Patent Publication Number: US-9892336-B2

Title: Detection devices and methods for detecting regions of interest

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 61/994,240, filed on May 16, 2014, the contents of which are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The invention relates to a detection device, and, more particularly, to a detection device for detecting a region of interest according to vital sign signals. 
     Description of the Related Art 
     Recently, video cameras are provided to determine vital signs of human subjects through non-contact methods. There are several advantages of extracting vital signs by video camera; it is convenient, comfortable, and safe to the human subjects, because of the wireless and non-contact operation of the video cameras. Moreover, compared with major medical equipment, the cost of a video camera is low. For long-term monitoring in home care, the lower cost is beneficial to the user. 
     When a video camera is used for extracting vital sign signals, a region of interest (ROI) should be identified in advance, such as a region of the skin of the human subject that will allow for heart-action detection or blood-oxygen detection, or the chest region can be used for detection of the respiration rate. However, if the human subject is not facing the video camera, the conditions surrounding the human subject can be too light or too dark, or the subject&#39;s face may not be recognized, and thus the video camera is not capable identifying an appropriate ROI. In such cases, vital signs cannot be measured accurately, or the measurement of vital signs will fail. 
     BRIEF SUMMARY OF THE INVENTION 
     Thus, it is desirable to provide a detection device which can detect a region of interest according to signals related to vital signs of subjects. 
     An exemplary embodiment of a detection device is provided. The detection device detects at least one region of interest (ROI) in a first frame captured by an image sensor. The detection device comprises an image processing module, a calculation module, and an identification module. The image processing module is configured to divide the first frame into a plurality of sub regions. The calculation module is configured to obtain a first vital-sign feature of a first sub region among the plurality of sub regions to generate a first feature signal. The identification module is configured to receive the first feature signal and determine whether the first feature signal is a first valid image signal. When the identification module determines the first feature signal is the first valid image signal, the identification module identifies the first sub region as a first ROI. 
     An exemplary embodiment of a detection method is provided. The detection method is performed to detect at least one region of interest (ROI). The detection method comprises steps of capturing a plurality of successive frames; dividing a first frame among the plurality of successive frame into a plurality of sub regions; obtaining a first vital-sign feature of a first sub region among the plurality of sub regions; generating a first feature signal according to the first vital-sign feature; determining whether the first feature signal is a first valid image signal; when it is determined that the first feature signal is the first valid image signal, identifying the first sub region as a first ROI; and tracking the first ROI in the frames occurring after the first frame. 
     Another exemplary embodiment of an image tracking apparatus. The tracking apparatus comprises an image sensor, a detection device, and a tracking module. The image sensor is configured to capture a plurality of successive frames. The detection device is configured to perform a detection operation on the successive frames by dividing one frame into a plurality of sub regions, obtain at least one vital-sign feature of at least one sub region among the plurality of sub regions to generate at least one feature signal, and determine whether the least one feature signal is valid, wherein when the detection device determines that the at least one feature signal is valid, the detection device identifies the at least one sub region as a region of interest (ROI). The tracking module is configured to track the ROI in the frames occurring after the one frame. 
     A detailed description is given in the following embodiments with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention can be more fully understood by reading the subsequent detailed description and examples with references made to the accompanying drawings, wherein: 
         FIG. 1  is a schematic diagram illustrating a portable electronic device according to an exemplary embodiment of the invention; 
         FIG. 2  shows one exemplary embodiment of an electronic system; 
         FIG. 3  is a schematic diagram illustrating division of a reference frame by quadrangles; 
         FIG. 4  is a schematic diagram illustrating division of a reference frame by super-pixels; 
         FIG. 5  shows another exemplary embodiment of an electronic system; 
         FIGS. 6 and 7  show an exemplary embodiment of a detection device; 
         FIG. 8  shows an exemplary embodiment of a tracking apparatus; and 
         FIG. 9  shows an exemplary embodiment of a detection method. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The following description is of the best-contemplated mode of carrying out the invention. This description is made for the purpose of illustrating the general principles of the invention and should not be taken in a limiting sense. The scope of the invention is best determined by reference to the appended claims. 
       FIG. 1  is a schematic diagram illustrating a portable electronic device  100  according to an exemplary embodiment of the invention. The portable electronic device  100  may comprise a processor  110 , a memory unit  120 , a RF circuitry  130 , a touch screen  140 , and a camera  150 . In an exemplary embodiment, the portable electronic device  100  may be a cellular telephone, a smartphone or a tablet PC. The processor  110  may be one or more data processors, image processors, digital signal processors, graphic processor, and/or central processors, which are capable of executing one or more types of computer readable medium stored in the memory unit  120 . The processor  110  is coupled to the RF circuitry  130 , the touch screen  140 , and the camera  150  through a peripheral interface  115 , as illustrated in  FIG. 1 . 
     The RF circuitry  130  may be coupled to one or more antennas  135  and may allow communication with one or more additional devices, computers and/or servers using a wireless network. The portable electronic device  100  may support various communications protocols, such as code division multiple access (CDMA), Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), High-Speed Downlink Packet Access (HSDPA), Wi-Fi (such as IEEE 802.11a/b/g/n), Bluetooth, Wi-MAX, a protocol for email, instant messaging (IM), and/or a short message service (SMS), but the invention is not limited thereto. 
     The camera  150  may capture a plurality of frames from scenes and transmit signals related to the captured frames to the processor  110  through the peripheral interface  115 . The peripheral interface  115  is coupled to the camera  150  by a wired or wireless connection manner. In the embodiment of  FIG. 1 , the camera  150  is equipped in the portable electronic device  100 . However, in another embodiment, the camera  150  is implemented independently or implemented in another device and coupled to the portable electronic device  100  by a wired or wireless manner. 
     The touch screen  140  may detect contact and any movement or break thereof using any of a plurality of touch sensitivity technologies now known or to be later developed, including, but not limited to, capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch screen  140 . However, the touch screen  140  may also display visual output from the portable electronic device  100 . In some embodiments, the portable electronic device  100  may include circuitry (not shown in  FIG. 1 ) for supporting a location determining capability, such as that provided by the Global Positioning System (GPS). In some embodiments, the touch screen  140  can be replaced by a display screen when the touch-sensitive function is not needed. 
     The memory controller  112  may be coupled to the memory unit  120 , which may include one or more types of computer readable medium. The memory unit  120  may include high-speed random access memory (e.g. SRAM or DRAM) and/or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, and/or flash memory. The memory unit  120  may store an operating system  122 , such as LINUX, UNIX, OS X, WINDOWS, Android, or an embedded operating system such as VxWorks. The operating system  122  may include procedures for handling basic system services and for performing hardware dependent tasks. The memory unit  120  may also store communication procedures in a communication module  124 . The communication procedures may be used for communicating with one or more additional devices, one or more computers and/or one or more servers. The memory unit  120  may include a display module  125 , a contact/motion module  126  to determine one or more points of contact and/or their movement, and a graphics module  128 . The graphics module  128  may support widgets, that is, modules or applications with embedded graphics. The widgets may be implemented using JavaScript, HTML, Adobe Flash, or other suitable computer program languages and technologies. 
     The memory unit  120  may also include one or more applications  129 . For example, applications stored in the memory unit  120  may include telephone applications, email applications, text messaging or instant messaging applications, memo pad applications, address books or contact lists, calendars, picture taking and management applications, and music playing and management applications. The applications  129  may include a web browser (not shown in  FIG. 1 ) for rendering pages written in the Hypertext Markup Language (HTML), Wireless Markup Language (WML), or other languages suitable for composing web pages or other online content. The memory unit  120  may further include a keyboard module (or a set of instructions)  131 . The keyboard module  131  operates one or more soft keyboards. 
     It should be noted that each of the above identified modules and applications correspond to a set of instructions for performing one or more functions described above. These modules (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules. The various modules and sub-modules may be rearranged and/or combined. Various functions of the portable electronic device  100  may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits. 
       FIG. 2  shows an exemplary embodiment of an electronic system. As shown in  FIG. 2 , an electronic system  2  comprises a processor  20  and an image sensor  21 . The processor  20  operates to detect a region of interest (ROI) on a subject. The image sensor  21  operates to capture a plurality of successive frames from scenes in a plurality of frame periods. The processor  20  is coupled to the image sensor  21  to receive sensing signals S 20  related to the captured frames. The processor  20  selects one frame among the captured frames to serve as a reference frame. In an embodiment, the reference frame may be the frame which is captured in the first frame period occurring after the processor  20  starts operating to detect an ROI. The processor  20  divides the reference frame into a plurality of sub regions. Accordingly, the sensing signals S 20  related to the reference frame respectively belong to the sub regions of the reference frame, and the sensing signal S 20  of each sub region comprises color information of the pixels covered by the sub region. The color information may be RGB, YUV, YCrCb, grayscale, infrared data, or sensor raw. In an embodiment, the processor  20  divides the reference frame by pixel configuration of the image sensor, a plurality of quadrangles, a plurality of polygons, or a plurality of circles. For example, as shown in  FIG. 3 , the reference frame  3  shows a scene including a subject (such as a human being)  30  and a background  31 , and the frame  3  is divided into sub regions, such as sub regions  301  and  302 , by quadrangles. In another embodiment, the processor  20  divides the reference frame by super-pixels. As shown in  FIG. 4 , there are some edges between a subject (such as a human being)  40  and a background  41 . Moreover, the background  41  includes some portions with relatively high brightness and some portions with relatively low brightness, which causes a color difference between the portions of varying brightness. Thus, the processor  20  divides the reference frame into sub regions (such as sub regions  401  and  402 ) by super-pixels which are determined according to the edge appearance and color difference shown in the reference frame  4 . Each sub region corresponds to one object. For example, as shown in  4 , the object corresponding to the sub region  402  includes the mouth, nose, chin and cheeks of the subject  40 . 
     After the division of the reference frame is completed, the subject may move in the following frame periods. At this time, the shape and/or position of at least one sub region may be changed by tracking corresponding the object of the at least one sub region. For example, as shown in  FIG. 4 , when the subject  40  moves towards the right, the position of the sub region  402  moves with the movement of the corresponding object (including the mouth, nose, chin and cheeks). The shape and/or position of the sub regions covering the subject  40  or disposed near the subject  40  may be changed by the movement of the corresponding object. 
     When the sub regions of the reference frame are obtained, the processor  20  performs a specific operation on the sensing signal S 20  of each sub region of the reference frame within a predetermined time-interval occurring after the reference frame is captured to obtain the feature. In the embodiment, within the predetermined time-interval, there are several frame periods. The processor  20  generates a feature signal related to the sub region according to the obtained feature. In the embodiment, the feature signal is a signal related to a vital sign of the subject (a human being  30  or  40 ), such as the heart rate, respiration rate, or blood-oxygen content. In the following, the specific operation will be described by using an example in which a feature signal related to the heart rate is obtained. In this case, the feature signal is referred to as a “vital sign signal”. Since the color of the skin of a specific subject can change when blood flows through it, the color information can serve as a vital-sign feature to estimate the heart rate of the subject. In this case, for each sub region, the processor  20  performs the specific operation on the corresponding sensing signal to calculate an average value of at least one color component (such as R, G, and/or B component) of the pixels, involved in the corresponding sensing signal, within the predetermined time-interval. The calculated average value serves as the vital-sign feature of the sub region. According to the calculated average value, the processor  20  estimates the feature signal related to the heart rate. 
     In the embodiment, the processor  20  estimates the feature signals of all the sub regions of the reference frame. However, in other embodiments, the processor  20  may estimate the feature signals of some of the sub regions of the reference frame. For example, the quality of the sensing signals from the sub regions with over-exposure or under-exposure in the reference frame is usually lower due to the subtle changes of vital signs may be lost, and, thus, these sub regions with over-exposure or under-exposure may be invalid for the ROI detection. Thus, the processor  20  just estimates the feature signals of the sub regions excluding the sub region with over-exposure or under-exposure. 
     Then, for each estimated feature signal, the processor  20  determines whether the feature signal is a valid image signal. In an embodiment, the processor  20  assesses the quality of the feature signal and determines whether the feature signal is a valid image signal according to the assessed quality. In one case, the processor  20  may determine whether the image difference in one sub region between two adjacent frame periods occurring within the predetermined time-interval is larger than a predetermined threshold (referred to as “pixel domain manner” for quality assessment). When the image difference is not larger than the predetermined threshold, which means that the subject may not move or may move by slight shifting in the two adjacent frame periods shifting, the processor  20  determines that the quality of the feature signal is high and determines the feature signal is a valid image signal; when the image difference is larger than the predetermined threshold, which means that the subject may move by greater shift in the two adjacent frame periods shifting, the processor  20  determines that the quality of the feature signal is low and determines that the feature signal is not a valid image signal. In another case, the processor  20  may determine whether there is a relative high peak occurring in the feature signal of one sub region within the predetermined time-interval (referred to as “signal domain manner” for quality assessment). When there is no relative high peak, which means that the subject may have stable feature signal or may not move or move by slight shifting in these several successive frame periods, the processor  20  determines that the quality of the feature signal is high and determines the feature signal is a valid image signal; when there is a relative high peak, which means that the subject may have unstable feature signal or may move by greater shifting in these several successive frame periods, the processor  20  determines that the quality of the feature signal is low and determines that the feature signal is not a valid image signal. In further another case, the processor  20  may convert the feature signal from time domain to frequency domain via some techniques, such as fast Fourier transform (FFT). The processor  20  determines whether the spectrum energy is concentrated within a small nearby frequency range of one spectrum frequency (referred to as “frequency domain manner” for quality assessment). When the concentration of the spectrum energy is larger than the predetermined threshold, the processor  20  determines that the quality of the feature signal is high and determines the feature signal is a valid image signal; when the concentration of the spectrum energy is not larger than the predetermined threshold, the processor  20  determines that the quality of the feature signal is low and determines that the feature signal is not a valid image signal. The processor  20  assesses the quality of each feature signal by at least one of the pixel domain, signal domain, and frequency domain manners. In the embodiment, for one sub region, at least one of the manners defined above is performed after the obtainment of the feature signal is completed. In another embodiment, for one sub region, at least one of the manners defined above is performed in several frame periods within the predetermined time-interval, wherein the total length of the several frame periods is shorter than the predetermined time-interval. That is, the quality of the feature can be pre-assessed before the estimation of the feature signal is completed. 
     In another embodiment, for each estimated feature signal, the processor  20  compares the feature signal with a predetermined reference signal which is obtained in advance or has previously been stored in a memory and determines whether the feature signal is a valid image signal according to the comparison result (feature comparison). When the difference between the feature signal and the predetermined reference signal is within a reference range, the processor  20  determines that the feature signal is a valid image signal; when the difference between the feature signal and the predetermined reference signal is outside the reference range, the processor  20  determines that the first feature signal is not a valid image signal. 
     In further another embodiment, for each estimated feature signal, the processor  20  determines whether the feature signal is a valid image signal by performing object detection (such as skin detection, face detection, or chest detection) to the sub regions or using the position information of the subject. In some embodiments, the processor  20  may perform at least two of the quality assessment, the feature comparison, and object detection to determine whether the feature signal is a valid image signal. 
     When only one feature signal is determined as a valid image signal, the processor  20  directly identifies the corresponding sub region as an ROI. In a case that several feature signals are determined as valid image signals, the processor  20  merges the corresponding sub regions which are adjacent to each other to form a merged region and identifies the merged region as an ROI. If there is still a sub region separated from the merged region, the processor  20  identifies the sub region as another ROI. When the ROI(s) is identified, the ROI can be shown in a display disposed in the electronic system  2  or connected to the electronic system  2 , such as a liquid crystal display or a touch panel. 
     In the embodiment of  FIG. 2 , both the processor  20  and image sensor  21  are implemented in one single electronic device, such as the portable electronic device  100  shown in  FIG. 1 . The processor  20  is implemented by the processor  110  of  FIG. 1 , and the image sensor  21  is a camera, such as the camera  150  shown in  FIG. 1 . In another embodiment, the processor  20  and image sensor  21  are implemented in different electronic devices. For example, the processor  20  is implemented in a vital sign measurement device, while the image sensor  21  is implemented in a camera. The processor  20  is coupled to the image sensor  21  in a wired or wireless connection manner. 
     In the embodiment of  FIG. 2 , the processor  20  may be implemented by one image processor and one data processor. As shown in  FIG. 5 , an image processor  50  performs the operations related to the frame division as described above, while a data processor  51  performs the operations related to the obtainment of the sensing signals, the estimation of the feature signals from the sensing signals, the determination of whether each feature signal is a valid image signal, and the identification of ROIs. 
     According to the above embodiment, when at least one ROI is identified, the ROI may serve as a region for vital sign measurement, such as heart rate, respiration, blood-oxygen content of the subject, facial recognition, or camera auto-focus. 
       FIG. 6  shows an exemplary embodiment of a detection device. As shown in  FIG. 6 , a detection device  6  comprises an image processing module  61 , a calculation module  62 , and an identification module  63  and operates to detect a region of interest (ROI) on a subject. There is an image sensor  60  coupled to the detection device  6  by a wired or wireless connection manner. The image sensor  60  operates to capture a plurality of successive frames from scenes in a plurality of frame periods. In the embodiment, the image sensor  60  is a camera, such as the camera  150  shown in  FIG. 1 . The image processing module  61  is coupled the image sensor  60  to receive sensing signals S 60  related to the captured frames. The image processing module  61  selects one frame among the captured frames to serve as a reference frame. In an embodiment, the reference frame may be the frame which is captured in the first frame period occurring after the processor  20  starts operating to detect an ROI. The image processing module  61  divides the reference frame into a plurality of sub regions. Accordingly, the sensing signals S 60  related to the reference frame respectively belong to the sub regions of the reference frame, and the sensing signal S 60  of each sub region comprises color information of the pixels covered by the sub region. The color information may be RGB, YUV, YCrCb, grayscale, infrared data, or sensor raw. In an embodiment, the image processing module  61  divides the reference frame by pixel configuration of the image sensor, a plurality of quadrangles, a plurality of polygons, or a plurality of circles. For example, as shown in  FIG. 3 , the reference frame  3  shows a scene including a subject (such as a human being)  30  and a background  31 , and the frame  3  is divided into sub regions, such as sub regions  301  and  302 , by quadrangles. In another embodiment, the image processing module  61  divides the reference frame by super-pixels. As shown in  FIG. 4 , there are some edges between a subject (such as a human being)  40  and a background  41 . Moreover, the background  41  includes some portions with relatively high brightness and some portions with relatively low brightness, which causes a color difference between the portions of varying brightness. Thus, the image processing module  61  divides the reference frame into sub regions (such as sub regions  401  and  402 ) by super-pixels which are determined according to the edge appearance and color difference shown in the reference frame  4 . Each sub region corresponds to one object. For example, as shown in  4 , the object corresponding to the sub region  402  includes the mouth, nose, chin and cheeks of the subject  40 . 
     After the division of the reference frame is completed, the subject may move in the following frame periods. At this time, the shape and/or position of at least one sub region is changed by tracking corresponding the object of the at least one sub region. For example, as shown in  FIG. 4 , when the subject  40  moves towards the right, the position of the sub region  402  moves with the movement of the corresponding object (including the mouth, nose, chin and cheeks). The shape and/or position of the sub regions covering the subject  40  or disposed near the subject  40  may be changed by the movement of the corresponding object. 
     When the sub regions of the reference frame are obtained, the calculation module  62  performs a specific operation on the sensing signal S 60  of each sub region of the reference frame within a predetermined time-interval occurring after the reference frame is captured to obtain the feature. In the embodiment, within the predetermined time-interval, there are several frame periods. The calculation module  62  generates a feature signal related to the sub region according to the obtained feature. In the embodiment, the feature signal is a signal related to a vital sign of the subject (a human being  30  or  40 ), such as the heart rate, respiration rate, or blood-oxygen content. In the following, the specific operation will be described by using an example in which a feature signal related to the heart rate is obtained. In this case, the feature signal is referred to as a “vital sign signal”. Since the color of the skin of a specific subject changes when blood flows through it, the color information can serve as a vital-sign feature to estimate the heart rate of the subject. In this case, for each sub region, the calculation module  62  performs the specific operation on the corresponding sensing signal to calculate an average value of at least one color component (such as R, G, and/or B component) of the pixels, involved in the corresponding sensing signal, within the predetermined time-interval. The calculated average value serves as the vital-sign feature of the sub region. According to the calculated average value, the calculation module  62  estimates the feature signal related to the heart rate. 
     In the embodiment, the calculation module  62  estimates the feature signals of all the sub regions of the reference frame. However, in other embodiments, the calculation module  62  may estimate the feature signals of some of the sub regions of the reference frame. For example, the quality of the sensing signals from the sub regions with over-exposure or under-exposure in the reference frame is usually lower due to the subtle changes of vital signs may be lost, and, thus, these sub regions with over-exposure or under-exposure may be invalid for the ROI detection. Thus, calculation module  62  just estimates the feature signals of the sub regions excluding the sub region with over-exposure or under-exposure. 
     For each estimated feature signal, the identification module  63  determines whether the feature signal is a valid image signal. In an embodiment, the identification module  63  assesses the quality of the feature signal and determines whether the feature signal is a valid image signal according to the assessed quality. In one case, the identification module  63  may determine whether the image difference in one sub region between two adjacent frame periods occurring within the predetermined time-interval is larger than a predetermined threshold (referred to as “pixel domain manner” for quality assessment). When the image difference is not larger than the predetermined threshold, which means that the subject may not move or may move by slight shifting in the two adjacent frame periods shifting in the two adjacent frame periods shifting, the identification module  63  determines that the quality of the feature signal is high and determines the feature signal is a valid image signal; when the image difference is larger than the predetermined threshold, which means that the subject may move by greater shifting, the identification module  63 , determines that the quality of the feature signal is low and determines that the feature signal is not a valid image signal. In another case, the identification module  63  may determine whether there is a relative high peak occurring in the feature signal of one sub region within the predetermined time-interval (referred to as “signal domain manner” for quality assessment). When there is no related high peak, which means that the subject may have stable feature signal or may not move or move by slight shifting in these several successive frame periods, the identification module  63  determines that the quality of the feature signal is high and determines the feature signal is a valid image signal; when there is a relative high peak, which means that the subject may have unstable feature signal or may move by greater shifting in these several successive frame periods, the identification module  63  determines that the quality of the feature signal is low and determines that the feature signal is not a valid image signal. In further another case, the identification module  63  may convert the feature signal from time domain to frequency domain via some techniques, such as fast Fourier transform (FFT). The identification module  63  determines whether the spectrum energy is concentrated within a small nearby frequency range of one spectrum frequency (referred to as “frequency domain manner” for quality assessment). The frequency range is determined according to the feature obtained by the calculation module  62 , such as the heart rate. When the concentration of the spectrum energy is larger than the predetermined threshold, the identification module  63  determines that the quality of the feature signal is high and determines the feature signal is a valid image signal; when the concentration of the spectrum energy is not larger than the predetermined threshold, the identification module  63  determines that the quality of the feature signal is low and determines that the feature signal is not a valid image signal. The identification module  63  assesses the quality of each feature signal by at least one of the pixel domain, signal domain, and frequency domain manners. In the embodiment, for one sub region, at least one of the manners defined above is performed after the obtainment of the feature signal is completed. In another embodiment, for one sub region, at least one of the manners defined above is performed in several frame periods within the predetermined time-interval, wherein the total length of the several frame periods is shorter than the predetermined time-interval. That is, the quality of the feature can be pre-assessed before the estimation of the feature signal is completed. 
     In another embodiment, for each estimated feature signal, the identification module  63  compares the feature signal with a predetermined reference signal which is obtained in advance or has previously been stored in a memory and determines whether the feature signal is a valid image signal according to the comparison result (feature comparison). When the difference between the feature signal and the predetermined reference signal is within a reference range, the identification module  63  determines that the feature signal is a valid image signal; when the difference between the feature signal and the predetermined reference signal is outside the reference range, the identification module  63  determines that the first feature signal is not a valid image signal. 
     In further another embodiment, for each estimated feature signal, the identification module  63  determines whether the feature signal is a valid image signal by performing object detection (such as skin detection, face detection, or chest detection) to the sub regions or using the position information of the subject. In some embodiments, the identification module  63  may perform at least two of the quality assessment, the feature comparison, and object detection to determine whether the feature signal is a valid image signal. 
     When only one feature signal is determined as a valid image signal, the identification module  63  directly identifies the corresponding sub region as an ROI. In cases where several feature signals are determined as valid image signals, the identification module  63  merges the corresponding sub regions which are adjacent to each other to form a merged region and identifies the merged region as an ROI. If there is still a sub region separated from the merged sub region, the identification module  63  identifies the sub region as another ROI. When the ROI(s) is identified, the ROI can be shown in a display disposed in the detection device  6  or connected to the detection device  6 , such as a liquid crystal display or a touch panel. 
     In the embodiment of  FIG. 6 , the detection device  6  may be implemented by a processor, such as the processor  110  shown in  FIG. 1 . In an embodiment, each of the modules in the detection device  6  may be implemented in a processor, such as the processor  110  shown in  FIG. 1  by hardware and/or software performing one or more corresponding functions described above. In another embodiment, a memory is coupled to the detection device  6 . As shown in  FIG. 7 , a memory  7  stores sets of instructions (or coding)  70 ,  71 , and  72 , respectively corresponding to the functions of the modules shown in  FIG. 6 . The detection device  6  is coupled to the memory  7  to load the sets of instructions  70 ,  71 , and  72 . When the detection device  6  performs any one set of instructions, the hardware and/or software in the detection device  6  is referred to as the corresponding module. For example, when the detection device  6  performs the set of instructions related to the quality assessment function, the hardware and/or software in the detection device  6  is referred to as the identification module  63 . 
     According to the above embodiment, when at least one ROI is identified, the ROI may serve as a region for measurement of a vital sign, such as heart rate, respiration, blood-oxygen content of the subject, facial recognition, or camera auto-focus. 
       FIG. 8  shows an exemplary embodiment of a tracking apparatus. As shown in  FIG. 8 , an image tracking apparatus  8  comprises an image sensor  80 , a detection device  81 , and a tracking module  82 . The image sensor  80  operates to capture a plurality of successive frames from scenes in a plurality of frame periods. The detection device  81  performs the same detection operation as the detection device  6  of  FIG. 6  to detect an ROI. After the ROI is detected, the tracking module  82  tracks the ROI in the following frames. 
     In an embodiment, the tracking module  82  may perform at least one tracking algorithm to track the ROI, such as an algorithm comprising at least one of existed image-based tracking techniques, such as mean shift, particle filter, or mosses. In another embodiment, the tracking module  82  enables the detection device  81  to repeatedly perform the detection operation to track the ROI. In further another embodiment, the tracking module  82  performs at least one tracking algorithm and enables the detection device  81  to repeatedly perform the detection operation to track the ROI. 
     The image tracking apparatus  8  may be implemented as a portable electronic device, such as the portable electronic device  100  of  FIG. 1  or a camera device. In another embodiment, the image tracking apparatus  8  may be implemented as bio-signal sensor for detecting heart rate, respiration rate, or blood-oxygen content form the ROI(s). Each of the detection device  81  and the tracking module  82  may be implemented in a processor, such as the processor  110  of  FIG. 1  by hardware and/or software performing one or more corresponding functions described above. 
       FIG. 9  shows an exemplary embodiment of a detection method. The detection method may be performing by at least one processor, such as the processor  110  shown in  FIG. 1  or the processor  20  shown in  FIG. 2 , or at least one module, such as the modules shown in  FIG. 6 . The detection method is performed to detect a region of interest (ROI) on a subject. A plurality of successive frames from scenes in a plurality of frame periods are captured by an image sensor, such as the camera  150  shown in  FIG. 1 . The detection method comprises a step of selecting one frame among the captured frames to serve as a reference frame (step S 90 ). In an embodiment, the reference frame may be the frame which is captured in the first frame period occurring after the detection method starts. In another embodiment, the reference frame may be a frame which is captured in any frame period occurring after the detection method starts. The detection method further comprises a step of dividing the reference frame into a plurality of sub regions (step S 91 ). Accordingly, the sensing signals related to the reference frame respectively belong to the sub regions of the reference frame, and the sensing signal of each sub region comprises color information of the pixels covered by the sub region. The color information may be RGB, YUV, YCrCb, grayscale, infrared data, or sensor raw. In an embodiment, the reference frame is divided by the pixel configuration of the image sensor, a plurality of quadrangles, a plurality of polygons, or a plurality of circles. For example, as shown in  FIG. 3 , the reference frame  3  shows a scene including a subject (such as a human being)  30  and a background  31 , and the frame  3  is divided into sub regions, such as sub regions  301  and  302 , by quadrangles. In another embodiment, the reference frame is divided by super-pixels. As shown in  FIG. 4 , there are some edges between a subject (such as a human being)  40  and a background  41 . Moreover, the background  41  includes some portions with relatively high brightness and some portions with relatively low brightness, which causes a color difference between the portions of varying brightness. Thus, the reference frame is divided into sub regions (such as sub regions  401  and  402 ) by super-pixels which are determined according to the edge appearance and color difference shown in the reference frame  4 . Each sub region corresponds to one object. For example, as shown in  4 , the object corresponding to the sub region  402  includes the mouth, nose, chin and cheeks of the subject  40 . 
     After the division step of the reference frame is completed, the subject may move in the following frame periods. At this time, the shape and/or position of at least one sub region may be changed by tracking corresponding the object of the at least one sub region. For example, as shown in  FIG. 4 , when the subject  40  moves towards the right, the position of the sub region  402  moves with the movement of the corresponding object (including the mouth, nose, chin and cheeks). The shape and/or position of the sub regions covering the subject  40  or disposed near the subject  40  may be changed by the movement of the corresponding object. 
     The detection method also comprises a step of performing a specific operation on the sensing signal of each sub region of the reference frame within a predetermined time-interval occurring after the reference frame is captured to obtain the feature (step S 92 ) and generating a feature signal related to the sub region according to the obtained feature (step S 93 ). In the embodiment, there are several frame periods within the predetermined time-interval. In the embodiment, the feature signal is a signal related to a vital sign of the subject (a human being  30  or  40 ), such as the heart rate, respiration rate, or blood-oxygen content. In the method, the specific operation will be described by using an example in which a feature signal related to the heart rate is obtained. In this case, the feature signal is referred to as a “vital sign signal”. Since the color of the skin of a specific subject changes when blood flows through it, the color information can serve as a vital-sign feature to estimate the heart rate of the subject. In this case, for each sub region, the specific operation is performed on the corresponding sensing signal to calculate the average value of at least one color component (such as the R, G, and/or B component) of the pixels, involved in the corresponding sensing signal, within the predetermined time-interval. The calculated average value serves as the vital-sign feature of the sub region. According to the calculated average value, the feature signal related to the heart rate is estimated. 
     In the embodiment, the feature signals of all the sub regions of the reference frame are estimated. However, in other embodiments, the feature signals of some of the sub regions of the reference frame are estimated. For example, the quality of the sensing signals from the sub regions with over-exposure or under-exposure in the reference frame is usually lower due to the subtle changes of vital signs may be lost, and, thus, these sub regions with over-exposure or under-exposure may be invalid for the ROI detection. Thus, the feature signals of the sub regions excluding the sub region with over-exposure or under-exposure are estimated. 
     The detection method further comprises a step of, for each estimated feature signal, determining whether the feature signal is a valid image signal (step S 94 ). In an embodiment, the quality of the feature signal is assessed, and whether the feature signal is a valid image signal is determined according to the assessed quality. In one case, whether the image difference in the feature signal of one sub region between two adjacent frame periods occurring within the predetermined time-interval is larger than a predetermined threshold is determined (referred to as “pixel domain manner” for quality assessment). When the image difference is not larger than the predetermined threshold, which means that the subject may not move or move by slight shifting in the two adjacent frame periods shifting, it is determined that the quality of the feature signal is high, and the feature signal is determined as a valid image signal; when the image difference is larger than the predetermined threshold, which means that the subject may move by greater shift between the two adjacent frame periods shifting, it is determined that the quality of the feature signal is low, and the feature signal is not determined as a valid image signal. In another case, whether there is a relative high peak occurring in the feature signal of one sub region within the predetermined time-interval is determined (referred to as “signal domain manner” for the quality assessment). When there is no relative high peak, which means that the subject may have stable feature signal or may not move or move by slight shifting in these several successive frame periods, it is determined that the quality of the feature signal is high, and the feature signal is determined as a valid image signal; when there is a relative high peak, which means that the subject may have stable feature signal or may move by greater shifting in these several successive frame periods, it is determined that the quality of the feature signal is low, and the feature signal is not determined as a valid image signal. In another case, the feature signal may be converted from time domain to frequency domain via some techniques, such as fast Fourier transform (FFT). Whether the spectrum energy is concentrated within a small nearby frequency range of one spectrum frequency is determined (referred to as “frequency domain manner” for quality assessment). When the concentration of the spectrum energy is larger than the predetermined threshold, it is determined that the quality of the feature signal is high, and the feature signal is determined as a valid image signal; when the concentration of the spectrum energy is not larger than the predetermined threshold, it is determined that the quality of the feature signal is low, and the feature signal is not determined to as a valid image signal. The quality of each feature signal may be assessed by at least one of the pixel domain, signal domain, and frequency domain manners. In the embodiment, for one sub region, at least one of the manners defined above is performed after the obtainment of the feature signal is completed. In another embodiment, for one sub region, at least one of the manners defined above is performed in several frame periods within the predetermined time-interval, wherein the total length of the several frame periods is shorter than the predetermined time-interval. That is, the quality of the feature can be pre-assessed before the estimation of the feature signal is completed. 
     In another embodiment, for each estimated feature signal, the feature signal is compared with a predetermined reference signal which is obtained in advance or has previously been stored in a memory and determines whether the feature signal is a valid image signal according to the comparison result (feature comparison). When the difference between the feature signal and the predetermined reference signal is within a reference range, the feature signal is determined as a valid image signal; when the difference between the feature signal and the predetermined reference signal is outside the reference range, the first feature signal is not determined as a valid image signal. 
     In further another embodiment, for each estimated feature signal, whether the feature signal is a valid image signal is determined by performing object detection (such as skin detection, face detection, or chest detection) on the sub regions, or by using the position information of the subject. In some embodiments, whether the feature signal is a valid image signal may be determined by performing at least two of the quality assessment, the feature comparison, and object detection. 
     The detection method also comprises a step of when at least one feature signal is determined as a valid image signal, identifying the at least one corresponding sub region as an ROI (step S 95 ). When only one feature signal is determined as a valid image signal, the corresponding sub region is directly identified as an ROI. When several feature signals are determined to be valid image signals, the corresponding sub regions which are adjacent to each other are merged to form a merged region, and the merged region is identified as an ROI. If there is still a sub region separated from the merged region, the sub region is identified as another ROI. When the ROI(s) is identified, the ROI can be shown on a display, such as a liquid crystal display or a touch panel. 
     The detection method further comprises a step of tracking the obtained ROI in the frames occurring after the reference. According to the above embodiment, through the ROI obtainments and ROI tracking, the detection method may be performed by an electronic device, such as the portable electronic device  100  shown in  FIG. 1  for vital sign measurement, such as heart rate, respiration, blood-oxygen content of the subject, face recognition, or camera auto-focus. 
     In an embodiment, when the detection method is performed by an electronic device equipped with an image sensor or connecting to an image sensor, such as the portable electronic device  100  shown in  FIG. 1 , the detection method further comprises a step of capturing a plurality of successive frames by the image sensor, such as the camera  150  shown in  FIG. 1 . 
     While the invention has been described by way of example and in terms of the preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments. On the contrary, it is intended to cover various modifications and similar arrangements (as would be apparent to those skilled in the art). Therefore, the scope of the appended claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements.