  makes one pair out of the target object feature point  145  and the target object feature point KT 1  with regard to all of the target object feature points  145  where the distance from the target object feature point KT 1  is equal to or more than the minimum radius R 11  and equal to or less than the maximum radius R 12 . 
     For example, in a case where there are N target object feature points  145  where the distance from the target object feature point KT 1  is equal to or more than the minimum radius R 11  and equal to or less than the maximum radius R 12 , N pairs of the target object feature points  145  are obtained. 
     The target object feature quantity calculation section  83  perform pairing of the other target object feature points  145  with regard to all of the target object feature points  145  (process of step S 4 ). 
     Further, the target object feature quantity calculation section  83  calculates the texture distances of regions with the same shape and same size, which are centered on each target object feature point  145  which forms a pair, as the target object feature quantity with regard to each of the pairs of the target object feature points  145  obtained due to pairing. 
     For example, in a case where the target object feature quantity with regard to a pair of the target object feature point KT 1  and a target object feature point KT 2  shown in  FIG. 10  is determined using SSD (Sum of Square Distance), the target object feature quantity calculation section  83  sets a predetermined region which is centered on the target object feature point KT 1  as a region T×1 and sets a region which is centered on the target object feature point KT 2  and is the same size as the region T×1 as a region T×2. 
     Then, the target object feature quantity calculation section  83  determines the total absolute value of the differences of the pixel values of the pixels in the region T×1 and the pixel values of the pixels in the region T×2 which corresponds to the pixels and the total absolute value of the determined differences is the target object feature quantity. 
     Here, the target object feature quantity is not limited to SSD and may be SAD (Sum of Absolute Distance), a normalized correlation, or the like (process of step S 5 ). 
     Returning to  FIG. 9 , in step S 6 , the target object feature quantity calculation section  83  determines whether the processes have been completed with regard to all of the target object feature points. 
     In a case where it is determined in step S 6  that the processes have not yet been completed with regard to all of the target object feature points  145 , the process returns to step S 3 , the next target object feature point  145  is selected as the focus point, and the same process is executed. 
     On the other hand, in a case where it is determined in step S 6  that the processes have been completed with regard to all of the target object feature points  145 , the process proceeds to step S 7 . 
     In step S 7 , the contour feature point extraction section  84  extracts contour feature points from the image which was generated in step S 1  and is the process target. 
     For example, in a case where the image shown in  FIG. 11A  is input in the contour feature point extraction section  84 , the contour feature point extraction section  84  extracts pixels which are lined up in predetermined intervals in the image as contour feature points  146  as shown in  FIG. 11B . 
     Here, in  FIG. 11B , the circles on the image show pixels which are contour feature points  146 . In addition, for convenience, the reference numeral is only attached to one circle on the image. 
     The image shown in  FIG. 11B  is an image formed from 32 pixels in the horizontal direction and 64 pixels in the vertical direction in the diagram. The contour feature point extraction section  84  selects pixels in the image as pixels which become the contour feature points  146  in every second pixel in the horizontal direction and the vertical direction. 
     According to this, 12 pixels in the horizontal direction and 28 pixels in the vertical direction for a total of 336 (=12×28) pixels are selected as the contour feature points  146 . 
     When the contour feature points  146  are extracted from the image, the contour feature point extraction section  84  supplies the extracted contour feature points  146  and the input image to the contour feature quantity calculation section  85 . 
     In step S 8 , the contour feature quantity calculation section  85  performs a contour feature quantity calculation process and calculates the contour feature quantity for each of the contour feature points  146  based on the contour feature points  146  and the image supplied from the contour feature point extraction section  84 . 
     Contour Feature Quantity Calculation Process 
     Here, the contour feature quantity calculation process which corresponds to the process of step S 8  in  FIG. 9  will be described with reference to the flow chart in  FIG. 12 . 
     In step S 21 , the contour feature quantity calculation section  85 , or in more detail, each of the first filter processing section  101 , the second filter processing section  102 , and the third filter processing section  103  of the contour feature quantity calculation section  85 , select one of the contour feature points which have not yet been processed as a focus point from the contour feature points supplied from the contour feature point extraction section  84 . 
     In step S 22 , the contour feature quantity calculation section  85  sets a counter q which indicates a direction θq as one. Due to this, the direction θq is set as a direction θ 1 . 
     In step S 23 , the first filter processing section  101  performs the first filter process. 
     That is, based on the pixel value of the focus pixel which is the process target, the first filter processing section  101  sets the Gaussian width so that σ=1, calculates equation (2) with the direction as θq, and supplies the result of the filter process to the feature quantity generating section  104 . 
     That is, the calculation is performed and the contours are extracted with the direction θ in equation (2) is set as θq. 
     Here, “the Gaussian width is set so that σ=1” is written, but in the case of the embodiment, since the Gaussian width is fixed so that σ=1 (the filter with one Gaussian width is set in advance), it is possible to omit the process where “the Gaussian width is set so that σ=1”. 
     That is, in the embodiment, the process, where equation (2) is calculated where the filter direction with the Gaussian width σ of one is set as the direction θq, is executed in step S 23 . 
     In addition, here, the description where the Gaussian width σ is set so that σ=1 will continue, but the Gaussian width of the filter prepared in advance may have a Gaussian width other than σ=1. 
     In step S 24 , the second filter processing section  102  performs the second filter process. 
     That is, based on the pixel value of the focus pixel, the second filter processing section  102  calculates equation (3) with the filter direction where the Gaussian width σ of one is set as θq and supplies the result of the filter process to the feature quantity generating section  104 . 
     That is, the calculation is performed and the contours are extracted with the direction θ in equation (3) is set as θq. 
     In step S 25 , the third filter processing section  103  performs the third filter process. 
     That is, based on the pixel value of the focus pixel, the third filter processing section  103  calculates equation (5) with the filter direction where the Gaussian width σ of one is set as θq and supplies the result of the filter process to the feature quantity generating section  104 . 
     That is, the calculation is performed and the contours are extracted with the direction θ in equation (5) is set as θq. 
     In step S 26 , the contour feature quantity calculation section  85  determines whether or not the direction θq is θ 4 , that is, whether or not the counter q is four. In a case where it is determined in step S 26  that the direction θq is not θ 4 , in step S 27 , the contour feature quantity calculation section  85  increments the counter q. 
     For example, in a case where the counter q is one, the counter q is incremented by only one so that q=2, and due to this, the direction θq is θ 2 . When the counter q is incremented by only one, the process returns to step S 23  and the processes above are repeated. 
     On the other hand, in a case where it is determined in step S 26  that the direction θq is θ 4 , in step S 28 , the feature quantity generating section  104  combines the calculation results supplied from the first filter processing section  101 , the second filter processing section  102 , and the third filter processing section  103  as the contour feature point, and generates the contour feature quantity with regard to one contour feature point. 
     The contour feature quantity is determined by equation (7) or equation (8) below. 
     
       
         
           
             
               
                 
                   
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     In equation (7) and equation (8), similar to equation (2) and the like, G d,θ  is a function where a Gaussian function G with an arbitrary angle θ has been differentiated to the dth order. In addition, out of I(x i , y i , s i ), (x i , y i ) represent coordinates in the image of the contour point which is the process target and (s i ) represents the scale of the image which is the process target out of the images which configure the image pyramid. 
     Equation (7) is an equation where convolution calculation of the function, where the Gaussian function G with an arbitrary angle θ has been differentiated to the dth order, and the contour feature quantity is performed and calculation of the total of the absolute values is performed using Σ. Equation (8) is an equation where convolution calculation of the function where the Gaussian function G with an arbitrary angle θ has been differentiated to the dth order and the contour feature quantity is performed and a maximum value of the absolute values is obtained using max. 
     Both equation (7) and equation (8) are equations where the feature quantity is calculated, but equation (7) is an equation which calculates local energy and the equation (8) is an equation which calculates a local maximum value. Here, there will be additional description of the meanings of the equations. 
     Using the processes described above, it is possible to generate a detection and classification device which detects target object such as people with a filter coefficient extracted from a function with an arbitrary angle and a scale as the feature quantity. 
     However, for example, in the detection and classification device, the feature points may depend on the relationship between the clothes that the person is wearing and the background. 
     In addition, in relation to a recognition target where the changing of shape is considerable such as with people, the selectivity is excessive with regard to the feature quantities. As such, it is necessary to absorb this and perform the processes, and it is necessary to make each of the feature quantities into feature quantities which have invariance. 
     It is possible to solve the making of “the feature points which depend on the relationship between the clothes that the person is wearing and the background” into feature points which have invariance by calculating absolute values of the output values after the filter processes. By calculating the absolute values, it is possible to extract the feature points which are close to the contours of people. 
     Furthermore, in the embodiment, the first order differential function, the second order differential function, and the third order differential function are calculated, and calculation of the absolute value of each is performed. As such, compared to a case where calculation is performed using only the absolute value of the first order differential function, it is possible to significantly improve accuracy and it is possible to calculate feature quantities which have invariance. 
     In addition, with regard to “the selectivity being excessive with regard to the feature points in relation to a recognition target where the changing of shape is significant such as with people”, by performing an invariant calculation using positional deviation, it is possible to calculate feature quantities which absorb this. 
     The invariant calculation using positional deviation is a calculation, for example, which uses that the lengths of the contours are substantially the same irrespective of the shape of a face when the contours of a face have been detected. 
     In other words, when focusing on a predetermined portion of the contour, it is a calculation that even if the position of the portion deviates such as when the position of the contours of a person with a substantially round face moves and overlaps with the contours of a person with a narrow and long face, a value such as the length does not change due to only deviation of the position. 
     As such a calculation, the total is calculated such as in equation (7). By calculating the total, for example, the total of the contours of the face of the person is calculated. 
     In addition, the maximum value is calculated in equation (8). By calculating the maximum value, for example, the maximum value out of the contours of the face of the person is calculated. 
     Here, the two calculations of the total and the maximum value are shown. In other words, as described above, the calculation where the local energy is calculated is shown based on equation (7) and the calculation where the local maximum value is calculated is shown based on equation (8). 
     Other than these calculations, a calculation may be performed where the local energy in the vicinity of a point which has a local maximum value is calculated. This is an image where the calculation result of equation (8) is received and the calculation of equation (7) is performed. 
     In addition, a calculation may be performed where the maximum value in the vicinity of the local energy is calculated. This is an image where the calculation result of equation (7) is received and the calculation of equation (8) is performed. Although a detailed equation is not shown, the feature quantity may be calculated using a calculation such as this. 
     Using such a calculation, the feature quantity is calculated from each of the contour feature points. Then, in step S 29 , the contour feature quantity calculation section  85  determines whether or not the process has been completed with regard to all of the contour feature points. 
     For example, in a case where the contour feature quantities have been determined in regard to all of the feature points supplied from the contour feature point extraction section  84 , it is determined that the process has been completed. 
     In a case where it is determined in step S 29  that the process has not been completed in regard to all of the contour feature points, the process returns to step S 21  and the next contour feature point is selected as the focus pixel. 
     On the other hand, in a case where it is determined in step S 29  that the process has been completed in regard to all of the contour feature points, the process proceeds to step S 9  in  FIG. 9 . 
     In step S 9 , the classification calculation section  86  reads out the feature quantity for classification and the composite classifier stored in advance in the storage section  48  and calculates the read-out composite classifier as a substitute for the feature quantity. 
     That is, as a substitute for the read-out composite classifier, the classification calculation section  86  performs calculation of a correspondence to the feature quantity for classification out of the target object feature quantity from the target object feature quantity calculation section  83  or the contour feature quantity from the contour feature quantity calculation section  85 . 
     Here, the feature quantity which is the feature quantity for classification is a feature quantity used in the setting of weak classifiers which configure the composite classifier during a statistical learning process. That is, it is the target object feature quantity of a pair of target object feature points which correspond to a predetermined number of weak classifiers with a comparatively low error rate and contour feature quantities of the contour feature points. 
     In addition, the composite classifier is generated by integrating the target object classifier and the contour classifier. The target object classifier is a classifier which identifies whether the target object is included in the image using the target object feature quantity. The contour classifier is a classifier which identifies whether the target object is included in the image using the contour feature quantity. 
     In a case where the target object feature quantity of a pair of corresponding target object feature points is substituted, the predetermined number of weak classifiers output “+1” when the result is positive and output “−1” when the result is negative. Also, the target object classifier outputs whether or not the target object which is to be recognized is present depending on what is determined by the majority of the predetermined number of weak classifiers. 
     In the same manner, the contour classifier also substitutes the contour feature quantities of the contour feature points with the weak classifiers and outputs whether or not the target object which is to be recognized is present depending on what is determined by the majority of the predetermined number of weak classifiers. 
     Specifically, the composite classifier is generated as follows. 
     First, equation (9) is calculated and a classifier total U(x) of a target object classifier R(x) and a contour classifier T(x) is determined. That is, the classifier total U(x) is determined using a linear combination of the target object classifier R(x) and the contour classifier T(x).
 
 U ( x )=α· R ( x )+β· T ( x )  (9)
 
     Here, in equation (9), α and β are predetermined constants, that is, tuning parameters, and are determined by, for example, a classification rate or the like with regard to a learning image used in a statistical learning process. 
     The composite classifier shown using equation (10) is generated using the determined classifier total U(x).
 
COMPOSITE CLASSIFIER=sign( U ( x ))  (10)
 
     As a result of the calculation when the calculation of equation (10) is performed, “+1” is obtained which shows that the person which is the target object is present in the input image or “−1” is obtained which shows that the person which is the target object is not present in the input image. The classification calculation section  86  supplies the calculation result of the composite classifier to the recognition result output section  87 . 
     In step S 10 , the recognition result output section  87  outputs the recognition result in regard to the person based on the calculation result from the classification calculation section  86  and the process is completed. That is, the recognition result of whether or not the target object is recognized in the image is output. 
     Here, it is described above that a person is detected as the target object, but the target object is not limited to a person and may be any object. 
     For example, as the classification result of whether or not the target object is recognized in the input image, the region where a person, a hand, or a face which is the target object is detect may be set in a frame and the displayed image may be displayed as shown in  FIG. 13 . 
     The image shown in  FIG. 13  is an image where a person  151 , a hand  152 , and a face  153  are displayed as the target objects, and in the image, a person recognition frame  161  which encompasses the person  151 , a hand recognition frame  162  which encompasses the hand  152 , and a face recognition frame  163  which encompasses the face  153  are set and displayed. 
     In a case where a display such as this is performed, the image is input also into the recognition result output section  87  and the classification calculation section  86  supplies information, which indicates a region where the target object of the input image, to the recognition result output section  87  along with the calculation result. 
     In a case where the target object is detected from the image based on the calculation result and the information which indicates the region from the classification calculation section  86 , the recognition result output section  87  sets a frame (for example, the person recognition frame  161 ) which encompasses the region where the target object (for example, the person  151 ) is detected and displays the frame in the display  47 A along with the image. 
     In this manner, the second recognition section  63  extracts the target object feature points from the image and determines the target object feature quantities of the pairs of the target object feature points as well as extracting the contour feature points from the image and determining the contour feature quantities. 
     Then, the second recognition section  63  detects a person as the target object from the image using the determined target object feature quantity and contour feature quantity and the composite classifier which is stored in advance in the storage section  48 . 
     As above, the second recognition section  63  recognizes a person as the target object. The process where the first recognition section  62  recognizes a hand as the target object is also similar to the process by the second recognition section  63  and only the target object is different. Since there will be repetition, the description thereof is omitted. 
     Target Object Recognition Process  1   
     Next, an example of a process, where a target object is recognized using the two recognition units of the first recognition section  62  and the second recognition section  63 , will be described with reference to  FIGS. 14 to 19 . Here, the number of recognition devices is not limited to two and a plurality of three or more recognition devices may be used. 
       FIG. 14  is a flow chart describing a target object recognition process. In the example in  FIG. 14 , the first recognition section  62  recognizes a hand as the target object and the second recognition section  63  recognizes a person as the target object. 
     In step S 41 , the acquisition section  61  acquires an image from the camera  46 A which configures the input section  46 . For example, an image is acquired which is shown in  FIG. 15A  where a person  151 - 1  is raising a hand  152 - 1 . 
     In step S 42 , the first recognition section  62  performs the first recognition process with regard to the acquired image. That is, a process is executed where the hand from the image in  FIG. 15A  is recognized. The details of the process are as described above. 
     In the image in  FIG. 15A , the person  151 - 1  is standing in a location which is separated from the camera  46 A. In such a case, it is difficult to recognize the hand  152 - 1  from the image in  FIG. 15A  in the first recognition process in step S 42  since the hand  152 - 1  is small. 
     In step S 43 , the determination section  64  determines whether the first recognition process was successful. That is, it is determined whether it was possible to recognize the hand  152 - 1  from the image in  FIG. 15A . 
     In a case where it is determined in step S 43  that the first recognition process has failed, that is, in a case where it was not possible to recognize the hand, the process proceeds to step S 44 . 
     In step S 44 , the second recognition section  63  performs the second recognition process with regard to the acquired image. That is, a process is executed where the person is recognized with regard to the image in  FIG. 15A . The details of the process are as described above. 
     In the image in  FIG. 15A , the person  151 - 1  is standing in a location which is separated from the camera. However, the size of the person  151 - 1  is larger than the hand  152 - 1  and is a size which is sufficient to be recognized. An image is shown in  FIG. 15B  when the person  151 - 1  is recognized in the second recognition process in step S 44 . 
       FIG. 15B  is a diagram which shows an example of an image when the person  151 - 1  is recognized. The person  151 - 1  is recognized using the second recognition section  63 , a person recognition frame  161 - 1  is set and displayed. 
     In step S 45 , the determination section  64  determines whether the second recognition process was successful. That is, it is determined whether it was possible to recognize the person  151 - 1  from the image in  FIG. 15A . 
     In a case where it is determined in step S 45  that the second recognition process has failed, the process returns to step S 41 . That is, in a case where both the first recognition process and the second recognition process have failed, the processes from step S 41  to step S 45  are repeated. 
     In a case where it is determined in step S 45  that the second recognition process was successful, that is, in a case where it was possible to recognize the person  151 - 1  as in the example of  FIG. 15B , that is, in a case where the hand recognition process failed but the person recognition process was successful, the process proceeds to step S 46 . 
     In step S 46 , the acquisition section  61  acquires an image quality parameter from the storage section  48 . It is possible to classify the image quality parameter into a camera control parameter or an image processing parameter. 
     A camera control parameter is, for example, a parameter which sets a setting value of the main body of a camera such as zoom ratio, a focus position, an aperture value, a shutter time, a gain value, a color adjustment value, camera direction, or the like. An image processing parameter is, for example, a parameter for processing an acquired image such as brightness, contrast, color conversion, a sharpness value, focus, digital zoom ratio, or the like. 
     At least one type out of the camera control parameters and the image processing parameters is stored in the storage section  48  in advance as the image quality parameter. The setting values of the image quality parameter which are stored in the storage section  48  are separately prepared. 
     For example, in a case where the zoom ratio is set as the image quality parameter and P (where P is a natural number) setting values are prepared, the zoom ratio is stored as zoom ratio a={a 1 , a 2 , . . . , aP}. The acquisition section  61  acquires a new setting value from among {a 1 , a 2 , . . . , aP} as the image quality parameter. 
     Here, for example, in a case where brightness b={b 1 , b 2 , . . . , bQ (where Q is a natural number)} is set as the image quality parameter in addition to the zoom ratio a, it is possible to provide a combination of the setting values of the zoom ratio a and the brightness b as the image quality parameter. 
     In addition, a case where there are three or more types of the image quality parameters is the same. Then, the method is valid where all of the combinations are tried until the image is recognized. 
     In step S 47 , the control section  65  controls the current image quality parameter so as to be changed to the new image quality parameter acquired in step S 46 . After the process of step S 47 , the process returns to step S 41 . 
     Here, instead of the image quality parameter, an instruction such as “please move closer to the camera” may be acquired and displayed in the display  47 A with regard to the user. 
     Until it is determined in step S 43  that the first recognition process was successful, the process from step S 41  to S 47  is repeated. 
     In the process of step S 41  which follows the process of step S 47 , the acquisition section  61  acquires an image where the image quality parameter has been changed in step S 47 . An image, where the image quality parameter has been changed and which is appropriate for recognizing the hand, will be described with reference to  FIG. 15C . 
       FIG. 15C  is a diagram which shows an image where the zoom ratio which is the image quality parameter has been changed. In  FIG. 15C , an example is shown of a case where the image quality parameter is changed so that the zoom ratio is set so the camera looks further into the distance. That is, the person  151 - 2  is displayed to be larger in the image. 
     In step S 42 , the first recognition section  62  executes the first recognition process. In the image of  FIG. 15C  where the image quality parameter has been changed, the size of the hand  152 - 2  is a size which is larger than the case shown in  FIG. 15A  and which is sufficient to be recognized. Accordingly, in the process of step S 42 , the hand  152 - 2  is recognized using the first recognition section  62  and a hand recognition frame  162 - 2  is set and displayed. 
     In a case where it is determined in step S 43  that the first recognition process was successful, that is, in a case where it was possible to recognize the hand, the process proceeds to step S 48 . 
     In step S 48 , the determination section  64  determines whether the second recognition process in step S 44  was successful. That is, it is determined whether the process of recognizing the hand was successful. 
     In a case where it is determined in step S 48  that the second recognition process was successful, the process proceeds to step S 49 , and in step S 49 , the determination section  64  determines whether a condition has been satisfied. 
     The condition is two conditions of a relative positional relationship and a relative size relationship. First, the relative positional relationship condition will be described with reference to  FIGS. 16A to 17B . 
       FIG. 16A  is a diagram which shows an example of a relative positional relationship of a person recognition frame  161  and a hand recognition frame  162 . A detection region  181  is set in advance in a predetermined position of the person recognition frame  161 . Then, as the condition, it is determined whether the hand recognition frame  162  is in the detection region  181  using the determination section  64 . 
     Specifically, for example, the coordinates (x 1 , y 1 ) of the farthest to the upper left of the detection region  181  and the coordinates (x 2 , y 2 ) of the farthest to the lower right of the detection region  181  are set in advance so as to be in a predetermined relationship with regard to the coordinates (xb, yb) of the farthest to the upper left of the person recognition frame  161 . 
     In this case, the determination section  64  determines whether the coordinates (xh, yh) of the farthest to the upper left of the hand recognition frame  162  satisfy the following equation. 
     
       
         
           
             
               
                 
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       FIG. 16B  is a diagram which shows another example of a relative positional relationship of the person recognition frame  161  and the hand recognition frame  162 . A detection region  182  is set based on the distribution of the relative position of the hand recognition frame  162  in regard to the person recognition frame  161 . 
     Specifically, when considering a two-dimensional normal distribution which is defined by averages μ x,y  and a covariance Σ xy  of the coordinates (xh, yh) of the position of the hand, a predetermined range is specified where the value of the covariance Σ xy  is equal to or greater than a threshold w 0 . 
     That is, when the coordinates (xh, yh) of the farthest to the upper left of the hand recognition frame  162  are acquired, the determination section  64  determines whether the coordinates (xh, yh) satisfy the following equation. That is, it is determined whether the coordinates are positioned in a range of a predetermined distance from the averages μ x,y  of the xy plane.
 
TWO-DIMENSIONAL NORMAL DISTRIBUTION  w=N ( x   h |μ x,y ,Σ xy )
 
 w&gt;w 0  (12)
 
     Here, in the example of  FIG. 16 , the coordinates (xh, yh) of the farthest to the upper left of the hand recognition frame  162  are set as the base coordinates but the base coordinates may be the farthest to another side, an edge, or the center of the hand recognition frame  162 . 
     In addition, the two-dimensional normal distribution is used in the example of  FIG. 16B  but another probability distribution, such as a logistic distribution, a hyperbolic secant distribution, a Rayleigh distribution, a Levy distribution, or a Cauchy distribution, may be defined and used in the same manner. 
       FIG. 17A  is a diagram which shows an example of the relative positional relationship condition being satisfied. The coordinates (xh, yh) of the farthest to the upper left of the hand recognition frame  162 - 3  with regard to the coordinates (xb, yb) of the farthest to the upper left of the person recognition frame  161 - 3  are within the detection region  181 - 3 . Accordingly, the determination section  64  determines that the relative positional relationship condition is satisfied. 
       FIG. 17B  is a diagram which shows an example of the relative positional relationship condition not being satisfied. As shown in  FIG. 17B , the coordinates (xh, yh) of the farthest to the upper left of the hand recognition frame  162 - 4  are in a position to the lower left of the person recognition frame  161 - 4  which is outside of the range of the detection region  181 - 4 . 
     In such a case, there is a high possibility that the recognition result is an erroneous recognition since, for example, the hand is positioned in the vicinity of a knee of the user. Accordingly, the determination section  64  determines that the relative positional relationship condition is not satisfied. 
     Next, the relative size relationship condition will be described with reference to  FIGS. 18A to 19B .  FIG. 18A  is a diagram which shows the size of the person recognition frame  161 . In the example of  FIG. 18A , the length of the width of the person recognition frame  161  is set as a size H of the person recognition frame  161 . 
       FIG. 18B  is a diagram which shows the size of the hand recognition frame  162 . In the example of  FIG. 18B , the length of the width of the hand recognition frame  162  is set as a size h of the hand recognition frame  162 . 
     Then, it is determined whether a ratio of the size H of the person recognition frame  161  and the size h of the hand recognition frame  162  is within a predetermined range which is set in advance. 
     Specifically, the determination section  64  determines whether the following equation is satisfied. 
     
       
         
           
             
               
                 
                   
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     In addition, as another example, when considering a normal distribution which is defined by an average value μs and a variance σ 2  in relation to a ratio s of the size h of the hand recognition frame  162  in regard to the size H of the person recognition frame  161 , a predetermined range is specified where the value of the variance σ 2  is equal to or more than a threshold s 0 . 
     That is, when the size h of the hand recognition frame  162  size and the H of the person recognition frame  161  are acquired, the determination section  64  determines whether the following equation is satisfied. That is, it is determined whether the variance σ 2  of the ratio s with regard to the size h of the hand recognition frame  162  and the size H of the person recognition frame  161  which are detected is equal to or more than the threshold s 0 , that is, whether the ratio s is within a predetermined range from the average μs. 
     
       
         
           
             
               
                 
                   
                     
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       FIG. 19A  is a diagram which shows an example of the relative size relationship condition being satisfied. 
     In a case where the ratio of the size h of the hand recognition frame  162 - 11  with regard to the size H of the person recognition frame  161 - 11  is within a range which is set in advance, that is, in a case where the size h of the hand recognition frame  162 - 11  is an appropriate size with regard to the size H of the person recognition frame  161 - 11 , the determination section  64  determines whether the relative size relationship condition is satisfied. 
       FIG. 19B  is a diagram which shows an example of the relative size relationship condition not being satisfied. As shown in  FIG. 19B , the size h′ of the hand recognition frame  162 - 12  is too small compared to the size H′ of the person recognition frame  161 - 12 . 
     In such a case, since there is a high possibility that the recognition result of the hand or the person is an erroneous recognition, the determination section  64  determines that the relative size relationship condition is not satisfied. According to this, it is possible to more accurately recognize the target object. 
     Here, the two conditions of the relative positional relationship and the relative size relationship are described as the condition. However, the determination may be performed with the two conditions combined. 
     That is, equation (12) and equation (13) may be combined and the determination section  64  determines whether the following equation is satisfied.
 
z=ws
 
z&gt;z0  (15)
 
     Returning to  FIG. 14 , in a case where it is determined in step S 49  that the condition is not satisfied, the process proceeds to step S 46 . That is, in a case where it is determined that the condition is not satisfied even if both of the hand and the person are recognized, the image quality parameter is acquired and the recognition process is performed from a new image. 
     In a case where it is determined in step S 49  that the condition is satisfied, that is, in a case where both the hand and the person are recognized and the condition is also satisfied, the process proceeds to step S 50 . 
     In addition, also in a case where it is determined in step S 48  that the second recognition process was not successful, that is, in a case where the hand was recognized but it was not possible to recognize the person from the image which was initially acquired in step S 41  and where the image quality parameter has not been changed, the process proceeds to step S 50 . 
     In step S 50 , the output section  66  outputs the recognition success. For example, the output section  66  outputs the recognition success to the application execution section  67 . 
     The application execution section  67  executes a predetermined application when there is notification of the recognition success from the output section  66 . For example, a command is executed based on gesture recognition using the application execution section  67 . 
     After the process of step S 50 , the target object recognition process of  FIG. 14  ends. 
     In this manner, by using the two recognition devices for recognition of each of the hand and the person, it is possible to more reliably recognize a hand even in an image where recognition is difficult with a single recognition device which recognizes a hand. 
     Here, in the example in  FIG. 14 , the first recognition section  62  recognizes a hand as the recognition target object and the second recognition section  63  recognizes a person as the recognition target object. However, the recognition target objects of the first recognition section  62  and the second recognition section  63  are not limited to these recognition target objects. 
     Target Object Recognition Process  2   
     As another example of the target object recognition process according to the embodiment, an example of a case where the first recognition section  62  recognizes a face as the recognition target object and the second recognition section  63  recognizes a person as the recognition target object will be described with reference to  FIGS. 14 and 20A  to  20 C. Here, description is simplified as the same process as in the example described with reference to  FIGS. 14 and 15A  to  15 C is repeated. 
     In step S 41  of  FIG. 14 , the acquisition section  61  acquires the image in  FIG. 20A . The image in  FIG. 20A  is a diagram which shows an example of an image which is out of focus. 
     In step S 42 , the first recognition section  62  performs the first recognition process with regard to the acquired image in  FIG. 20A , that is, a process where a face is recognized. However, it is difficult for the face  153 - 15  to the recognized since the image in  FIG. 20A  is out of focus. 
     In step S 43 , the determination section  64  determines whether the first recognition process, that is, recognition of the face  153 - 15  was successful. It is determined in step S 43  that the first recognition process has failed since it was not possible to recognize the face  153 - 15  in the image in  FIG. 20A  and the process proceeds to step S 44 . 
     In step S 44 , the second recognition section  63  performs the second recognition process with regard to the acquired image in  FIG. 20A , that is, a process where a person is recognized. The image in  FIG. 20A  is out of focus but is an image which is sufficient for recognition of the person  151 - 15 . 
     When the second recognition process is executed in step S 44  and the person  151 - 15  is recognized, the person recognition frame  161 - 15  is set and displayed as shown in  FIG. 20B . 
     In step S 45 , the determination section  64  determines whether the second recognition process was successful. In a case where the image in  FIG. 20A  was recognized, it is determined in step S 45  that the second recognition process was successful and the process proceeds to step S 46 . 
     In step S 46 , the acquisition section  61  acquires a new image quality parameter. In the case of the example of  FIGS. 20A to 20C , the focus position and the sharpness value are stored as the image quality parameter and a new setting value is acquired for the combination of the setting values of the focus position and the sharpness value. 
     In step S 47 , the control section  65  controls the current image quality parameter so as to be changed to the new image quality parameter acquired in step S 46 . After the process of step S 47 , the process returns to step S 41 . 
     Here, until it is determined in step S 43  that the first recognition process was successful, the process from step S 41  to S 47  is repeated. 
     When an image which is appropriate for recognizing a face is acquired in step S 41 , the image in  FIG. 20C  is output by executing the first recognition process in step S 42 . When the face  153 - 16  is recognized in the process of step S 42 , the face recognition frame  163 - 16  is set and displayed. 
     Then, it is determined in step S 43  that the first recognition process was successful and the process proceeds to step S 48 . The processes from S 48  onward are the same as the example described with reference to  FIGS. 14 to 19B  and are omitted. 
     Here, in the case of the example of  FIGS. 20A to 20C , when the recognition success is output in the process of step S 50 , the application execution section  67  executes an application which adjusts the focus and color so that it is possible display the face so that it is sharper and clearer. Of course, the focus may be changed as the image quality parameter. 
     Configuration of Posture Recognition Section 
     It is possible to use the posture of a person in the determination of whether the condition is satisfied.  FIG. 21  is a block diagram which shows a detailed configuration example of a posture recognition section  201  which is further include in the CPU  41  in such a case. 
     The posture recognition section  201  is configured from a delayed image acquisition section  221 , a calculation section  222 , a signal strength detection section  223 , and a detection result output section  224 . 
     The delayed image acquisition section  221  acquires an image which was stored a predetermined time beforehand in the RAM  43  or an image from a predetermined time beforehand which is supplied via a delay circuit which is not shown and the image is supplied to the calculation section  222 . 
     The calculation section  222  calculates the difference between the original image and the delayed image and supplies the calculation result to the signal strength detection section  223 . The signal strength detection section  223  detects a region where the signal strength is higher than a predetermined value and supplies the detection result to the detection result output section  224 . 
     The detection result output section  224  overlaps and outputs the detection result on an image. 
     Target Object Recognition Process  3   
     Another example of the target object recognition process according to the embodiment will be shown with reference to  FIGS. 22 to 26B . The processes of steps S 61  to S 68 , S 70  and S 71  in  FIG. 22  are processes which correspond to the processes of steps S 41  to S 50  in  FIG. 14  and the description is simplified as the processes are repeated. 
     In the example of  FIG. 22 , an example is shown of a case where the first recognition section  62  recognizes a hand as the recognition target object and the second recognition section  63  recognizes a face as the recognition target object. 
     In step S 61 , the acquisition section  61  acquires the image in  FIG. 23A . The image in  FIG. 23A  is an example of an image where the hand  152 - 21  does not fit within the angle of view of the camera since the person  151 - 21  is close to the camera  46 A or the zoom ratio parameter of the camera  46 A is set to a wide angle of view. 
     In step S 62 , the first recognition section  62  performs the first recognition process with regard to the acquired image in  FIG. 23A , that is, a process where the hand  152 - 21  is recognized. However, it is not possible for the hand  152 - 21  to be recognized in the image in  FIG. 23A  since only a portion of the hand  152 - 21  is displayed. 
     In step S 63 , the determination section  64  determines whether the first recognition process, that is, recognition of the hand  152 - 21  was successful. It is determined in step S 63  that the first recognition process has failed since it was not possible to recognize the hand  152 - 21  in the image in  FIG. 23A  and the process proceeds to step S 64 . 
     In step S 64 , the second recognition section  63  performs the second recognition process with regard to the acquired image in  FIG. 23A , that is, a process where the face  153 - 21  is recognized. The image in  FIG. 23A  displays the face  153 - 21  with a sharp focus. 
     When the second recognition process is executed in step S 64  and the face  153 - 21  is recognized, the face recognition frame  163 - 21  is set and displayed as shown in  FIG. 23B . 
     In step S 65 , the determination section  64  determines whether the second recognition process was successful. In this case, it is determined in step S 65  that the second recognition process was successful and the process proceeds to step S 66 . 
     In step S 66 , the acquisition section  61  acquires a new image quality parameter. In the case of the example of  FIGS. 23A to 23C , the zoom ratio is stored as the image quality parameter and a new setting value is acquired from the setting value of the stored zoom ratio. 
     In step S 67 , the control section  65  controls the current image quality parameter so as to be changed to the new image quality parameter acquired in step S 66 . After the process of step S 67 , the process returns to step S 61 . 
     In the same manner as above, until it is determined in step S 63  that the first recognition process was successful, the process from step S 61  to S 67  is repeated. 
     When an image which is appropriate for recognizing a hand is acquired in step S 61 , the hand recognition frame  162 - 22  is set and displayed in the vicinity of the hand  152 - 22  as shown in the image in  FIG. 23C  due to the execution of the first recognition process in step S 62 . 
     Then, it is determined in step S 63  that the first recognition process was successful and the process proceeds to step S 68 . 
     In step S 68 , the determination section  64  determines whether the second recognition process was successful. In the case of the example in  FIGS. 23A to 23C , it is determined in step S 68  that the second recognition process was successful, that is, it is determined that the hand and face are recognized and the process proceeds to step S 69 . 
     In step S 69 , the posture recognition section  201  executes the posture recognition process. The posture recognition process will be described with reference to FIG.  24 . 
     Posture Recognition Process 
       FIG. 24  is a flow chart describing an example of the posture recognition process. 
     In step S 81 , the delayed image acquisition section  221  acquires a delayed image. The delayed image which is an image of an original image from a predetermined time beforehand is temporarily stored in the RAM  43 . 
     In step S 82 , the calculation section  222  calculates the difference between the original image and the delayed image. In step S 83 , the signal strength detection section  223  detects a region where the signal strength is high from the image where the difference has been calculated. That is, the region, where the absolute value of the value of the difference is large, is detected. 
     For example, when a user moves their hand, the region of the position of the hand and the arm before the movement of the user and the position of the hand and the arm after the movement is detected. 
     In step S 84 , the detection result output section  224  overlaps and outputs the region, which is detected by the signal strength detection section  223 , on the original image. An example where the detected region is overlapped and output on the original image will be described with reference to  FIG. 23C . 
     In the image of  FIG. 23C , a posture  164 - 1  and a posture  164 - 2  are set and output. The posture  164 - 1  shows the user before movement, that is, a position of the hand and the arm of the user in the delayed image. The posture  164 - 2  shows the user after movement, that is, a position of the hand and the arm of the user in the original image. 
     After the process of step S 84  in  FIG. 24 , the process proceeds to step S 70  in  FIG. 22 . In step S 70 , the determination section  64  determines whether the condition is satisfied. Conditions of the relative distance relationship and the relative angle relationship of the hand recognition frame  162  and the posture  164  will be described with reference to  FIGS. 25 ,  26 A and  26 B. 
       FIG. 25  is a diagram which shows an example of the conditions of the relative distance relationship and the relative angle relationship conditions of the hand and the arm. 
     First, the relative distance relationship condition will be described. The relative distance is calculated as, for example, a calculation of a distance d between the coordinates at a central point of an edge out of the four edges of the hand recognition frame  162  which is the edge closest to an end of the posture  164  and the coordinates of an end of the posture  164  which is closest to the hand recognition frame  162 . The distance d is calculated using equation (16).
 
 d =√{square root over ( dx   2   +dy   2 )}  (16)
 
     dx is calculated using the difference of the x coordinate of a central point Pm of a predetermined edge of the hand recognition frame  162  and the x coordinate of an end Pf (the end of the arm in this case) of the posture  164 . In the same manner, dy is calculated using the difference of the y coordinate of the central point Pm of a predetermined edge of the hand recognition frame  162  and the y coordinate of the end Pf of the posture  164 . 
     Then, the determination section  64  determines whether the following equation is satisfied. That is, it is determined whether the distance d is within the values d 1  and d 2  set in advance.
 
 d 1 ≦d&lt;d 2 ( d   1   ,d   2 : ACTUAL NUMBER)  (17)
 
     In addition, as another example, when considering a normal distribution which is defined by being given an average value μd and a variance σ d   2  in relation to the distance d, a predetermined range is specified where the value of the variance σ d   2  is equal to or more than a threshold e 0 . 
     Then, the determination section  64  determines whether the following equation is satisfied. That is, it is determined whether the detected distance d is within a predetermined range from the average μd.
 
NORMAL DISTRIBUTION  e=N ( d|μ   d ,σ d   2 )
 
 e&gt;e 0  (18)
 
     Next, the relative angle relationship condition will be described. The relative angle is expressed by an angle dθ which is possible between a parallel line  191 , which is parallel to a predetermined edge of the hand recognition frame  162 , and the posture  164 . 
     As the edge corresponding to the parallel line  191 , the edge where the angle dθ is smaller is selected out of the edges of the hand recognition frame  162 . In the example of  FIG. 25 , the edge which is close in the vertical direction is selected. 
     When considering a normal distribution which is defined by being given an average value μθ and a variance σ θ   2  in relation to the angle dθ, a predetermined range is specified where the value of the variance σ θ   2  is equal to or more than a threshold f 0 . 
     Then, the determination section  64  determines whether the following equation is satisfied. That is, it is determined whether the detected angle dθ is within a predetermined range from the average μθ.
 
NORMAL DISTRIBUTION  f=N ( dθ|μθ,σ   θ   2 )
 
 f&gt;f 0  (9)
 
     Here, the two conditions of the relative distance relationship and the relative angle relationship are described as the condition. However, the determination may be performed with the two conditions combined. 
     For example, equation (18) and equation (19) may be combined and the determination section  64  determines whether the following equation is satisfied.
 
q=ef
 
q&gt;q0  (20)
 
       FIGS. 26A and 26B  are diagrams which show examples of a determination result of the relative distance. 
       FIG. 26A  is a diagram which shows an example where the relative distance relationship condition is satisfied. The hand recognition frame  162 - 31  and the posture  164 - 31  are in contact and the distance d is zero. Accordingly, since the distance between the hand recognition frame  162 - 31  and the posture  164 - 31  is within the range set in advance, the determination section  64  determines that the relative distance relationship condition is satisfied. 
       FIG. 26B  is a diagram which shows an example where the relative distance relationship condition is not satisfied. As shown in  FIG. 26B , the distance d between the coordinates at a central point of an edge out of the four edges of the hand recognition frame  162 - 32  which is the edge closest to an end of the posture  164 - 32  and the coordinates of an end of the posture  164 - 32  which is closest to the hand recognition frame  162 - 32  is d′. 
     Since there is a high possibility that the hand recognition result is an erroneous recognition when the distance d between the hand recognition frame  162 - 32  and the posture  164 - 32  is outside of the range set in advance, the determination section  64  determines that the relative distance relationship condition is not satisfied. 
     According to this, as shown in  FIG. 26B , the relative position of the hand recognition frame  162 - 32  with regard to the face recognition frame  163 - 32  is within the detection region  181 . Even if the relative positional relationship condition is satisfied, since the relative distance relationship condition of the hand recognition frame  162 - 32  and the posture  164 - 32  is not satisfied, the determination section  64  determines that the condition is not satisfied. 
     In this manner, with regard to the target object recognition process of  FIG. 14 , since it is determined whether it is possible to recognize the target object (for example, the hand) further using the recognition result of posture recognition (for example, the arm), it is possible to more reliably recognize the target object. 
     Returning to  FIG. 22 , in a case where it is determined in step S 70  that the condition is not satisfied, the process proceeds to step S 66 . 
     In a case where it is determined in step S 70  that the condition is satisfied, the process proceeds to step S 71 . In step S 71 , the output section  66  outputs the recognition success. After the process of step S 71 , the target object recognition process of  FIG. 22  ends. 
     Configuration of Television Image Reception Device Control System 
     Next, a television image reception device control system  301  according to an embodiment will be described with reference to  FIG. 27 .  FIG. 27  is a block diagram which shows a configuration example of the television image reception device control system  301  according to an embodiment of the disclosure. 
     For example, the television image reception device control system  301  executes a process, as the image processing device, which is the same as the target object recognition process  1  which was described above with reference to  FIGS. 14 to 15C . The television image reception device control system  301  is configured from a camera  321 , a control device  322 , an image display device  323 , and a RAM  324 . 
     The camera  321  acquires an image based on the image quality parameter which has been set. In addition, the camera  321  is arranged in a position where it is possible for a user viewing the image display device  323  to be imaged. 
     Other than images of digital terrestrial broadcasting, BS broadcasting, CS broadcasting, or the like, the image display device  323  displays images acquired using the camera  321  when necessary. 
     The RAM  324  stores data, programs and the like which are necessary for the execution of various processes in the control device  322 . 
     In the control device  322 , a camera control engine  341 , a person recognition engine  342 , a hand recognition engine  343 , a recognition result determination engine  344 , a gesture recognition engine  345 , a television image reception main engine (referred to below as TV main engine)  346 , and a control CPU  348  are connected to each other using an image signal bus  347  and a bus  349 . 
     The camera control engine  341  acquires an image from the camera  321 . In addition, the camera control engine  341  controls the camera so that the image is an image based on the setting value of the image quality parameter described above. 
     The person recognition engine  342  recognizes a person as the recognition target object from the image acquired from the camera control engine  341  via the image signal bus  347 . Specifically, the processing of the person recognition engine  342  is to perform the same process as the process of the second recognition section  63  in the target object recognition process  1 . 
     The hand recognition engine  343  recognizes a hand as the recognition target object from the image acquired from the camera control engine  341  via the image signal bus  347 . Specifically, the processing of the hand recognition engine  343  is to perform the same process as the process of the first recognition section  62  in the target object recognition process  1 . 
     The recognition result determination engine  344  determines whether it is possible for the target object to be recognized based on the recognition results of the person recognition engine  342  and the hand recognition engine  343  acquired via the image signal bus  347  or the bus  349  and outputs the determination result. Specifically, the recognition result determination engine  344  executes the same processes as the processes of steps S 43 , S 45 , S 48 , S 49  and S 50  of  FIG. 14 . 
     The gesture recognition engine  345  recognizes the types of gestures of a person or a hand from the recognition result of the person recognition engine  342  or the hand recognition engine  343  acquired via the image signal bus  347 . 
     The TV main engine  346  controls the output of the image to the image display device  323  based on the gesture recognition result of the gesture recognition engine  345  acquired via the bus  349  and instruction from the control CPU  348 . 
     The control CPU  348  controls the operations of the camera control engine  341 , the person recognition engine  342 , the hand recognition engine  343 , the recognition result determination engine  344 , the gesture recognition engine  345 , and the TV main engine  346  via the bus  349 . 
     According to this, the television image reception device control system  301  acquires an image of the movement gestures of the user&#39;s hand using the camera  321 , and performing of an operation such as the changing of a channel is possible without using a remote controller or the like since the types of gestures are recognized. The operation is the same as the case described above so the description is omitted. 
     Configuration of Digital Camera Control System 
     Next, a digital camera control system  401  according to an embodiment will be described with reference to  FIG. 28 .  FIG. 28  is a block diagram which shows a configuration example of the digital camera control system  401  according to an embodiment of the disclosure. 
     For example, the digital camera control system  401  executes a process, as the image processing device, which is the same as the target object recognition process  2  which was described above with reference to  FIGS. 14 and 20A  to  20 C. The digital camera control system  401  is configured from a camera  421 , a control device  422 , an image display device  423 , a RAM  424 , and a recording medium  425 . 
     The camera  421  acquires an image. The image display device  423  displays the image acquired using the camera  421  or an image where image processing has been performed. 
     The RAM  424  stores data, programs and the like which are necessary for the execution of various processes in the control device  422 . The recording medium  425  is controlled by the control device  422  and stores images which are captured by the camera  421  and other information. 
     In the control device  422 , a camera signal process engine  441 , a person recognition engine  442 , a face recognition engine  443 , a recognition result determination engine  444 , an image sharpness adjustment engine  445 , a JPEG (Joint Photographic Experts Group) engine  446 , an image display engine  447  and a control CPU  449  are connected to each other by an image signal bus  448  and a bus  452 . 
     In addition, the control CPU  449  and the external medium controller  451  are connected to each other via a data bus  450 . The data bus  450  is also connected to the image signal bus  448 . 
     The camera signal process engine  441  converts an analog data image acquired by the camera  421  and acquires a digital data image. 
     The person recognition engine  442  recognizes a person as the recognition target object from the image acquired from the camera signal process engine  441  via the image signal bus  448 . Specifically, the processing of the person recognition engine  442  is the same as the process of the second recognition section  63  in the target object recognition process  2  described above. 
     The face recognition engine  443  recognizes a face as the recognition target object from the image acquired from the camera signal process engine  441  via the image signal bus  448 . Specifically, the processing of the face recognition engine  443  is the same as the process of the first recognition section  62  in the target object recognition process  2  described above. 
     The recognition result determination engine  444  determines whether it is possible for the target object to be recognized based on the recognition results of the person recognition engine  442  and the face recognition engine  443  acquired via the image signal bus  448  or the bus  452  and outputs the determination result. Specifically, the same processes as the processes of steps S 43 , S 45 , S 48 , S 49  and S 50  of  FIG. 14  are executed. 
     The image sharpness adjustment engine  445  performs image processing where the sharpness of the image from the camera signal process engine  441  acquired via the image signal bus  448  is adjusted. 
     The JPEG engine  446  performs compression and uncompression processes based on the JPEG standard with regard to the image from the camera signal process engine  441  and the image sharpness adjustment engine  445  which was acquired via the image signal bus  448 . 
     The image display engine  447  outputs each type of image acquired via the image signal bus  448  to the image display device  423 , and the images are displayed. 
     The control CPU  449  controls the camera signal process engine  441 , the person recognition engine  442 , the face recognition engine  443 , the recognition result determination engine  444 , the image sharpness adjustment engine  445 , the JPEG engine  446 , and the image display engine  447  via the bus  452 . 
     The external medium controller  451  controls a recording medium  452 , stores image data captured using the camera  421 , reads out the stored image data, and display the image data on the image display device  423 . 
     According to this, the digital camera control system  401  recognizes the face or the like of a user from the image acquired using the camera  421 , improves the sharpness of the recognized face region, and records on the recording medium  425  so that it is possible for the user to capture an image which is more appropriate and perform recording and reproduction. The operation in this case is also the same as the case described above so the description is omitted. 
     Above, the recognition target is set as a person or a portion thereof. However, it is possible to set another object as the recognition target object. For example, in the information processing device which recognizes a car, it is possible to combine “body recognition”, “tire recognition”, “number plate recognition” and the like and execute the same recognition process. 
     Other 
     In the disclosure, in the program, the processes which are performed in a time series manner in the order described above may, of course, not necessarily be processed in a time series manner and also may include processes which are executed in parallel or independently. 
     The embodiments of the disclosure are not limited to the embodiment described above but various modifications are possible within the scope which does not depart from the concept of the disclosure. 
     The present disclosure contains subject matter related to that disclosed in Japanese Priority Patent Application JP 2010-260869 filed in the Japan Patent Office on Nov. 24, 2010, the entire contents of which are hereby incorporated by reference.