Patent Publication Number: US-8111925-B2

Title: System and method for identifying object in image

Description:
BACKGROUND 
     1. Field of the Invention 
     The present invention generally relates to a system and a method for identifying an object in an image. 
     2. Description of Related Art 
     Image capturing is widely used in industrial production, consumer electronics, and medical equipments. Take surface mounting technology (SMT) as an example, surface mount technology is a method for constructing electronic circuits in which surface mounted components (SMC) are mounted directly onto a surface of a printed circuit board (PCB). During the process of mounting the SMC onto the PCB, an image of the SMC is captured. 
     The image is analyzed to identify the SMC in the image and detect whether the SMC is correctly positioned. If it is detected that the SMC is misaligned, a nozzle for mounting the SMC can be adjusted according to the misalignment of the SMC to the PCB. 
     A conventional method for analyzing the image to identify an object (e.g., the SMC) in the image is by comparing the image of the object with a pre-captured standard image of the object, pixel by pixel. As a result of this comparison, properties of the object can be obtained, such as size, shape, offset, skew (i.e., rotated angle with respect to the object in the pre-captured standard image), etc. 
     However, when the file size of the image is large, it takes a relatively long time to complete the comparison. 
     Therefore, a need exists for a system and method for identifying the object in the image resolving the above problem in the industry. 
     SUMMARY 
     According to one aspect, a method for identifying an object in an image includes steps of determining a center of the object; calculating a radius for scanning the image; scanning along a scan circle defined by the center and the radius; and identifying the object according to scanned data of the image along the scan circle. 
     According to another aspect, a system for identifying an object in an image includes a center calculating module for calculating a center of the object, a radius calculating module for calculating a radius for scanning the image, and a comparing unit for comparing scanned data scanned along a scan circle defined by the center and the radius with predefined data to identify the object in the image. 
     Other systems, methods, features, and advantages of the present system and method for identifying an object in an image will be or become apparent to one with skill in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features, and advantages included within this description, be within the scope of the present device, and be protected by the accompanying claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Many aspects of the present system and method for identifying an object in an image can be better understood with reference to the following drawings. The components in the drawings are not necessarily drawn to scale, emphasis instead being placed upon clearly illustrating the principles of the present device. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views. 
         FIG. 1  shows an image and three scan lines for scanning the image. 
         FIG. 2  shows relationships between recorded values of pixels along a first scan line and horizontal locations of the pixels in the image. 
         FIG. 3  shows relationships between recorded values of pixels along a second scan line and horizontal locations of the pixels in the image. 
         FIG. 4  shows relationships between recorded values of pixels along a third scan line and horizontal locations of the pixels in the image. 
         FIG. 5  shows an image including a triangle object and a scan circle for scanning the image. 
         FIG. 6  shows an aggregate of the values of the pixels scanned along the circumference of the scan circle in  FIG. 5 . 
         FIG. 7  shows an image including a rectangle object and a scan circle for scanning the image. 
         FIG. 8  shows an aggregate of the values of the pixels scanned along the circumference of the scan circle in  FIG. 7 . 
         FIG. 9  shows an image including a rotated triangle object and a scan circle for scanning the image. 
         FIG. 10  shows an aggregate of the values of the pixels scanned along the circumference of the scan circle in  FIG. 9 . 
         FIG. 11  shows an image including a smaller triangle object and a scan circle for scanning the image. 
         FIG. 12  shows an aggregate of the values of the pixels scanned along the circumference of the scan circle in  FIG. 11 . 
         FIG. 13  shows an image including a polygon object and two scan circles for scanning the image. 
         FIG. 14  shows an aggregate of the values of the pixels scanned along the circumference of a first scan circle in  FIG. 13 . 
         FIG. 15  shows an aggregate of the values of the pixels scanned along the circumference of a second scan circle in  FIG. 13 . 
         FIG. 16  is a flow chart of a method for identifying an object in an image. 
         FIG. 17  is a block diagram of a system for identifying an object in an image in accordance with an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     Reference will now be made to the drawings to describe exemplary embodiments of a present system and method for identifying an object in an image, in detail. 
       FIG. 1  shows an image  10  including an object  12  and a background  14 . In order to simplify the description of the present embodiment, the image  10  is monochrome and the object  12  is a triangle. In other alternative embodiments, the object can be a rectangle, or other regular or irregular shapes. 
     First, a center  122  of the object  12  is computed. The image  10  is scanned line by line to record a value of each pixel in the image  10 . For simplicity and to better describe the present embodiment, the image is in monochrome, and a black pixel represents a pixel value of 1 and a white pixel represents a pixel value of 0. That is, a value of the pixel of the object  12  in black is 1, while a value of the pixel of the background  14  in white is 0.  FIG. 2  shows relationships between pixel values (Y-axis) along a scan line  31  and horizontal location (X-axis) of the pixels in the image  10 . Along the scan line  31 , pixels along both end sections of the scan line  31  (referring to  FIG. 1 ) are background  14  of the image  10  and are white pixels, thus the values of the pixels at end sections are 0. Pixels along the middle section of the scan line  31  are of the object  12  and are black pixels, and the values of the pixels at the middle section are 1. Similarly, the pixel values along scan lines  32 ,  33  are correspondingly illustrated in  FIGS. 3 ,  4 , so as to determine whether scanned pixels in the image  10  are part of the object  12 . 
     For example, when a resolution of the image  10  is 2048×2048 pixels, there are 2048 scan lines with each of the scan line scans 2048 pixels. That is, 2048 values of 2048 pixels will be correspondingly obtained in each of the scan lines. When scanning, each location of the dark pixels is recorded. The location of the dark pixels is represented with a Y-coordinate (Yi) and an X-coordinate (Xi) in the image  10 . The Y-coordinate is determined by the location of the scan line in the image, that is, pixels in a same scan line have same Y-coordinates. The X-coordinate is determined by the location of the pixel in the scan line. If the number of the dark pixels is N, a location of the center  122  can be calculated, wherein a Y-coordinate of the center  122  is ΣYi/N, and an X-coordinate of the center  122  is ΣXi/N. 
     Second, a radius of a scan circle is calculated.  FIG. 5  shows the scan circle  40  defined by the center  122  and the radius R. The radius R is calculated via the following formula:
 
 R=√{square root over (N/K)};  
 
wherein N represents the number of the pixels having the value of 1, K is a predetermined constant, for example, π, 3.5, or 0.9π.
 
     Third, after the center  122  and the radius R of the scan circle is calculated, the image  10  is scanned along the circumference of the scan circle. That is, the values of the pixels along the circumference of the scan circle are read. A number of the pixels scanned along the circumference can be determined according to a desired precision. For example, if the number of the pixels is 2048, an increment of a central angle θ to scan a next pixel along the circumference is 2π/2048. Therefore, each pixel that is going to be scanned can be decided, and the location thereof can be calculated according the center  122 , the radius R, and the central angle θ. 
     Referring also to  FIG. 6 , the values of the pixels along the circumference of the scan circle  40  are shown. A first scanned pixel (i.e., the central angle θ equals to 0 degree) is part of the background  14 , thus the value of the first pixel is 0. During the scan process, the central angle θ increases gradually (e.g., 2π/2048 per pixel). If the circumference of the scan circle  40  intersects on the object  12 , the value of the scanned pixel is 1. 
     Referring to  FIG. 7 , an image  70  includes an object  72  and a background  74 . Similarly, a center  722  and a number of pixels of the object  72  can be calculated. Accordingly, a scan circle  80  for scanning the image  70  is determined by the center  722  and the number of pixels of the object  72 .  FIG. 8  shows an aggregate of the values scanned along the circumference of the scan circle  80 . 
     Comparing  FIG. 6  and  FIG. 8 , it&#39;s clear that the two aggregates of the two images  10 ,  70  are distinct. Therefore, images of different objects can be scanned employing the above described method to identifying the objects in the images by comparing the aggregate of the scanned values (i.e., scanned data) of the image with predefined aggregate data of standard images. 
     Referring to  FIG. 9 , an image  90  includes an object  92  and a background  94 . Compared to  FIG. 6 , the objects  12 ,  92  have a same size and shape, but the object  92  is rotated with an angle around a center of the object  92 . Similarly, an aggregate of the values scanned employing the above described method is shown in  FIG. 10 . Comparing  FIG. 6  and  FIG. 10 , a graph of the aggregate in  FIG. 6  is similar to a graph of the aggregate in  FIG. 10 , but the graph of the aggregate in  FIG. 10  has a different phase. That is, if an object is rotated, its graph of scanned aggregate will be the same as before except having a shifted phase. 
     Referring to  FIG. 11 , an image  110  includes an object  112  and a background  114 . Comparing with  FIG. 6 , the objects  12 ,  112  have a same shape, but a size of the object  112  is smaller than that of the object  12 . Similarly, an aggregate of the values scanned, employing the above described method, is shown in  FIG. 12 . Comparing  FIG. 6  and  FIG. 12 , the shape of the graph of the aggregate in  FIG. 12  is substantially the same as the shape of the graph of the aggregate in  FIG. 6 . However, different sizes of the objects  12 ,  112  will lead to generate different number of pixels of the objects  12 ,  112 . Accordingly, the radii R of the different objects  12  and  112  when calculated from the formula R=√{square root over (N/K)} will be different. Therefore, different objects having different sizes can be identified by comparing the number of pixels or the radii R. 
     To sum up, the shape of an object in an image can be derived from the graph of the scanned values (i.e., aggregate of the scanned pixels). The rotated angle of the object can be calculated from the shifted phase of the graph. The size of the object can be determined by counting the number of pixels or calculating the radius R. If the object  12  in  FIG. 5  is moved, the center  122  of the object will be moved accordingly. However, the graph of the scanned values, the phase of the graph, and the radius R will not change. Thus, the movement of the object  12  won&#39;t affect the identification of the object  12  in the image  10 . In fact, the displacement of the object  12  can be calculated according to the location of the center  122 . 
     Furthermore, in order to more precisely identify the object, more than one scan circles can be employed to scan the image.  FIG. 13  shows an image  130  including an object  132  and a background  134 . Two scan circles  140 ,  150  for scanning the image  130  are determined by selecting two different constants K. The graphs obtained by the two scan circles  140 ,  150  are respectively shown in  FIG. 14  and  FIG. 15 . Although  FIG. 14  is similar to  FIG. 8 , it is still able to distinguish between the polygon object  132  and the rectangle object  72 , as it is readily understood that the graph illustrated in  FIG. 15  will be different from a graph obtained by another scan circle scanning the rectangle object  72 . 
     A brief description of the method for identifying an object in an image will be given with reference to  FIG. 16 . In step S 1602 , a center of the object is determined. The center of the object is determined by the coordinates of each pixel of the object. That is, an X-coordinate of the center is an average value of the X-coordinates of pixels of the object, and a Y-coordinate of the center is an average value of the Y-coordinates of pixels of the object. 
     In step S 1604 , a radius for scanning the image is calculated. As mentioned above, the radius R is calculated via the following formula: R=√{square root over (N/K)}; wherein N represents the number of pixels of the object, K is a predetermined constant, for example, π, 3.5, or 0.9π. 
     In step S 1606 , the image is scanned along a scan circle defined by the center and the radius. Each pixel that is going to be scanned can be determined according to the following formula:
 
 A=A 0+ dx+dy*W;  
 
wherein A represents the location of the pixel that is going to be scanned, A 0  represents the location of the center, dx represents a horizontal deviation from the center, dy represents a vertical deviation from the center, and W is a width of the image, for example, 2048 pixels. Generally, the image is linearly stored in a storage unit (e.g., random access memory, RAM) pixel by pixel according to addresses of the storage unit, other than planarly stored, thus one pixel increment along a vertical direction in the image will result that the address in the storage unit increases by the width of the image, rather than by one pixel.
 
     In step S 1608 , the object in the image is identified according to the scanned data of the image along the scan circle. The scanned data are compared with predefined data to identify the object. For example, if the scanned data is similar to those of a triangle, the shape of the object is identified as the triangle. The location of the center determines the location of the object. The number of pixels of the object determines the size of the object. The phase difference between the scanned data and predefined data determines the rotate angle of the object. 
     Referring to  FIG. 17 , a system  500  for identifying an object in an image is illustrated. The system  500  includes a center calculating module  510 , a radius calculating module  520 , an address module  530 , an image storage unit  540 , a scanned data storage unit  550 , a predefined data storage unit  560 , a comparing unit  570 , and a counter  580 . 
     The center calculating module  510  is configured for calculating a center of the object. The center of the object is determined by the coordinates of each pixel of the object. That is, an X-coordinate of the center is an average value of the X-coordinates of pixels of the object, and a Y-coordinate of the center is an average value of the Y-coordinates of pixels of the object. 
     The radius calculating module  520  is used for calculating a radius R of the scan circle for scanning the image. The radius R is calculated via following formula: R=√{square root over (N/K)}; wherein N represents the number of the pixels of the object, K is a predetermined constant, for example, π, 3.5, or 0.9π. 
     The address module  530  is coupled to the radius calculating module  520  and the center calculating module  510  for determining the address of pixels, which are going to be scanned along the scan circle, stored in the image storage unit  540 . The address module  530  includes a position calculate unit  532  and an address calculate unit  534 . 
     The position calculate unit  532  is connected to the counter  580 . The counter  580  is configured for generating binary numbers, wherein the length of the binary numbers is determined by the number of pixels that is going to be scanned. For example, if it is preset that the number of pixels that is going to be scanned along the scan circle is 2048, the length of the binary numbers is eleven bits, since 2 11 =2048. The position calculate unit  532  receives the binary numbers for calculating horizontal deviations (dx) and vertical deviations (dy) from the center of the pixels corresponding to the binary numbers. 
     The position calculate unit  532  includes a lookup table  5322 , a first position unit  5324 , a second position unit  5326 , and a mapping unit  5328 . The lookup table  5322  is coupled to the counter  580  for storing sine values and cosine values corresponding to the binary numbers. Because the calculation of a sine value or cosine value is complex and requires a period of time, thus storing the actual sine and cosine values will accelerate the identification of the object. In order to reduce the capacity of the lookup table  5322 , only sine and cosine values of the central angle corresponding to the first quadrant are stored in the lookup table. Thus the capacity of the lookup table  5322  can be reduce to ¼ of the capacity that stores four quadrants of the sine values and cosine values. Correspondingly, the higher two bits of the binary numeral can be used to represent the four quadrants respectively, and the lower nine bits of the binary numeral are used to correspond to sine values and cosine values in the first quadrant. 
     The first position unit  5324  is connected to the lookup table  5322  for receiving the cosine value and calculating the horizontal deviation (dx) in the first quadrant, wherein dx equals to radius R multiplied by the cosine value of the center angle. The second position unit  5326  is connected to the lookup table  5322  for receiving the sine value and calculating the vertical deviation (dy) in the first quadrant, wherein dy equals to radius R multiplies the sine value of the center angle. 
     The mapping unit  5328  is coupled to the first position unit  5324  and the second position unit  5326  for receiving the horizontal deviation (dx) and the vertical deviation (dy) in the first quadrant and generating the horizontal deviation (dx) and the vertical deviation (dy) in the four quadrants. The relation of mapping is shown in the following table: 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                 Higher two bits 
                   
                   
                   
               
               
                 of the binary number 
                 Quadrant 
                 dx 
                 dy 
               
               
                   
               
             
            
               
                 00 
                 First quadrant 
                 R * cosθ 
                 R * sinθ 
               
               
                 01 
                 Second quadrant 
                 −R * sinθ 
                 R * cosθ 
               
               
                 10 
                 Third quadrant 
                 −R * cosθ 
                 −R * sinθ 
               
               
                 11 
                 Fourth quadrant 
                 R * sinθ 
                 −R * cosθ 
               
               
                   
               
            
           
         
       
     
     The address calculate unit  534  is coupled to the mapping unit  5328  for calculating the address of the pixel that is going to be scanned along the circumference of the scan circle. The address of the pixel in the image storage unit  540  can be determined according to the following formula:
 
 A=A 0+ dx+dy*W;  
 
wherein A represents the address of the pixel that is going to be scanned, A 0  represents the address of the center in the image storage unit  540 , dx represents the horizontal deviation from the center, dy represents the vertical deviation from the center, and W is a width of the image.
 
     The image storage unit  540  is configured for storing image data of the image having the object that is going to be identified, for example, storing image data of the image  10  in  FIG. 5 . 
     The scanned data storage unit  550  is coupled to the image storage unit  540  and the counter  580  for receiving the scanned data and the binary number. The scanned data are values of pixels read from the image storage unit  540  according to the addresses of the pixels. The scanned data are matched with the binary number. 
     The predefined data storage unit  560  is used for storing predefined data of some objects, such as triangle, rectangle, polygon, etc. 
     The comparing unit  570  is connected to the scanned data storage unit  550  and the predefined data storage unit  560  for comparing the scanned data with the predefined data to identify the object in the image. For example, if the scanned data are illustrated as the graph in  FIG. 10 , and the predefined data include the aggregate illustrated as the graph in  FIG. 6 , the object in the image will be identified as a triangle with a rotated angle relative to the object  12  in  FIG. 5 . 
     The object in the image is identified by scanning the image along the circumference of the scan circle and comparing the scanned data with predefined data, thus comparing the image with a standard image pixel by pixel is avoid and the speed of identifying the object is expedite. 
     The foregoing description of the exemplary embodiments of the invention has been presented only for the purposes of illustration and description and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to explain the principles of the invention and their practical application so as to enable others skilled in the art to utilize the invention and various embodiments and with various modifications as are suited to the particular use contemplated. Alternative embodiments will become apparent to those skilled in the art to which the present invention pertains without departing from its spirit and scope. Accordingly, the scope of the present invention is defined by the appended claims rather than the foregoing description and the exemplary embodiments described therein.