Abstract:
A computer program product capable of enabling a computer to perform a digital image analyzing operation, wherein the digital image analyzing operation comprises: receiving settings of a plurality of lines corresponding to one or more image edges of a digital image; and identifying a plurality of intersections of the plurality of lines and the one or more image edges of the digital image.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of priority to Taiwanese Patent Application No. 099109318, filed on Mar. 29, 2010, the entirety of which is incorporated herein by reference for all purpose. 
       BACKGROUND 
       [0002]    The present disclosure generally relates to digital image analysis technology, and more particularly, to methods and computer program products related to digital image recognization or image measurement. 
         [0003]    Digital image recognization technology has been widely applied in various fields, such as product inspection, microscopic measurement, image object recognization, or the like, to determine image features (e.g., image edges or appearance) for particular portions of a digital image. 
         [0004]    However, in conventional digital image recognization methods, the processor of a computer has to compare and analyze all pixel values of the digital image, and therefore requires consuming considerable computing resource. Thus, the processing speed of conventional digital image recognization methods is restricted by the computing power of the computer and is difficult to be realized in apparatus with low computing resource. 
         [0005]    In addition, image features such as image texture, image shape, or the like may usually influence the conventional image recognization methods and thus deteriorate the accuracy of image recognization. 
         [0006]    In view of the foregoing, it can be appreciated that a substantial need exists for methods and apparatuses that can mitigate or reduce the problems in conventional digital image recognization technology. 
       SUMMARY 
       [0007]    An exemplary embodiment of a computer program product capable of enabling a computer to perform a digital image analysis operation is disclosed, wherein the digital image analysis operation comprises: receiving settings of a plurality of lines corresponding to one or more image edges of a digital image; and identifying a plurality of intersections of the plurality of lines and the one or more image edges of the digital image. 
         [0008]    An exemplary embodiment of a digital image analyzing method is disclosed comprising: receiving settings of a plurality of lines corresponding to one or more image edges of a digital image; and identifying a plurality of intersections of the plurality of lines and the one or more image edges of the digital image. 
         [0009]    It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0010]      FIG. 1  is a simplified functional block diagram of a digital image analyzing device in accordance with an exemplary embodiment. 
           [0011]      FIG. 2  is a flowchart illustrating a digital image recognization method according to an exemplary embodiment. 
           [0012]      FIG. 3  and  FIG. 4  are simplified schematic diagrams of a partial image of a circuit board sensed by the image capturing device of  FIG. 1 . 
           [0013]      FIG. 5  is a partial enlarged diagram of the digital image of  FIG. 4 . 
           [0014]      FIG. 6  is a simplified schematic diagram of a partial image of another circuit board sensed by the image capturing device of  FIG. 1 . 
           [0015]      FIG. 7  is a simplified schematic diagram of an image of an object having a concave shape sensed by the image capturing device of  FIG. 1 . 
           [0016]      FIG. 8  is a flowchart illustrating a digital image measuring method according to an exemplary embodiment. 
           [0017]      FIG. 9  is a schematic diagram of a cross-section view of a circular tube. 
       
    
    
     DETAILED DESCRIPTION 
       [0018]    Reference will now be made in detail to exemplary embodiments of the invention, which are illustrated in the accompanying drawings. The same reference numbers may be used throughout the drawings to refer to the same or like parts or operations. 
         [0019]    Certain terms are used throughout the description and following claims to refer to particular components. As one skilled in the art will appreciate, vendors may refer to a component by different names. This document does not intend to distinguish between components that differ in name but not in function. In the following description and in the claims, the terms “include” and “comprise” are used in an open-ended fashion, and thus should be interpreted to mean “include, but not limited to . . . .” 
         [0020]      FIG. 1  shows a simplified functional block diagram of a digital image analyzing device  100  in accordance with an exemplary embodiment. As shown in  FIG. 1 , the digital image analyzing device  100  comprises an image capturing device  110 , a host  120 , a display  130 , and an input interface  140 . The image capturing device  110 , the display  130 , and the input interface  140  are coupled with the host  120 . The phrase “coupled with” as used herein is intended to compass any indirect or direct connection. In other words, each of the image capturing device  110 , the display  130 , and the input interface  140  may be directly connected to the host  120  (including through an electrical connection or other signal connections, such as wireless communications or optical communications), or may be indirectly connected to the host  120  through an indirect electrical connection or signal connection via other intermediate device or connection means. 
         [0021]    The image capturing device  110  is used for sensing images of a target object  102  positioned on a supporting device  150 . In implementations, the image capturing device  110  may be a digital camera, digital video recorder, digital microscope, and any other apparatus with image sensing capability. In this embodiment, the host  120  comprises a processor  122  and a storage module  124 . The storage module  124  may be implemented with one or more storage media. 
         [0022]    The digital image analyzing device  100  may be used for conducting digital image recognization or digital image measuring operations. The operations of the digital image analyzing device  100  will be described in further detail with reference to  FIG. 2 . 
         [0023]      FIG. 2  is a flowchart  200  illustrating a digital image recognization method according to an exemplary embodiment. 
         [0024]    In an operation  210 , the processor  122  of the host  120  displays the image of the target object  102  sensed by the image capturing device  110  on the display  130 . For example, a digital image  300  shown in  FIG. 3  is a partial image of a circuit board sensed by the image capturing device  110 . Reference number  302  denotes an opening on the circuit board, and reference numbers  304  and  306  denote two sides of the circuit board. 
         [0025]    Recognization of image edges is very important for digital image recognization operations. The shape of the image may be recognized once the image edges are identified. In order to reduce required computing resource for digital image recognization operations and increase the recognization speed and accuracy, the digital image analyzing device  100  interacts with the user when conducting digital image recognization operations, and requests the user to provide auxiliary information for determining the edges and/or shape of the digital image. The recognization operations for the opening  302  of the circuit board will be used as an example in the following descriptions. 
         [0026]    In an operation  220 , the processor  122  displays a plurality of recognization pattern options in the form of characters or pictures on the display  130  for the user to select. For example, as shown in  FIG. 3 , the processor  122  may group multiple recognization pattern options in the form of character buttons as an item bar  310  and show on the display  130 . Each recognization pattern option has a corresponding image edge computing algorithm that can be implemented with computer program codes and stored in the storage module  124 . The type and amount of the recognization pattern options descried previously is merely an example, not intended to restrict the practical implementations. In practice, the type and amount of the recognization pattern options may be increased, decreased, or changed based on the design requirement. 
         [0027]    In an operation  230 , the user is required to select one or more recognization patterns related to the shape of the digital image  300  out of the plurality of recognization pattern options shown in the item bar  310 . In operations, the user may select one or more recognization pattern options through the input interface  140  by using the cursor selection, touch control, voice control, or other commanding approaches. In this embodiment, the user may select the recognization pattern option “Round” as it is most approximate to shape of the opening  302  of the circuit board. 
         [0028]    In an operation  240 , the host  120  receives settings of the recognization pattern selected by the user from the input interface  140  and stores the settings in the storage module  124  as reference basis of image recognization operations to be performed by the processor  122 . 
         [0029]    In an operation  250 , the user is required to set a plurality of lines on the digital image  300 . The user may roughly draw a plurality of lines corresponding to the image edge of the opening  302  to be recognized on the digital image  300  through the input interface  140  by using the mouse drag approach or touch control approach. The quantity of lines needed to be set in the operation  250  depends upon the recognization pattern selected by the user. For example, if the selected recognization pattern is “Round” or “Arc,” the user is required to input at least three lines in the operation  250 . If the selected recognization pattern is “Straight line,” the user is required to input at least two lines in the operation  250 . If the selected recognization pattern is “Polygon” or “Irregular,” more lines are required in the operation  250 . 
         [0030]    In one embodiment, each line is only required to intersect an image edge of the image object to be recognized, i.e., the two terminals of each line should be on the opposing sides of the image edge. There is no restriction on the length of each line. Additionally, each line may be a curve or irregular line, and not restricted in a straight line. In order to reduce the computing complexity, it is preferred that each line only intersects one image edge. In implementations, the display  130  and the input interface  140  may be integrated together. For example, a touch screen may be utilized to implement the functions of both the display  130  and the input interface  140 . 
         [0031]    In one embodiment shown in  FIG. 4 , the user roughly draws three lines  410 ,  420 , and  430  on the digital image  300 , and each of which intersects the image edge of the opening  302 . 
         [0032]    In an operation  260 , the host  120  receives the settings of multiple lines inputted by the user via the input interface  140 , and stores the line settings in the storage module  124  as reference basis of image recognization operations to be performed by the processor  122 . 
         [0033]    In an operation  270 , the processor  122  reads the settings of the plurality of lines stored in the storage module  124 , and identifies a plurality of intersections of the plurality of lines and one or more image edges of the opening  302  in the digital image  300 . For example, the processor  122  of this embodiment calculates and obtains an intersection  412  of the line  410  and the image edge of the opening  302 , an intersection  422  of the line  420  and the image edge of the opening  302 , and an intersection  432  of the line  430  and the image edge of the opening  302 . 
         [0034]    The processor  122  may calculate the intersection of each line and the image edge of the opening  302  in various ways. In one embodiment, the processor  122  may compute pixel value difference between adjacent pixels on the path of each line, and set a position with the maximum pixel value difference as the intersection of the line and the image edge of the opening  302 . This intersection calculating approach may be applied in the applications where there is an obvious pixel value difference between pixels on the opposing sides of the image edge. 
         [0035]    Please note that the term “pixel value” as used throughout the specification and following claims may be luminance or chrominance of the pixel. 
         [0036]    In another embodiment, the processor  122  may compute pixel value difference between adjacent pixels on the path of each line along a predetermined direction, and set the first position where the pixel value difference reaches or exceeds a predetermined threshold as the intersection of the line and the image edge of the opening  302 . This intersection calculating approach may be applied in the applications where there is no obvious pixel value difference between pixels on the opposing sides of the image edge, but the pixel value of pixels on the image edge is apparently different from that of pixels on both sides of the image edge. For illustrative purpose, a partial enlarged diagram of the digital image  300  shown in  FIG. 5  will be taken as an example in the following. 
         [0037]    In  FIG. 5 , pixels  510 ,  512 ,  514 ,  516 ,  518 ,  520 ,  522 ,  524 ,  526 , and  528  are part of pixels on the image edge of the opening  302 . Pixels  530 ,  532 ,  518 ,  516 ,  534 ,  536 ,  538 , and  540  are part of pixels on the path of the line  410 . Reference numbers  502  and  504  denote the two terminals of the line  410 . The user may fine tune the intersection of the line  410  and the image edge of the opening  302  by moving one terminal of the line  410 , such as the terminal  504 , clockwise or counterclockwise. The method for setting an intersection of a line and an image edge described above offers higher precision level for the intersection setting compared to the conventional way where the intersection on the image edge is directly set by the user using the mouse click or touch control approaches. 
         [0038]    The processor  122  may compute pixel value difference between adjacent pixels on the path of the line  410  along a predetermined direction D 1 . For example, the processor  122  may first compute the pixel value difference between the pixels  530  and  532 , then the pixel value difference between the pixels  532  and  518 , and then the pixel value difference between the pixels  518  and  516 , and so forth. The processor  122  compares each obtained pixel value difference with the predetermined threshold. In this embodiment, the pixel value difference between the pixels  532  and  518  is the first pixel value difference that exceeds the predetermined threshold. Accordingly, the processor  122  may omit the subsequent pixel value difference calculations and set an intersection  552  of the pixels  532  and  518  as the intersection  412  of the line  410  and the image edge of the opening  302 . 
         [0039]    In another embodiment, the processor  122  may first compute a median or mean value of a plurality of pixel values (e.g., all of the pixel values) on the path of the line  410  and set the median or mean value as a threshold. That is, the setting of the threshold may be dynamically adjusted. Then, the processor  122  compares the pixel value of pixels on the path of the line  410  in turn with the obtained threshold along the direction D 1 , and set the first position where the pixel value reaches or is across the threshold as the intersection  412  of the line  410  and the image edge of the opening  302 . Assuming the pixel  518  on the path of the line  410  is the first pixel that has a pixel value reaching or equal to the threshold along the direction D 1 , the processor  122  may omit subsequent pixel value comparisons and set a center position  554  of the pixel  518  as the intersection  412  of the line  410  and the image edge of the opening  302 . 
         [0040]    Assuming the pixel  518  on the path of the line  410  is the first pixel whose pixel value is across the threshold along the direction D 1 , i.e., the first pixel whose pixel value is either greater than or less than the threshold, the processor  122  may omit subsequent pixel value comparisons and set the intersection  552  of the pixels  518  and  532  as the intersection  412  of the line  410  and the image edge of the opening  302 . In another embodiment, the processor  122  may perform an interpolation calculation on the pixel values of the pixels  518  and  532  to obtain a position  556  corresponding to the threshold, and set the position  556  as the intersection  412  of the line  410  and the image edge of the opening  302 . 
         [0041]    It can be appreciated from the above descriptions that the precision of intersection of each line and the image edge of an image object identified by the processor  122  could be less than a pixel. 
         [0042]    In an operation  280 , the processor  122  connects the plurality of intersections obtained in the operation  270  according to the recognization pattern selected in the operation  230  to recognize one or more edge portions or whole boundary of the digital image  300 . For example, in this embodiment, the recognization pattern selected by the user in the operation  230  is “Round,” the processor  122  thus determines a circumference based on the three intersections  412 ,  422 , and  432  identified in the operation  270  as the image edge recognization result of the opening  302 . 
         [0043]    After recognized the boundary of an image object, such as the circumference of the opening  302 , the processor  122  may further calculate a location of a geometrical feature of the image object, such as a center  408  of the opening  302 . 
         [0044]    The recognization operations for the side  304  of the circuit board will be used as another example below to describe the digital image recognization method of exemplary embodiments. 
         [0045]    Since the side  304  of the circuit board is a straight edge, the user may select the recognization pattern option “Straight line” in the operation  230  as it is most approximate to shape of the side  304  of the circuit board, and set two lines  440  and  450  that intersect the image edge of the side  304  of the circuit board in the operation  250 . 
         [0046]    In the operation  270 , the processor  122  may utilize one of aforementioned methods to identify an intersection  442  of the line  440  and the image edge of the side  304  of the circuit board, and an intersection  452  of the line  450  and the image edge of the side  304  of the circuit board. 
         [0047]    In the operation  280 , since the recognization pattern setting stored in the storage module  124  is “Straight line,” the processor  122  determines an edge line based on the intersections  442  and  452  as the image edge recognization result for the side  304  of the circuit board. 
         [0048]    In another embodiment, the user selects the recognization pattern option “Broken line” which is most similar to the shape of the two sides  304  and  306  of the circuit board in the operation  230 . Then, the user in the operation  250  sets two lines  440  and  450  that intersect the image edge of the side  304  of the circuit board, and sets two lines  460  and  470  that intersect the image edge of the side  306  of the circuit board. In the operation  270 , the processor  122  determines an intersection  442  of the line  440  and the image edge of the side  304  of the circuit board, an intersection  452  of the line  450  and the image edge of the side  304  of the circuit board, an intersection  462  of the line  460  and the image edge of the side  306  of the circuit board, and an intersection  472  of the line  470  and the image edge of the side  306  of the circuit board by utilizing above approaches. 
         [0049]    Then, the processor  122  in the operation  280  connects the intersections  442 ,  452 ,  462 , and  472  to form an L-shaped edge line as the image edge recognization result for both the sides  304  and  306  of the circuit board based on the indication of the recognization pattern “Broken line”. 
         [0050]    Once the positions of the center  408  of the opening  302  and the sides  304  and  306  of the circuit board are obtained, the processor  122  is able to calculate a distance between the center  408  and the side  304  or the side  306  of the circuit board. 
         [0051]    According to the foregoing illustrations, it can be appreciated that the digital image recognization methods described above are able to calculate and obtain an intersection of a line and a specific image edge by simply analyzing the pixel values on the path of the line, and then accomplishes the image edge recognization purpose. Since there is no need to analyze all the pixels of the digital image  300 , the computing resource required by the processor  122  can be greatly reduced. In addition, since the processor  122  performs the image recognization operations based on the recognization patterns selected by the user, the digital image recognization accuracy for the image edges or shape can be significantly improved. 
         [0052]    In applications, the digital image analyzing device  100  may be utilized to implement product inspection or quality control purposes. For example, the supporting device  150  may be implemented by a conveyor for periodically transporting target objects (such as the aforementioned circuit board) to be inspected to an inspection area aimed by the image capturing device  110 . Target objects to be inspected usually have similar image features. Thus, the user only needs to input appropriate auxiliary information (such as image recognization patterns, settings of lines corresponding to image edges of the image object, or the like) to the host  120  at the first time the image capturing device  110  conducting image capturing and recognization operations, the image capturing device  110  would automatically perform the image recognization operations for subsequent target objects based on the same auxiliary information so as to realize yield rate determination or defect detection. Further details will be described in the following with reference to an example shown in  FIG. 6 . 
         [0053]    In  FIG. 6 , a digital image  600  is a partial image of another circuit board sensed by the image capturing device  110 . Reference number  602  denotes an opening on the circuit board, and reference numbers  604  and  606  denote two sides of the circuit board. In general, difference between two products made by the same production process is usually within a limited range. Thus, the digital image analyzing device  100  may maintain the settings of the auxiliary lines  410 ,  420 ,  430 ,  440 ,  450 ,  460 , and  470  and the recognization patterns “Round” and “Broken line” (or “Straight line”) selected by the user during the recognization operations for the digital image  300 . The processor  122  may utilize one of the aforementioned digital image recognization methods to automatically identify an intersection  612  of the line  410  and the image edge of the opening  602 , an intersection  622  of the line  420  and the image edge of the opening  602 , and an intersection  632  of the line  430  and the image edge of the opening  602 . The processor  122  then utilizes the three intersections  612 ,  622 , and  632  to determine a circumference of the opening  602  based on the recognization pattern “Round,” and calculates a center  608  of the opening  602 . 
         [0054]    Similarly, the processor  122  may utilize one of the aforementioned digital image recognization methods to identify an intersection  642  of the line  440  and the image edge of the side  604  of the circuit board, an intersection  652  of the line  450  and the image edge of the side  604  of the circuit board, an intersection  662  of the line  460  and the image edge of the side  606  of the circuit board, and an intersection  672  of the line  470  and the image edge of the side  606  of the circuit board. In the operations  280 , the processor  122  connects the intersections  642 ,  652 ,  662 , and  672  to form an L-shaped edge line as the image edge recognization result for both the sides  604  and  606  of the circuit board based on the setting of the recognization pattern “Broken line”. 
         [0055]    Afterward, the processor  122  may further calculate a distance between the center  608  of the opening  602  and the side  604  and/or the side  606  of the circuit board to determine whether the location of the opening  602  complies with a predetermined specification requirement, and then accordingly determine whether the circuit board is a qualified product. 
         [0056]    According to the foregoing descriptions, it can be appreciated that when applied in product inspections, the digital image recognization methods disclosed previously are able to complete the inspection of subsequent products in a nearly automatic way and only require minimum input from the user, such as setting the image recognization patterns and some simple lines. Furthermore, since the computing resource required for the above image recognization and product inspection operations is much lower than the conventional approaches, the efficiency of the quality control system can be improved, and related hardware cost can be further reduced. 
         [0057]    In addition to product inspection applications, the digital image analyzing device  100  can also apply the foregoing digital image recognization methods in object positioning and alignment applications. For example, when the processor  122  of the host  120  identified a relative location and distance between the target object  102  and one or more particular locations (e.g., positioning line, positioning edge, position corner, positioning point, or the like) on the supporting device  150 , the processor  122  may control the supporting device  150  to more or rotate the target object  102  to a predetermined location, thereby realizing object positioning or alignment. 
         [0058]    The above digital image recognization methods can also be employed to recognize partial edges of whole boundary of a digital image having a concave shape. For example, for a concave digital image  700  shown in  FIG. 7 , the user may select the recognization pattern option “Polygon” in the operation  230  as it is most approximate to the shape of the main body of the digital image  700 , and set a plurality of lines  720 ,  730 ,  740 ,  750 ,  760 ,  770 ,  780 , and  790  that intersect the image edges of the digital image  700  in the operation  250 . By utilizing the aforementioned intersection calculations, the processor  122  calculates and obtains intersections  722 ,  732 ,  742 ,  752 ,  762 ,  772 ,  782 , and  792  of the lines and the image edges of the digital image  700  based on the above settings in the operation  270 . 
         [0059]    In addition, the user may repeat the aforementioned digital image recognization methods, but select the recognization pattern option “Arc” and set lines  702 ,  704 , and  706  that intersect an arc  710  of the digital image  700 . As a result, the processor  122  recognizes intersections  712 ,  714 , and  716  of the arc  710  and the lines  702 ,  704 , and  706 , respectively. 
         [0060]    Once the intersections  712 ,  714 ,  716 ,  722 ,  732 ,  742 ,  752 ,  762 ,  772 ,  782 , and  792  are obtained, the processor  122  performs the operation  280  based on the recognization pattern settings “Polygon” and “Arc” to determine multiple image edge lines of the digital image  700 . Then, the processor  122  could rapidly recognize the whole boundary and shape of the digital image  700  by connecting the recognized edge lines. 
         [0061]    For a digital image with an irregular shape, the user may select the recognization pattern option “Irregular” in the operation  230  and set a number of lines that intersect the image edges of the irregular digital image in the operation  250 . In general, the processor  122  can achieve higher recognization accuracy for the shape of the digital image as the number of lines set by the user increases. 
         [0062]    Please note that the executing order for the operations in the flowchart  200  described above is merely an example, not intended to restrict the practical implementations of the present invention. For example, the operations  230  and  240  can be adjusted to behind the operations  250  and  260 . Additionally, the digital image analyzing device  100  may be designed to only recognize digital images of target objects having the same shape. In such applications, the operations  220 ,  230 , and  240  may be omitted. 
         [0063]    The digital image analyzing device  100  may further apply the aforementioned digital image recognization methods in digital image measurements. For example,  FIG. 8  shows a flowchart  800  illustrating a digital image measuring method according to an exemplary embodiment. The operations  210  through  280  of the flowchart  800  are similar to the operations labeled the same in the flowchart  200 . For the sake of brevity, similar descriptions will not repeat here. 
         [0064]    As shown in  FIG. 8 , after identifying a plurality of intersections on the digital image in the operation  270 , the processor  122  may further perform an operation  870  to calculate an interval between two intersections or a path length of the plurality of intersections according to a zooming scale of the digital image, and converts the length value from pixel distance into real length value. 
         [0065]    In addition, after recognizing partial edge portions or whole boundary of the digital image in the operation  280 , the processor  122  may further perform an operation  880  to calculate a specific image feature value of the digital image according to the edge recognization result. Further details will be described in the following with reference to an example shown in  FIG. 9 . 
         [0066]      FIG. 9  shows a digital image  900  which is a cross-section view of a circular tube. If the target to be measured is an image feature value related to the interior fringe of the digital image  900 , such as the internal diameter or interior cross sectional area of the digital image  900 , the user may select the recognization pattern option “Round” that is most approximate to the shape of the interior fringe of the digital image  900  in the operation  230 , and set a plurality of lines intersecting the interior fringe of the digital image  900  along a predetermined direction in the operation  250 . For example, in the embodiment shown in  FIG. 9 , lines  910 ,  920 , and  930  are set outward from the inside to the outside of the interior of the digital image  900 . 
         [0067]    Afterward, the processor  122  in the operation  270  only needs to analyze pixel values on the path of each of the lines  910 ,  920 , and  930  along their respective drawing direction to identify an intersection  912  of the line  910  and the interior image edge of the digital image  900 , an intersection  922  of the line  920  and the interior image edge, and an intersection  932  of the line  930  and the interior image edge by using the aforementioned methods. That is, the pixel value analysis for the path of the line  910  is performed along a direction D 1 , the pixel value analysis for the path of the line  920  is performed along a direction D 2 , and the pixel value analysis for the path of the line  930  is performed along a direction D 3 . 
         [0068]    In the operation  280 , the processor  122  determines a circumference corresponding to the recognization pattern “Round” as the recognization result of the interior image fringe of the digital image  900  based on the intersections  912 ,  922 , and  932 . 
         [0069]    When the circumference of the interior image edge of the digital image  900  is recognized, the processor  122  may perform the operation  880  to further calculate an image feature value related to the interior image fringe of the digital image  900  based on a zooming scale of the digital image  900 . For example, the processor  122  may calculate the internal diameter, radius, perimeter, cross sectional area, average color of the interior cross-section of the circular tube, or the like of the interior image fringe of the digital image  900 . 
         [0070]    Similarly, if the target to be measured is an image feature value related to the exterior image fringe of the digital image  900 , such as the external diameter or exterior cross sectional area of the digital image  900 , the user may set a plurality of lines intersecting the exterior fringe of the digital image  900  along a predetermined direction in the operation  250 . For example, in the embodiment shown in  FIG. 9 , lines  950 ,  960 , and  970  are drawn inward from the outside to the inside of the exterior of the digital image  900 . The processor  122  in the operation  270  analyzes pixel values on the path of the line  950  along a direction D 5  to identify an intersection  952  of the line  950  and the exterior image fringe of the digital image  900 , analyzes pixel values on the path of the line  960  along a direction D 6  to identify an intersection  962  of the line  960  and the exterior image fringe, and analyzes pixel values on the path of the line  970  along a direction D 7  to identify an intersection  972  of the line  970  and the exterior image fringe. 
         [0071]    Then, the processor  122  determines a circumference corresponding to the recognization pattern “Round” as the recognization result of the exterior image fringe of the digital image  900  based on the intersections  952 ,  962 , and  972  in the operation  280 . 
         [0072]    Then, the processor  122  may perform the operation  880  to calculate an image feature value related to the exterior image fringe of the digital image  900  based on a zooming scale of the digital image  900 . For example, the processor  122  may calculate the external diameter, radius, perimeter, cross sectional area, average color of the exterior cross-section of the circular tube, or the like of the exterior image fringe of the digital image  900 . 
         [0073]    By utilizing the aforementioned digital image measuring methods, the digital image analyzing device  100  could rapidly and correctly obtain a specific geometrical feature value related to the image object (such as a length of partial edge, a length of an arc, a radian, an included angle, a central angle, a circumferential angle, a tangential angle, and an image object&#39;s perimeter, area, radius, diameter, internal diameter, or external diameter) or a specific image feature value (such as the average color or luminance) with only minimum input from the user. 
         [0074]    In addition to recognizing the shape of a particular portion of a digital image, the processor  122  may employ adequate image recognization mechanism to further recognize and compute the location and/or quantity of specific image features (such as particle, crystal structure, or the like) contained in the particular image portion to provide more applications. 
         [0075]    As described above, the processor  122  is able to determine the intersection of each line and the image edge of an image object with a precision level less than one pixel. Therefore, when the aforementioned digital image recognization methods applied in digital image measurements, the accuracy of image measurement can be greatly improved. 
         [0076]    Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.