Patent Publication Number: US-11663714-B2

Title: Image processing apparatus, image processing method, and program

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit under 35 U.S.C. § 371 as a U.S. National Stage Entry of International Application No. PCT/JP2018/043092, filed in the Japanese Patent Office as a Receiving Office on Nov. 22, 2018, which claims priority to Japanese Patent Application Number JP2017-250534, filed in the Japanese Patent Office on Dec. 27, 2017, each of which is hereby incorporated by reference in its entirety. 
     FIELD 
     The present invention relates to an image processing apparatus, an image processing method, and a program. 
     BACKGROUND 
     For example, the following Patent Literature 1 describes the conventional technique in which it is assumed that the processing time is shortened when all of the characters are read from the image captured by a digital camera, or the like. Furthermore, the following Non Patent Literature 1 describes the segmentation of a microscopic image by using learning. 
     CITATION LIST 
     Patent Literature 
     
         
         Patent Literature 1: JP 2016-053763 A 
       
    
     Non Patent Literature 
     
         
         Non Patent Literature 1: ZhaozhengYin, Ryoma Bise, Mei Chen, Takeo Kanade, “CELL SEGMENTATION IN MICROSCOPYIMAGERY USING A BAG OF LOCAL BAYESIAN CLASSIFIERS” Rotterdam, Netherlands Apr. 14-17, 2010IEEE Press Piscataway, N.J., USA c2010 &lt;URL: https://dl.acm.org/citation.cfm?id=1855995&gt; 
       
    
     SUMMARY 
     Technical Problem 
     With the technique disclosed in the above Patent Literature 1, the process is performed to reduce the search range so as to shorten the processing time. Specifically, according to the technique disclosed in Patent Literature 1, the input image is not fully searched, and the region of interest is set so that the search is executed at two steps. This method has a problem in that the processing is complicated due to the step-by-step search. 
     Furthermore, in the field of medicine and life science, the observation on a change in the motion or the condition of many kinds of cells and body tissues has been executed recently. In order to achieve an objective evaluation of them, the technique for discriminating an event with regard to the living tissue has been under development. Particularly, when the living tissue moves sharply, it is necessary to read the image at high speed in real time. With the technique described in the above Patent Literature, the step-by-step search makes it difficult to read the image of the sharply moving living tissue, or the like, in real time. 
     Therefore, there is a demand for a reduction in the processing time without performing complicated processing during image reading. 
     Solution to Problem 
     According to the present disclosure, an image processing apparatus is provided that includes: a dividing unit that divides a detection region for detecting a feature value of an image into a plurality of regions; a decimation processing unit that performs a decimation process on a pixel value for each of the regions; and a histogram calculating unit that interpolates a pixel value having undergone the decimation process to calculate a histogram of pixel values of the regions. 
     Moreover, according to the present disclosure, an image processing method is provided that includes: dividing a detection region for detecting a feature value of an image into a plurality of regions; performing a decimation process on a pixel value for each of the regions; and interpolating a pixel value having undergone the decimation process to calculate a histogram of pixel values of the regions. 
     Moreover, according to the present disclosure, a program is provided that causes a computer to function as: a means that divides a detection region for detecting a feature value of an image into a plurality of regions; a means that performs a decimation process on a pixel value for each of the regions; and a means that interpolates a pixel value having undergone the decimation process to calculate a histogram of pixel values of the regions. 
     Advantageous Effects of Invention 
     As described above, according to the present disclosure, during image reading, it is possible to shorten the processing time without performing complicated processing. 
     Furthermore, there is no particular limitation on the above-described advantage and, in addition to the above advantage or instead of the above advantage, any advantage described in the description or other advantages, which may be recognized from the description, may be produced. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a diagram illustrating the outline of a configuration of a system according to an embodiment of the present disclosure. 
         FIG.  2    is a schematic view illustrating an example of the configuration of a learning unit. 
         FIG.  3    is a schematic view illustrating an example of the configuration of a detection unit. 
         FIG.  4    is a schematic view illustrating the manner of detecting an image feature from an image. 
         FIG.  5    is a schematic view illustrating a method for dividing a window. 
         FIG.  6    is a schematic view illustrating a pixel decimation pattern of a window of 4×4 pixels. 
         FIG.  7    is a flowchart illustrating a process by the system according to the present embodiment. 
         FIG.  8    is a schematic view illustrating an example of an application for real-time object detection. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     A preferred embodiment of the present disclosure is described below in detail with reference to the accompanying drawings. In the description and the drawings, the components having substantially the same functional configuration are denoted with the same reference numerals, and duplicated descriptions are omitted. 
     The descriptions are given in the following order. 
     1. Example of system configuration 
     2. Process performed by a detection unit 
     
         
         
           
             2. 1. Outline 
             2. 2. Specific processing content
 
3. Flow of process performed in the present embodiment
 
4. Example of adaptation to application
 
           
         
       
    
     1. Example of System Configuration 
       FIG.  1    is a diagram illustrating the outline of a configuration of a system  1  according to an embodiment of the present disclosure. As illustrated in  FIG.  1   , the system  1  includes an imaging device  10 , an information processing apparatus  20 , and a display device  30 . The imaging device  10 , the information processing apparatus  20 , and the display device  30  are connected to each other via various wired or wireless networks. 
     The imaging device  10  is a device that executes imaging to generate an image (still image or moving image). The imaging device  10  according to the present embodiment is implemented by using, for example, a digital camera. Alternatively, the imaging device  10  may be implemented by using any device having an imaging function, for example, a smartphone, a tablet, or a wearable device. 
     As illustrated in  FIG.  1   , the imaging device  10  according to the present embodiment is provided inside an incubator I 1  and above an observation target M 1  (cell or body tissue) inside a dish D 1 . Further, the imaging device  10  captures the observation target M 1  at a predetermined frame rate to generate an image. 
     Furthermore, the imaging device  10  may be provided inside the incubator I 1  or may be provided outside the incubator I 1 . Further, the imaging device  10  is also applicable to capturing of the observation target M 1  that is not housed in the incubator I 1 . Moreover, the imaging device  10  may be provided integrally with the incubator I 1 . 
     More specifically, as illustrated in  FIG.  1   , the imaging device  10  according to the present embodiment includes an imaging unit  101  and an imaging control unit  102 . 
     The imaging unit  101  includes various members such as an imaging element, e.g., a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor), a lens that controls the focus of an object image onto the imaging element, or a light source that irradiates the subject with light, with which the actual space is captured. 
     In order to properly determine the movement of the observation target M 1 , the imaging unit  101  according to the present embodiment captures a certain imaging region including the observation target M 1 . Furthermore, the imaging unit  101  may capture the observation target M 1  directly (without using a different member such as a lens) or may capture the observation target M 1  via a different member such as a microscope including an objective lens. In this case, to capture the movement of the observation target M 1  in the order of submicron meters, it is preferable that the magnification of the objective lens is approximately 40 times to 60 times. Although the frame rate is not particularly limited, it is preferably set in accordance with the degree of change in the observation target, and specifically, it is preferably set to the frame rate at which the movement of the observation target M 1  in the order of sub-second may be captured. 
     The signal generated by the imaging unit  101  during an imaging process is output to the imaging control unit  102 . 
     The imaging control unit  102  includes a processing circuitry implemented by using a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), or the like, and a communication device to control the overall operation of the imaging unit  101 . For example, the imaging control unit  102  controls an imaging process by the imaging unit  101  and generates a captured image based on the signal obtained during the imaging process. 
     For example, the imaging control unit  102  may control the timing of the imaging process performed by the imaging unit  101 . More specifically, the imaging control unit  102  may control the imaging unit  101  to continuously capture images during a predetermined period of time so as to generate a moving image. Furthermore, the imaging control unit  102  may control the imaging unit  101  so as to intermittently perform an imaging process (what is called time-lapse imaging) at a predetermined interval. Further, for example, to capture a plurality of embryos, the imaging control unit  102  may directly or indirectly control the incubator I 1  so as to move the imaging device  10  or the dish in accordance with the imaging timing for the observation target M 1 , which is the target to be captured. 
     Furthermore, the imaging control unit  102  may control the wavelength, the irradiation intensity, or the irradiation time of the light source included in the imaging unit  101 . For example, the imaging control unit  102  may control the light source of the imaging unit  101  so as to irradiate the observation target M 1  with the light having the appropriate wavelength and the minimum irradiation intensity during only the period in which the imaging unit  101  performs the imaging process. 
     The imaging control unit  102  outputs the generated image, or the like, to the information processing apparatus  20 . 
     The information processing apparatus  20  is an apparatus having an image analysis function using a learning function. The information processing apparatus  20  is implemented by using any device having an image analysis function, e.g., a PC (personal computer), a tablet, or a smartphone. The information processing apparatus  20  includes a processing circuitry such as a CPU (Central Processing Unit) and a communication device including the hardware that enables the wireless or wired communication. For example, in the information processing apparatus  20  according to the present embodiment, the communication device acquires images (e.g., a time-lapse image or a moving image) from the imaging device  10 . Then, the information about each still image or moving image acquired by the processing circuitry is acquired, and the event for the observation target M 1  is determined using each set of information. Each process performed by the processing circuitry of the information processing apparatus  20  is output to a storage device, a display device, or the like, provided inside or outside the information processing apparatus  20 . Furthermore, the information processing apparatus  20  may be implemented by using one or more information processing apparatuses on a network. The functional configuration for performing each of the functions of the information processing apparatus  20  is described later. 
     Furthermore, according to the present embodiment, the imaging device  10 , the information processing apparatus  20 , and the display device  30  constitute the system  1 ; however, this technology is not limited to this example. For example, the imaging device  10  may perform a process (e.g., a dynamic analysis process, a feature value extraction process, each estimation process, or a determination process) regarding the information processing apparatus  20 . In this case, the system  1  is implemented by using an imaging device having a dynamic analysis function, or the like. 
     The information processing apparatus  20  may include the functions of a learning unit  200  and a detection unit  300 .  FIG.  2    is a schematic view illustrating an example of the configuration of the learning unit  200 . The learning unit  200  receives the input of the same image as the observation target M 1 , executes clustering, and generates a discriminator. The learning unit  200  includes: a filter processing unit  202  that performs a filter process on an input image; a sampling unit  204  that samples an input image having undergone the filter process; a multiwindow histogram calculating unit  206  that calculates a multiwindow histogram; a similar matrix calculating unit  208 ; a clustering unit  210 ; and a learning coefficient deriving unit (discriminator generating unit)  212 . 
     Examples of the observation target M 1 , which is the target according to the present embodiment, include a fast-moving cell (e.g., a sperm). When such a cell is captured by the imaging device  10  and presented on the display device  30 , it is desirable to detect the image of the region of the cell from the background image at high speed and to display the movement in real time. However, if the detection of the region of the cell is time-consuming, the real-time display is disabled, and the sufficient observation of the cell including its movement is difficult. Therefore, according to the present embodiment, a high-processing load part of the detection algorithm is reduced so that the high-speed detection is enabled and the real-time cell observation is possible. 
     Furthermore, with regard to the learning side, basically, the existing algorithm disclosed in the above Non Patent Literature 1 may be used. Although the relearning by the learning unit  200  associated with the high-speed process at the detection side is not needed, the calculation of the multiwindow histogram that is similar to that at the detection side is incorporated at the time of learning. During the learning by the learning unit  200 , after the filter processing unit  202  performs a filter process on the learning image, the sampling unit  204  samples a correct window and an incorrect window based on the correct data, and then the multiwindow histogram calculating unit  206  calculates a multiwindow histogram with regard to the correct window. Here, the histogram is calculated on all the pixels. After the calculation of the histogram is finished, the similar matrix calculating unit  208  calculates a similar matrix, the clustering unit  210  executes clustering, and after the learning coefficient is derived, the discriminator generating unit  212  generates a discriminator. 
     Furthermore,  FIG.  3    is a schematic view illustrating an example of the configuration of the detection unit  300 . The detection unit  300  includes a filter processing unit  302 , a sampling unit  304 , a multiwindow histogram calculating unit  310 , and a discrimination processing unit  320 . The multiwindow histogram calculating unit  310  includes a window dividing unit  312 , a sub-window decimation processing unit  314 , and a histogram calculation interpolation processing unit  316 . Furthermore, the configurations of the learning unit  200  and the detection unit  300  illustrated in  FIG.  2    and  FIG.  3    may be configured by using a circuitry (hardware) or a processor such as a CPU and a program (software) for causing it to function. 
     Although the basic flow of the detection unit  300  is the same as that of the learning unit  200 , the learning unit  200  and the detection unit  300  are different in the multiwindow histogram calculation part. As illustrated in  FIG.  3   , in the multiwindow histogram calculating unit  310 , after the window division by the window dividing unit  312 , the sub-window decimation process by the sub-window decimation processing unit  314  and the histogram interpolation process by the histogram calculation interpolation processing unit  316  are performed. In the multiwindow histogram calculating unit  310 , the histogram calculation interpolation processing unit  316  interpolates the pixel values so as to calculate a histogram. The process of the multiwindow histogram calculating unit  310  is a part different from the learning unit  200 , and this process reduces the load. The process performed by the detection unit  300  is described below in detail. 
     2. Process Performed by the Detection Unit 
     2. 1. Outline 
     According to the present embodiment, in order to reduce the calculation load during the local feature value calculation, the division of the window in an image during the local feature value calculation and the pixel decimation process during the histogram calculation in the window are combined so as to reduce the calculation load. A window may be set to any size, and a window may be further divided into regions. Furthermore, the pixel decimation rate may be also changed for each divided region. A histogram is calculated for each region of the window and is applied to a discriminant function. Moreover, the present embodiment is characterized in that the decimation is not executed during learning but is executed during the detection, and the value of the pixel corresponding to the decimated pixel is interpolated, whereby the calculation load is reduced while the detection accuracy is maintained. 
     A local feature value is an image expression technique, and the pattern recognition is executed by using the feature value. To find or discriminate an object included in an image, it is necessary to extract the feature corresponding to the object. One of the calculation algorithms for expressing a feature value is the generation of a local histogram. As a local histogram, for example, a histogram such as HOG (Histograms of Oriented Gradients) may be used. 
     To calculate a local feature value, the process is performed to divide the image into cells or divide the image into patches. Furthermore, to generate a discriminator using machine learning, the histogram of a window region in the image is calculated, and a discriminant function such as SVM (Support Vector Machine) is used. 
     The large part of the processing load of the object recognition or the region detection is the calculation of local feature values. Due to the development in the technology of the GPU and distributed processing, there has been much more developments in the performance than ever before. Furthermore, it is expected that the combination of a real-time process and other recognition processes for sounds, or the like, in a mobile device such as a smartphone is increasingly used in the future. Therefore, it is desirable to reduce the load on the calculation of local feature values as much as possible. One of the examples of a reduction in the processing load is changing the calculation algorithm for a feature value; however, relearning processing is needed for changing the algorithm. Therefore, the present embodiment uses a method for reducing the amount of processing calculation without executing relearning. 
     2. 2. Specific Processing Content 
       FIG.  4    is a schematic view illustrating the manner of acquiring a histogram  500  representing an image feature from an image  10 . According to the present embodiment, a window  400  for detecting the feature of the image  10  is prepared, and the feature in the window  400  is converted into the histogram  500 . In the histogram  500 , the vertical axis represents a frequency, and the horizontal axis represents, for example, a luminance value. The histogram  500  is obtained for each window so that an image feature may be detected for each of the windows  400 . 
     To determine an image feature, the histogram of each image feature already acquired by learning is compared with the histogram  500  obtained from the window  400  of the image  10 . 
     The unit of detection of the histogram  500  may be flexibly determined depending on an application. For example, in the case of an object detection application, an image is divided into cells that are equally spaced, and the histogram  500  of a cell (=window) is calculated. In the case of a segmentation application, the histogram  500  of the window  400  is extracted by one pixel to several pixels so that the image feature of each region may be detected, although the calculation load is accordingly increased. 
     According to the present embodiment, the window dividing unit  312  divides the window  400 , and the sub-window decimation processing unit  314  executes pixel decimation, whereby the calculation cost during the detection of an image feature is reduced. Particularly, the present embodiment is most effective in a region detection process for determining the presence or absence of an object at the center of the window  400 . 
       FIG.  5    is a schematic view illustrating a method for dividing the window  400 . In the example of  FIG.  2   , the window  400  is divided into three regions, i.e., a sub-window  402 , a sub-window  404 , and a sub-window  406 . The sub-window  402  is the outermost region of the window  400 , the sub-window  406  is the region near the center of the window  400 , and the sub-window  404  is the intermediate region between the sub-window  402  and the sub-window  406 . Furthermore, it is desirable that the method for dividing the window  400  conforms with that for the learning. That is, the multiwindow histogram calculating unit  206  of the learning unit  200  calculates the histogram based on the division of the window that is the same as that for the multiwindow calculating unit  310  of the detection unit  300 . Moreover, at the learning side, a histogram is calculated by using all the pixels without executing the pixel decimation. Thus, it is unnecessary to execute relearning on the learning side. 
     With regard to each of the windows  400 , it is determined whether an object such as a cell is present, and it is presented on the display device  30 . Thus, an object such as a cell may be observed on the screen of the display device  30 . For example, when it is determined whether the central pixel of the window  400  is an object such as a cell, the most important window for determining the existence probability of an object is the sub-window  406  located at the center of the window  400 . 
     The sub-window decimation processing unit  314  performs a decimation process on each of the sub-window  402 , the sub-window  404 , and the sub-window  406 , which have been divided.  FIG.  6    is a schematic view illustrating a pixel decimation pattern of the window of 4×4 pixels. A pixel decimation pattern  600  illustrated in  FIG.  6    illustrates the case of a zigzag decimation pattern, and white pixels having no dots are decimated. The decimated pixels are not reflected in the histogram  500 . In the case of a zigzag decimation pattern such as the pixel decimation pattern  600 , the frequency of the histogram  500  is ½ of that in the case of no decimation. 
     Further, a pixel decimation pattern  610  illustrated in  FIG.  6    illustrates the case where the pixels are decimated to be ¼. The decimation process may reduce the calculation load on the generation of the histogram  500  and further increase the speed of the histogram calculation. 
     In the case of a zigzag decimation pattern such as the pixel decimation pattern  600 , the frequency of the histogram  500  is ½ of that in the case of no decimation. Therefore, the frequency is doubled to interpolate the value of the histogram  500 . Furthermore, when the pixels are decimated by ¼ as illustrated in a pixel decimation pattern  610 , the value of the histogram  500  is interpolated so that the frequency is quadrupled. Thus, the pixel values of the decimated pixels are interpolated so as to conform with the number of sets of data on the pixels used during the learning. This interpolation process eliminates the need for the relearning by the learning unit  200 . 
     The method for the interpolation process is not particularly limited; for example, in the case of the pixel decimation pattern  600  that is a zigzag decimation pattern illustrated in  FIG.  6   , the pixel value of a decimated pixel  602  is interpolated with the pixel value of an immediate left pixel  604  which has not been decimated. Furthermore, in the case of the pixel decimation pattern  610 , the pixel value of a decimated pixel  612  is interpolated with the average value of the pixel values of four neighboring pixels  614  which have not been decimated. Moreover, it is desirable that the pixel used for interpolation is the one located at a close distance from the center of the pixel to be interpolated; however, there is no particular limitation as long as a pixel is located in the vicinity as there is a subtle difference. This interpolation process is performed by the histogram calculation interpolation processing unit  316 . 
     According to the present embodiment, the combination of the division of the window  400  and the pixel decimation achieves a further speeding-up process. As described above, when it is determined whether the central pixel of the window  400  is an object such as a cell, the most important window for determining the existence probability of an object is the sub-window  406  located at the center of the window  400 . Therefore, as an outer window has a lower contribution rate for the existence probability of an object at the center of the window, an outer window among the divided windows has a higher decimation rate for the pixel decimation. This makes it possible to greatly reduce the processing load on the generation of the histogram  500  and to perform a speeding-up process. 
     After the histogram  500  is acquired, the image in the window may be discriminated based on the shape of the histogram  500 . The discrimination processing unit  320  of the detection unit  300  uses the discriminator generated by the discriminator generating unit  212  of the learning unit  200  to discriminate the image of the region in the window. The discriminator has a database of image arrays clustered in accordance with a histogram of a learning image. Therefore, the histogram  500  is applied to the discriminator so that the image of the region in the window may be discriminated. Thus, it is possible to make a discrimination as to whether a region in the window is, for example, a cell, a living tissue, or a background. 
     3. Flow of Process Performed in the Present Embodiment 
       FIG.  7    is a flowchart illustrating a process by the system  1  according to the present embodiment. First, at Step S 10 , the window  400  and the sub-windows  402 ,  404 , and  406  are set for an input image. Here, the window  400  is divided into the sub-windows  402 ,  404 , and  406  by the window dividing unit  312 , and the obtained sub-windows  402 ,  404 , and  406  are extracted. Furthermore, as the input image, it is possible to use the one to which the filter processing unit  302  has applied a filter process (edge detection, circle detection, or the like). 
     At the subsequent Step S 12 , a pixel decimation pattern is set. Here, a pixel decimation pattern is set for each of the sub-windows  402 ,  404 , and  406 . A pixel decimation pattern is set such that an outer side of the window  400  has a higher decimation rate for the pixel decimation, as described above. 
     At the subsequent Step S 14 , the histogram  500  illustrated in  FIG.  4    is generated based on the information obtained from each of the sub-windows  402 ,  404 , and  406 . A decimation pattern is applied to each of the set sub-windows, and the histogram  500  is generated for each sub-window. 
     At the subsequent Step S 16 , the histogram  500  is corrected. Here, the above-described interpolation process is performed on each of the sub-windows  402 ,  404 , and  406 , and the histogram  500  is corrected by using the obtained pixel values. The interpolation is executed by using the data on the adjacent pixels corresponding to the number of pixels to be interpolated in accordance with the decimation pattern. The operations at Step S 14  and Step S 16  may be performed simultaneously. 
     At the subsequent Step S 18 , the image of the region is discriminated. Here, the discriminator generated by the learning unit  200  is used, and the discrimination processing unit  320  applies the discriminator to the histogram  500  to discriminate the image of the region in the window  400 . Thus, the determination on the feature of the image is made, including the determination as to whether the image in the window  400  is an object or a background. 
     4. Example of Adaptation to Application 
     It is expected that, for the capturing and the analysis of cells for which real-time performance is required, there is an increasing need for changing the capture speed in accordance with the magnitude of the movement of a cell in the future. Here, the capture speed is the interval of time to acquire the image of an object, and a shorter interval causes a higher capture speed. For the real-time detection in the case of an increase in the capture speed, an increase in the speed of the detection process is expected. 
       FIG.  8    is a schematic view illustrating an example of the application for real-time object detection.  FIG.  8    illustrates a state in which the observation target M 1  is displayed on a display screen  32  of the display device  30 . For example,  FIG.  8    illustrates an example in which the display device  30  is configured by using a touch panel, and the capture speed may be changed by sliding an operating button  34 . 
     When the observation target M 1  is a moving cell, the discrimination processing unit  320  of the detection unit  300  performs the process to discriminate the object present in each of the windows  400  as to whether it is a cell or a background. Thus, as illustrated in  FIG.  8   , the outline of the cell, which is the observation target M 1 , is detected in real time. 
     In the case of the real-time object detection, the processing load may be predicted based on the capture speed and the screen size. For this reason, the detection speed at which the discrimination processing unit  320  detects whether it is a cell or a background is changed in accordance with the capture speed. For example, as the capture speed becomes higher, the detection speed may be changed to be doubled, quadrupled, or the like, in accordance with the capture speed. The detection speed may be determined in accordance with the amount of decimation, and as the amount of decimation by the sub-window decimation processing unit  314  is larger, it may be faster. In accordance with the detection speed, the sub-window decimation processing unit  314  increases the amount of decimation as the detection speed becomes faster. Further, as the detection speed becomes slower, the amount of decimation becomes smaller; thus, the image quality of the image may be enhanced. Thus, it is possible to optimally adjust the amount of decimation by the sub-window decimation processing unit  314  in accordance with the discrimination mode in which the detection speed is emphasized or the image quality is emphasized. 
     There is a method for tracking and displaying an object based on the feature of the shape of the object when the object is displayed in real time. However, in the technique using tracking, when the shape of an object such as a cell is changed, the tracking itself is difficult, and the real-time detection on the region of the dynamically moving object is not possible. Therefore, to detect particularly an object having a variable region, it is desirable to use the discriminator obtained by the learning as in the present embodiment. The processing load is increased if the object detection is performed every time the capture speed is increased; however, if the processing load is reduced by using the technique according to the present embodiment, the object detection with high real-time performance is possible. Thus, the present embodiment is advantageous in the case where an object (e.g., a cell such as a sperm) moving and changing in size or motion is detected in real time, displayed on the screen, and observed. 
     As described above, according to the present embodiment, as the window  400  is divided and the pixel value is decimated for each of the sub-windows  402 ,  404 , and  406 , the processing load on the generation of the histogram  500  may be reduced, and a further increase in the speed of the feature value detection may be achieved. In this case, as the input image itself is not reduced, it is possible to obtain an image with higher accuracy as compared with that in the detector that performs a reduction process. Further, even when the frame rate is changed, it is possible to perform processing without reducing the capture size. 
     While the preferred embodiment of the present disclosure is described above in detail with reference to the accompanying drawings, the technical scope of the present disclosure is not limited to the above example. It is apparent to those skilled in the art of the present disclosure that various modifications and variations may be made within the scope of the technical idea described in the claims, and it is understood that they naturally belong to the technical scope of the present disclosure. 
     Furthermore, the advantage described in this description is merely for purposes of description or illustration and not limitation. That is, the technique according to the present disclosure may have other advantages apparent to those skilled in the art from this description in addition to or in place of the advantage described above. 
     Furthermore, the following configuration also belongs to the technical range of the present disclosure. 
     (1) 
     An image processing apparatus comprising: a dividing unit that divides a detection region for detecting a feature value of an image into a plurality of regions; 
     a decimation processing unit that performs a decimation process on a pixel value for each of the regions; and 
     a histogram calculating unit that interpolates a pixel value having undergone the decimation process to calculate a histogram of pixel values of the regions. 
     (2) 
     The image processing apparatus according to (1), wherein the decimation processing unit performs the decimation process such that the outer region of the detection region has a larger decimation amount for the decimation process. 
     (3) 
     The image processing apparatus according to (1) or (2), wherein the dividing unit divides the detection region into the regions that are concentric with respect to a center of the detection region. 
     (4) 
     The image processing apparatus according to (3), wherein the decimation processing unit performs the decimation process such that, with regard to the concentric regions, the outer concentric region has a larger decimation amount for the decimation process. 
     (5) 
     The image processing apparatus according to any one of (1) to (4), wherein the histogram calculating unit interpolates the pixel value having undergone the decimation process with a pixel value of a pixel adjacent to a pixel having undergone the decimation process. 
     (6) 
     The image processing apparatus according to any one of (1) to (5), further comprising a discrimination processing unit that discriminates a feature of an image of the detection region based on the histogram. 
     (7) 
     The image processing apparatus according to (6), wherein the discrimination processing unit discriminates an image of the detection region based on a discriminator that has classified an image feature obtained by learning. 
     (8) 
     The image processing apparatus according to (7), wherein the dividing unit executes division into the regions in accordance with a region division for calculating a histogram of pixel values of a learning image during the learning. 
     (9) 
     The image processing apparatus according to (6), wherein the decimation processing unit changes the decimation amount for the decimation process in accordance with a discrimination mode for discriminating a feature of the image. 
     (10) 
     An image processing method comprising: 
     dividing a detection region for detecting a feature value of an image into a plurality of regions; 
     performing a decimation process on a pixel value for each of the regions; and 
     interpolating a pixel value having undergone the decimation process to calculate a histogram of pixel values of the regions. 
     (11) 
     A program causing a computer to function as: 
     a means that divides a detection region for detecting a feature value of an image into a plurality of regions; 
     a means that performs a decimation process on a pixel value for each of the regions; and 
     a means that interpolates a pixel value having undergone the decimation process to calculate a histogram of pixel values of the regions. 
     REFERENCE SIGNS LIST 
     
         
           300  DETECTION UNIT 
           312  WINDOW DIVIDING UNIT 
           314  SUB-WINDOW DECIMATION PROCESSING UNIT 
           316  HISTOGRAM CALCULATION INTERPOLATION PROCESSING UNIT