Abstract:
An apparatus, program product and method detect region boundaries, or edge, of an arbitrary number of pixels in width in a digital image that has coarse resolution, high noise levels and/or significant blur. A self-optimizing kernel generator detects edges of arbitrary thickness. In addition, combining results of multi-resolution edge detection provides significant noise tolerance, such as a 10 dB improvement over conventional techniques. Moreover, the edge detection preserves the precise location of a blurred edge and quantifies image clarity.

Description:
RIGHTS OF THE GOVERNMENT 
     The invention described herein may be manufactured and used by or for the Government of the United States for all governmental purposes without the payment of any royalty. 
    
    
     FIELD OF THE INVENTION 
     The invention is generally related to computers and computer software. More specifically, the invention is generally related to a manner of edge detection in image processing. 
     BACKGROUND OF THE INVENTION 
     Traditional edge detection methods have generally required a high enough signal to noise ratio and fine enough image clarity that the transition from one region to another does not significantly exceed a single pixel in width. Even edge detection methods that have been adapted to noisy images still appear to carry the assumption that the noise is superimposed on top of an image with edges that do not exceed a single pixel in width. 
     In essence, edge detection has traditionally been a form of high-pass filtering, although in noisy images it performs better when implemented as a band-pass filter process. Since speckle noise has a definite high frequency component, the best results will naturally come from a process that excludes as much noise energy from the detection process as possible. In certain images, such as highly magnified images, low light images, or pictures taken of a moving object or where the camera is moved, the displayed resolution creates a non-negligible spatial auto-correlation among the pixel amplitudes. Thus, edges become border regions with non-zero width. 
     Images that have blur or noise conventionally cannot receive the benefits of edge detection. For example, digital photography effects such as embossing or conversion to a line drawing are not readily available. Certain artificial intelligence applications rely upon interpreting a scene. In some application, these limitations are partially offset by having knowledge of the expected shape and edge thickness of objects within a digital image so that a tailor-made template may be used for detection. 
     Consequently, a significant need exists for a way to tune the spectral response of edge detection to accommodate only the bandwidth of a natural edge in a particular image, and reject as much of the high frequency noise energy as possible, yet not require beforehand knowledge of the characteristics of the image clarity. 
     SUMMARY OF THE INVENTION 
     The invention addresses these and other problems associated with the prior art by providing an apparatus, program product and method in which “Wilson” horizontal and vertical edge detection kernels are formed of various sizes, each size sensitive to detecting edge widths of various sizes. Repeating convolution of a digital image with various sizes of Wilson kernels achieves edge detection even when the size of the edge is not known in advance. 
     In one aspect of the invention, an image is subjected to a series of edge detection processes using kernels tailored to border regions of increasing width, until the natural edge width is found. In blurred images, such a process yields improving results with successive iterations until the natural edge width for that particular image is reached. Further increasing the width of the kernel does not yield significant improvement, but rather begins to cause a loss of features, so the process is then halted. There are at least two advantages to this approach. First, since the method is noise tolerant, edges may be found in images otherwise too noisy or coarse for traditional approaches. Second, a measure of the natural edge width quantifies the blur in the image and acts as a metric for clarity. 
     These and other advantages and features, which characterize the invention, are set forth in the claims annexed hereto and forming a further part hereof. However, for a better understanding of the invention, and of the advantages and objectives attained through its use, reference should be made to the Drawings, and to the accompanying descriptive matter, in which there is described exemplary embodiments of the invention. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 depicts a block diagram of an edge detection processor within an image-processing environment. 
     FIG. 2 is an illustrative computer system for performing edge detection processing in the image-processing environment of FIG.  1 . 
     FIG. 3 is a flowchart of a sequence of operations performed by an edge detection routine to detect an edge of arbitrary width in the presence of noise in a digital image. 
     FIG. 4 is a flowchart of a generate Wilson kernels routine referenced in the edge detection routine of FIG.  3 . 
     FIG. 5 is a flowchart of a clean-peak adaptive threshold routine referenced in the edge detection routine of FIG.  3 . 
     FIG. 6 is a flowchart of a median thresholding routine referenced in the edge detection routine of FIG.  3 . 
     FIG. 7 is a flowchart of an isolation cleaning routine referenced in the edge detection routine of FIG.  3 . 
     FIG. 8 is a flowchart of a sequence of operations, or routine, for self-optimizing edge detection, which references the edge detection routine of FIG.  3 . 
     FIG. 9 is a flowchart of a calculate self-optimization metric routine referenced in the self-optimizing edge detection routine of FIG.  8 . 
     FIG. 10 is an illustrative example of results from the self-optimization metric routine of FIG.  9 . 
     FIG. 11 is a digital image including coarse resolution, noise and blur. 
     FIGS. 11A-11I are illustrative output results from edge detection using Wilson kernels size N=0 to 8 respectively. 
    
    
     DETAILED DESCRIPTION 
     Detection of multi-pixel edge regions in an image is achieved by an adaptive approach, wherein edge detection kernels are selected to accommodate the various pixel widths of the edge. In addition, an edge detection operation is performed when the edge detection kernels of various sizes have been used on the image. The edge detection operation may include multiplying the results achieved by each kernel for increased noise reduction or comparing the results from each kernel to determine the optimum kernel size for the natural edge width. 
     Turning to the drawings, wherein like numbers represent similar items throughout the several figures, FIG. 1 illustrates an edge detection processor  10  consistent with aspects of the present invention as part of an image processing system  12 . An image acquisition device  14 , such as a digital camera, prepares a digital image that is made available to the edge detection processor  10  as a stored image  16 . The processor  10  may be incorporated as part of the image acquisition device  14  or be a separate device. The edge detection processor  10  processes the stored image  16  in a memory  18 . The edge detection result is provided to post-edge detection processing  20  and then presented on a display  22 . 
     FIG. 2 illustrates in another way an exemplary hardware and software environment for an apparatus  58  consistent with the invention. For the purposes of the invention, apparatus  58  may represent practically any type of computer, computer system, or other programmable electronic device, including a computer (e.g., similar to computers  12 - 16  of FIG.  1 ), a server computer, a portable computer, a handheld computer, an embedded controller, etc. Apparatus  58  may be coupled in a network as shown in FIG. 1, or may be a stand-alone device in the alternative. Apparatus  58  will hereinafter also be referred to as a “computer”, although it should be appreciated that the term “apparatus” may also include other suitable programmable electronic devices consistent with the invention. 
     Computer  58  typically includes at least one processor  60 , depicted as a CPU, coupled to a system memory  62 . A system bus  64  couples various system components, including system memory  62 , to CPU  60 . System bus  64  may be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of architectures. Processor  62  may represent one or more processors (e.g., microprocessors), and memory  62  may represent read-only memory (ROM)  64  and random access memory (RAM)  66  comprising the main storage of computer  58 , as well as any supplemental levels of memory, for example, cache memories, non-volatile, or backup memories (e.g., programmable or flash memories), read-only memories, etc. A basic input/output system (BIOS)  68 , containing the basic routines that help to transfer information between elements within computer  58 , such as during start-up, is stored in ROM  64 . In addition, memory  62  may be considered to include memory storage physically located elsewhere in computer  58 , (e.g., any cache memory in a processor  60 ), as well as any storage capacity used as a virtual memory, for example, as stored on a mass storage device or on another remote computer. Computer  58  has mass storage devices including a (typically fixed) magnetic hard disk  72 , a removable “floppy” or other magnetic disk  74 , and a CD-ROM, or other optical media  76 . The computer  58  may further include other types of mass storage such as direct access storage device (DASD), tape drive, etc. A hard disk drive  78  for hard disk  72  is connected to the system bus  64  via a hard disk drive interface  80 . A floppy disk drive  82  for floppy disk  74  connects to the system bus  64  via a floppy disk drive interface  84 . A CD-ROM drive  86  for CD-ROM  76  connects to the system bus  64  via a CD-ROM interface  88 . 
     A number of program modules are stored on mass storage media and/or ROM  64  and/or RAM  66  of system memory  62 . Such program modules may include an operating system  90 , providing graphics and sound application program interfaces (API), one or more application programs  92 - 96 , other program modules, and program data. 
     In general, the routines executed to implement the embodiments of the invention, whether implemented as part of an operating system or a specific application, component, program, object, module, or sequence of instructions, will be referred to herein as “computer programs,” or simply “programs.” The computer programs typically comprise one or more instructions that are resident at various times in various memory and storage devices in the computer, and that, when read and executed by one or more processors in the computer, cause that computer to perform the steps necessary to execute steps or elements embodying the various aspects of the invention. Moreover, while the invention has and hereinafter will be described in the context of fully functioning computers and computer systems, those skilled in the art will appreciate that the various embodiments of the invention are capable of being distributed as a program product in a variety of forms and that the invention applies equally, regardless of the particular type of signal bearing media used to actually carry out the distribution. Examples of signal bearing media include, but are not limited to, recordable type media such as volatile and non-volatile memory devices, floppy and other removable disks, hard disk drives, magnetic tape, optical disks (e.g., CD-ROMs, DVDs, etc.), among others, and transmission type media such as digital and analog communication links. 
     In addition, various programs described hereinafter may be identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature that follows is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     A user may enter commands and information into the computer  58  through input devices such as a keyboard  98  and a pointing device  100 . Other input devices may include a microphone joystick, game controller, satellite dish, scanner, or the like. These and other input devices are often connected to processing unit  60  through a serial port interface  102  that is coupled to system bus  64 , but may be connected by other interfaces, such as a parallel port interface or a universal serial bus (USB). A monitor  104  or other type of display device is also connected to system bus  64  via an interface, such as a video adapter  106 . 
     Computer  58  may also include a modem  108  or other means for establishing communications over wide area network (WAN)  110 , such as communication network  12 . Modem  108 , which may be internal or external, is connected to system bus  64  via serial port interface  102 . A network interface  112  may also be provided for allowing computer  58  to communicate with a remote computer  114  via local area network (LAN)  116  (or such communication may be via wide area network  110  or other communications pat such as dial-up or other communications means). Computer  58  typically includes other peripheral output devices, such as printers and other standard devices. 
     Those skilled in the art will recognize that the exemplary environments illustrated in FIGS. 1 and 2 are not intended to limit the present invention. Indeed, those skilled in the art will recognize that other alternative hardware and/or software environments may be used without departing from the scope of the invention. 
     FIG. 3 illustrates a sequence of operations for edge detection, depicted as routine  130 . The size of an edge detection kernel is set to a minimum size N MIN  (e.g., 0) without necessarily having a beforehand knowledge of the natural edge size of the digital image or the noise inherent in the image (block  132 ). A particularly useful type of edge detection kernel, which will be discussed in greater detail below with regard to FIG. 4, is illustrated in Tables 1-4. Specifically, two Wilson kernel classes are introduced, F HNT  and F VNT , wherein ‘H’ denotes horizontal, ‘V’ denotes vertical, ‘N’ denotes the size, and ‘T’ refers to the taper of the coefficients of the kernel, which will be discussed below. 
     
       
         
               
             
               
               
               
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Wilson Kernels F H01 , F V01   
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 −1 
                 1 
                   
                 −1 
                 −1 
                   
               
               
                   
                 −1 
                 1 
                   
                 1 
                 1 
               
               
                   
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                 Wilson Kernels F H11 , F V11   
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 −1 
                 0 
                 0 
                 0 
                 1 
                   
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                   
               
               
                   
                 −1 
                 −1 
                 0 
                 1 
                 1 
                   
                 0 
                 −1 
                 −1 
                 −1 
                 0 
               
               
                   
                 −1 
                 −1 
                 0 
                 1 
                 1 
                   
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 0 
                 1 
                 1 
                   
                 0 
                 1 
                 1 
                 1 
                 0 
               
               
                   
                 −1 
                 0 
                 0 
                 0 
                 1 
                   
                 1 
                 1 
                 1 
                 1 
                 1 
               
               
                   
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                 Wilson Kernels F H21 , F V21   
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 −1 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 1 
                   
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                   
               
               
                   
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 0 
                 1 
                 1 
                   
                 0 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 1 
                 1 
                 1 
                 1 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 0 
                 1 
                 1 
                   
                 0 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 0 
               
               
                   
                 −1 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 1 
                   
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
               
               
                   
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                 Wilson Kernels F H31 , F V31   
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 −1 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 1 
                   
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                   
               
               
                   
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 1 
                 1 
                   
                 0 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 0 
                 0 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 1 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 0 
                 −1 
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 1 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 1 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 1 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 1 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 0 
                 1 
                 1 
                 1 
                 1 
                 1 
                 0 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 0 
                 0 
                 1 
                 1 
                 1 
                   
                 0 
                 0 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 0 
                 0 
               
               
                   
                 −1 
                 −1 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 1 
                 1 
                   
                 0 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 0 
               
               
                   
                 −1 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 1 
                   
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
               
               
                   
                   
               
             
          
         
       
     
     With reference to FIG. 4, the generate Wilson kernels routine  134  is depicted for calculating a kernel of any value N as set (block  136 ). A matrix is formed of size (3N+2)×(3N+2) (block  138 ). For a horizontal kernel, the middle N rows are given coefficients, or values, of zero (0). These middle N zero rows are flanked on both sides by row with the centermost N+2 coefficients having a nonzero value or opposite sign from the other, flanked by N zero values on each side (block  140 ). Each more outward row is a copy of its adjacent more inward row, except with two more centered nonzero values (block  142 ). The Wilson horizontal kernel F HNT  is transposed to create the Wilson vertical kernel F VNT , as indicated at  144  in FIG.  4 . 
     Such a scheme allows for generation of kernels for any size N, including N=0. Moreover, both horizontal and vertical kernels detect diagonal edges. It should be appreciated that applications consistent with aspects of the invention may utilize other edge detection kernels. In addition, the kernels may be stored as a lookup table or in other available format without having to create a table. 
     Returning to FIG. 3, the Tables 1-4 illustrate a taper of equal magnitude coefficients for nonzero coefficients (e.g., −1, 1). Other taper functions may be advantageously selected (block  146 ). For example, for a row described as follows: 
     
       
         [− a   (N+1)   −a   N    . . . a   2    a   1  0 . . . 0  a   1    a   2    . . . a   N    a   (N+1) ], 
       
     
     a taper profile includes a maximum at a 1  with a minimum at a (N+1) . Another taper profile has a maximum at a (N+1)  tapering to a minimum at a 1 , which tends to provide a clean result. Yet another taper profile includes a maximum at a N/2  tapering in both directions. Examples of taper functions to provide these tapers include sinusoids and the square root of i/N, where I=1, 2, . . . (N+1). 
     Then each Wilson kernel is convolved with the digital image (blocks  148 ,  150 ). The convolution results are optionally cleaned by clean-peak thresholding (blocks  152 ,  154 ) and summed (block  156 ), discussed in greater detail below. Additional pixel cleaning may optionally include median thresholding (block  158 ) followed by isolation cleaning (block  160 ). In an illustrative embodiment, further optional pixel cleaning may be selected by repeating block  158 , then block  160 , then block  150  again. 
     The results are stored (block  162 ), advantageously allowing multiple passes of edge detection to be performed, suggested by the determination as to whether multi-kernel noise filtering (X=2) is selected (block  164 ). If so, then a determination is made as to whether the size of the kernel N is less than a predetermined maximum kernel size (block  166 ). If the maximum has not been reached, then the size N is incremented (block  168 ) and control returns to block  134 . If reached, then the results from use of each kernel are multiplied (block  170 ). 
     Multiplying the results from the various kernels takes advantage of the spectral properties of the kernels. Detection of infinitesimally thin edges is essentially a high pass filtering operation, which unfortunately includes much of the noise. Tailoring the kernels to find border regions with finite thickness rejects the high frequency components of the image, reducing the noise power and improving the Signal to Noise Ratio (SNR). As can be expected from basic Fourier Transform theory, as the kernel becomes wider, the bandwidth used in the detection process becomes narrower. This does reach a practical limit, though, as kernels must be small enough relative to image dimensions to capture detail. Since there is some overlap between the pass bands of each of the different kernels, it is possible to apply several operations using different widths (N), to take the absolute value of each convolution, and to then multiply the products together. The result is a more highly filtered set of detected edges with reduced noise. 
     If multi-kernel noise filtering was not selected in block  164  (X=1) or after multiplying the results in block  170 , then optional isolation cleaning is performed (block  172 ). Then a determination is made as to whether a binary (yes/no) decision output is desired (Z=1) (block  174 ), and if so, surviving pixels are set to 1 (block  176 ), else surviving pixels are set to confidence values (block  178 ). The confidence value refers to normalizing against the largest value in the result. 
     The various types of pixel cleaning operations referenced in FIG. 3 are depicted in greater detail in FIGS. 5-7. With reference to FIG. 5, the clean-peak thresholding routine  152  begins by calculating confidence values for each of the pixels (block  180 ). Direction of clean is selected, horizontal or vertical (block  182 ). If vertical is selected (block  184 ), then the result is transposed (block  186 ). 
     Application of a kernel to a bitmap image is described hereafter as being performed as a raster scan. In the illustrative method, the Wilson kernel is applied in the manner of a raster scan as used in computer and video graphic displays. In particular, displaying or recording a video image in computer monitors and TV&#39;s is line by line, based on the way in which a cathode ray tube electron gun is directed. Electrons are beamed (scanned) onto the phosphor coating on the screen a line at a time from left to right starting at the top-left corner. At the end of the line, the beam is turned off and moved back to the left and down one line, which is known as the horizontal retrace (fly back). When the bottom-right corner is reached, a vertical retrace (fly back) returns the gun to the top-left corner. In a TV signal, this is known as the vertical blanking interval. It will be appreciated by those skilled in the art having the benefit of the present disclosure that a kernel may be applied in various manners since the order in which pixels are tested is not critical to the result. 
     After horizontal is selected in block  184  or after being transposed in block  186 , then each pixel is tested. Specifically, a determination is made as to whether any pixels remain to be tested (block  188 ). If so, then the next raster position in the image is selected (block  190 ). A subset of N successive pixels at the current pixel position is selected (block  192 ). Consequently, N should be greater than or equal to 2. The median of the subset is determined, including pixels having a value of zero (0) (block  194 ). The pixel under test is flagged for later discarding if below the median for the subset (block  196 ). Then control returns to block  188  for testing for additional pixels, and if none, then flagged pixels are discarded by setting each to zero (0) (block  198 ). 
     Then, if the processing was for results from a vertical kernel (block  200 ), then the cleaned result is transposed (block  202 ). Routine  152  is complete if not vertical in block  200  or after being transposed in block  202 . 
     FIG. 6 illustrates the median thresholding routine  158  that includes ignoring pixels having a value of zero (0) (block  204 ). The confidence value for each pixel is calculated (block  206 ). The median for non-zero confidence values is found (block  208 ) and all nonzero pixels below the median are discarded by setting to zero (0) (block  210 ). 
     FIG. 7 depicts the isolation cleaning routine  160  that begins by calculating confidence values (block  220 ). Then, each pixel is tested for being an isolated pixel for cleaning. Specifically, a determination is made as to whether additional pixels remain to be tested (block  222 ), and if so, the next raster position is tested (block  224 ). The confidence values for all surrounding pixels are summed (block  226 ). If the sum is less than 0.5 (block  228 ), then the pixel under test is set to zero (0) as being an isolated pixel (block  230 ). If not under 0.5 in block  228 , then control returns to block  222  until all pixels have been tested and the routine returns. 
     FIG. 8 illustrates an advantage of the ability to scale the edge detection kernels to the natural edge of a digital image. Specifically, a self-optimizing edge detection routine  240  iteratively locates an optimum kernel for a blurred image having noise. The routine  240  does not require a beforehand knowledge of the clarity of the image or the type of edges contained therein in order to perform edge detection. First, the size of the kernel is set to N=1 (thus, a 5×5 kernel) (block  242 ). The options for utilizing the edge detection routine  130  are set to X=1, Y=1, Z=1 (block  244 ). Specifically, multiplying results of edge detection operations is turned off, clean-peak thresholding is selected, and binary output is selected. Then, edge detection routine  130  is run and the results for this pass is stored (block  246 ). 
     If the kernel size N is greater than 2 (block  248 ), then self-optimization metrics are calculated (block  250 ). If N is not greater than 2 or after calculating metrics, then N is incremented (block  252 ). Then, if N is not greater than a predetermined maximum N (block  254 ), then control returns to block  130  to perform the next pass. If the maximum allowable size for the kernel has been reached in bock  254 , then a natural edge width N OPTIMAL  is determined by locating a peak composite metric (block  256 ) and the corresponding pass for this kernel size is output (block  258 ). 
     With reference to FIG. 9, the calculate self-optimization metric routine  250  referenced in FIG. 8 is depicted wherein the greatest change in pixels deemed to be an edge is found. In particular, first edge pixels are counted that are both in the Result(N) and the previous Result(N−1) and this count is the referred to as “Same” (block  264 ). The edge pixels are counted that were not in the previous Result (N−1) but are in the current Result(N) and referred to as “New” (block  266 ). Also, edge pixels were in the previous Result(N−1) but are not in the current Result(N) are counted and referred to as “Lost” (block  268 ). A first derivative of “Same” is estimated: Same′(N−1)=Same(N)-Same(N−1) (block  270 ). A first derivative of “New” is estimated: New′(N−1)=New(N)-New(N−1) (block  272 ). Also, a first derivative of “Lost” is estimated: Lost′(N−1)=Lost(N)-Lost(N−1) (block  274 ). The composite metric for Result(N−1) is then Same′(N−1)-New′(N)-Lost′(N) (block  276 ). 
     In the illustrative example, the composite metric is shown in FIG. 10, wherein the kernel of size N=6 is optimum. With reference to FIG. 11, a photograph of a grid shadow was intentionally created with significant blur in order to simulate an image collected with resolution exceeding the practical limit. Blur is the result of representing an image at a higher resolution than the true level of detail supported by the collection process and equipment. This photograph of FIG. 11 underwent self-optimizing edge detection routine  240 , resulting in the various Results shown in FIGS. 11A-11I. The optimal size 6 thus corresponds to the output image depicted in FIG.  11 F. 
     In use, a digital image undergoes successive convolutions with increasing sizes of Wilson vertical and horizontal edge detecting kernels with optional pixel cleaning processes to reduce noise in the output. The spatial bandwidth of successive passes provides an opportunity to reduce noise by multiplying the results of multiple passes. Alternatively, the ability of to find the natural edge of an over magnified or blurred image is supported by a self-optimizing edge detection routine that increases the size of the kernels used until the optimum is found. 
     By virtue of the foregoing, there is thus provided self-optimizing general edge detection that performs well in both noisy and blurred images and is suitable for use in imagery where conventional techniques begin to fail. The technique recognizes that edges may actually be transition regions of nontrivial finite width, and so it is able to find edges that span several pixels. Since kernels are generated according to a well-defined algorithm, the kernels are created for specified edge width under test, giving the technique scale independence. Combining kernels of differing sizes reduces vulnerability of the process to noise, giving the process improved noise tolerance. 
     While the present invention has been illustrated by the description of embodiments thereof, and while the embodiments have been described in considerable detail, it is not intended to restrict or in any way limit the scope of the appended claims to such detail. Additional advantages and modifications will readily appear to those skilled in the art. The invention in its broader aspects is, therefore, not limited to the specific details, representative apparatus and method, and illustrative examples shown and described. Accordingly, departures may be made from such details without departing from the spirit or scope of the general inventive concept.