Patent Abstract:
A method, apparatus and program product are presented for detecting an image. Ring contour images are created by blurring the image, posterizing the blurred image at a plurality of levels and creating the ring contour images from each of the posterized images. Convex hull images are created by creating a plurality of corner images from corners within the image located by at least two different corner algorithms, finding a bounding rectangle that encompasses the ring contour images, cropping the corner images using the bounding rectangle, applying a threshold to the cropped corner images, and creating the convex hull images by generating a convex hull from the corners in each of the cropped corner images. A plurality of triangles is created by fitting a triangle with an orientation to the ring contour images and the convex hull images. Finally the orientation of the triangle is determined from the plurality of triangles.

Full 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. 
    
    
     CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is related to U.S. patent application Ser. No. 13/025,624, filed on even date herewith by Pinkus et al., and entitled “Automatic Landolt C Gap Detection Software Architecture for Image Quality Analysis” (AFD 1121), the disclosure of which is incorporated by reference herein in its entirety. 
     BACKGROUND OF THE INVENTION 
     The present invention relates to pattern and object recognition, and more particularly to a method for detecting an object in a digital image. 
     Over the years, there have been many methods developed to determine the image quality of an image-generating system such as a sensor/display combination. In most cases, the final consumer of the image produced is a human observer using their visual capability to extract visual information from the displayed image. In recent years, imaging systems and image manipulation have moved from the analog world to the digital world, which has probably added a bit more confusion to the issue of image quality or resolution. 
     In general, resolution is the ability of a sensor/display system to produce detail; the higher the resolution, the finer the detail that can be displayed. With the advent of digital imagery and sensor detectors that are composed of an array of discrete elements, it is tempting, and not entirely wrong, to characterize the resolution of the system by the number of picture elements (pixels) for the display or sensor elements in the case of the sensor. For example, VGA resolution for a computer display is 480 elements high by 640 elements wide and SVGA is 600×800 elements. This describes the number of samples that can be displayed; however, the number of pixels alone says nothing of the quality of the actual display medium characteristics (luminance, contrast capability, noise, color, refresh rate, active area to total area ratio, etc.) or of the signal/information used to feed the individual pixels. Nevertheless, this numerical value of pixel or sensor element count is often given as a primary metric to the resolution (quality) of the sensor or display. 
     Another common approach to determining the resolution of a sensor/display system is to image an appropriate resolution test target and determine the smallest sized critical test pattern element that can be seen by a human observer. Many test patterns have been developed over the years such as grating, tri-bars, tumbling Es, the Snellen chart, and the Landolt C chart to test vision or to test imaging systems using vision. The test pattern typically has test elements of various sizes so that the human observer can pick out the smallest size that they can resolve. An alternative to the multi-sized test pattern is to use a single size test element, but image it at various distances until a distance is obtained at which the test object is barely resolved. 
     Related to resolution is visual acuity, which is acuteness or clearness of vision that is dependent on the sharpness of the retinal focus within the eye and the sensitivity of the interpretative faculty of the brain. For example, numerous methods have been used to determine night vision goggle (“NVG”) visual acuity such as limiting resolution, Snellen Acuity, square wave targets, Landolt Cs, adaptive psychophysical, and directly measuring the psychometric function or the “frequency of seeing” curve. Each method produces a number that is composed of an actual acuity value plus error. There can be many sources of error but the largest is generally the method itself as well as the inherent variability of the observer while working under threshold conditions. Observer variability may be reduced through extensive training, testing the same time every day, and shortened sessions in order to reduce eye fatigue. Additionally, even though observers are given specific instructions, response criteria may also vary among or within observers; even over the course of a single experimental session. To assist in eliminating the criteria problem, a four alternative forced-choice paradigm was developed and utilized to measure the entire psychometric function. This paradigm allowed for any desired response criteria level (e.g., 50% or 75% corrected for chance, probability of detection) to be selected for the prediction of (NVG) visual acuity performance. Although all of the preceding was directed at visual acuity/resolution assessment of night vision goggles using multiple human observers the “resolution” concept applies equally well to digital imagery. 
     Current and future military weapons systems (e.g. micro UAVs, satellites, surveillance, weapons aiming optics, day/night head-mounted devices) will increasingly rely on digitally-based multi-spectral imaging capabilities. With digital media comes the potential to register, fuse, and enhance digital images whether they are individual images or streaming video gathered in real-time. Multi-spectral fusion and enhancement provides the greatly increased potential to detect, track, and identify difficult targets, such as those that are camouflaged, buried, hidden behind a smoke screen or obscured by atmospheric effects (haze, rain, fog, snow). 
     There are several different conventional techniques to assess the relative improvement in image quality when an image-enhancing algorithm has been applied to a digital image. The testing of enhancing effects often consists of subjective quality assessments or measures of the ability of an automatic target detection program to find a target before and after an image has been enhanced. It is rare to find studies that focus on the human ability to detect a target in an enhanced image using scenarios that are relevant for the particular application for which the enhancement is intended. While a particular algorithm may make an image appear substantially better after enhancement, there is no indication as to whether this improvement is significant enough to improve human visual performance. 
     Therefore, there is a need in the art to automatically assess image quality in terms of modeled human visual resolution perceptual qualities (i.e., the “frequency of seeing” curve) but without the need to actually use human observers. 
     SUMMARY OF THE INVENTION 
     Embodiments of the invention address the need in the art by providing a method of detecting a target image, and in particular a triangle having a particular orientation. A plurality of ring contour images is created by blurring the image, posterizing the blurred image at a plurality of levels to generate a plurality of posterized images, and creating the plurality of ring contour images from each of the plurality of posterized images. Additionally, a plurality of convex hull images is created by creating a plurality of corner images from corners within the image located by at least two different corner algorithms, finding a bounding rectangle that encompasses the plurality of ring contour images, cropping the plurality of corner images using the bounding rectangle, applying a threshold to the plurality of cropped corner images, and creating the plurality of convex hull images by generating a convex hull from the corners in each of the plurality of cropped corner images. From these sets of images a plurality of triangles is created by fitting a triangle with an orientation to each of the plurality of ring contour images and each of the plurality of convex hull images. Finally, the orientation of the triangle is determined from the plurality of triangles. 
     In some embodiments, and prior to creating the plurality of ring contour images and the plurality of convex hull images, the image may be prepared by first enlarging the image. The enlarged image may then be cropped to a target area of interest in the image to assist in reducing computation and processing times. The cropped image is then denoised and sharpened utilizing standard denoise and sharpening algorithms as known in the art. 
     In some embodiments, the plurality of ring contour images is created from each of the plurality of posterized images. The approximate center of the blurred image is determined. A start point is located on a color boundary. The color boundary is then traversed from the start point to generate a contour. If the traversal of the color boundary ends on the start point and if the resulting contour encloses the approximate center of the blurred image, a ring contour image is created including the contour. 
     In some embodiments, triangles are fit to each of the plurality of ring contour images and each of the plurality of convex hull images by first retrieving a contour from an image of the plurality of contour images or a convex hull from an image of the plurality of convex hull images. A triangle in a first orientation is fit to the contour or convex hull. If the triangle in the first orientation does not encompass the contour or convex hull, the triangle in the first orientation is enlarged until it does encompass the contour or convex hull. A triangle in a second orientation is also fit to the contour or convex hull. Similarly, if the triangle in the second orientation does not encompass the contour or convex hull, the triangle in the second orientation is enlarged until it does encompass the contour or convex hull. In some embodiments, additional orientations of triangles may be fitted to the contour and complex hull. Finally, the smaller of the triangles fit to the contour or convex hull is selected. 
     In some embodiments, the orientations of the triangles may include the triangle being oriented in an upward direction, a downward direction, a rightward direction, a leftward direction, and combinations thereof. In a specific embodiment, the orientation of the triangle is determined from the plurality of triangles by selecting an orientation corresponding to a majority of the plurality of triangles. 
     Embodiments of the invention also provide an apparatus having a processor and program code. The program code is configured to be executed by the processor to detect an image. The program code is further configured to create a plurality of ring contour images by blurring the image, posterizing the blurred image at a plurality of levels to generate a plurality of posterized images, and creating the plurality of ring contour images from each of the plurality of posterized images. The program code is further configured to create a plurality of convex hull images by creating a plurality of corner images from corners within the image located by at least two different corner algorithms, finding a bounding rectangle that encompasses the plurality of ring contour images, cropping the plurality of corner images using the bounding rectangle, applying a threshold to the plurality of cropped corner images, and creating the plurality of convex hull images by generating a convex hull from the corners in each of the plurality of cropped corner images. The program code is further configured to create a plurality of triangles by fitting a triangle with an orientation to each of the plurality of ring contour images and each of the plurality of convex hull images. And finally the program code is further configured to determine the orientation of the triangle from the plurality of triangles. 
     Embodiments of the invention additionally provide a program product including a computer recordable type medium and a program code configured to detect an image. The program code is resident on the computer recordable type medium and further configured, when executed on a hardware implemented processor, to create a plurality of ring contour images by blurring the image, posterizing the blurred image at a plurality of levels to generate a plurality of posterized images, and creating the plurality of ring contour images from each of the plurality of posterized images. The program code is further configured to create a plurality of convex hull images by creating a plurality of corner images from corners within the image located by at least two different corner algorithms, finding a bounding rectangle that encompasses the plurality of ring contour images, cropping the plurality of corner images using the bounding rectangle, applying a threshold to the plurality of cropped corner images, and creating the plurality of convex hull images by generating a convex hull from the corners in each of the plurality of cropped corner images. The program code is further configured to create a plurality of triangles by fitting a triangle with an orientation to each of the plurality of ring contour images and each of the plurality of convex hull images. And finally the program code is further configured to determine the orientation of the triangle from the plurality of triangles. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with a general description of the invention given above, and the detailed description given below, serve to explain the invention. 
         FIG. 1  is a schematic block diagram of an exemplary hardware and software environment for a computer system suitable for implementing an algorithm to detect images consistent with embodiments of the invention. 
         FIG. 2  illustrates a target image to be recognized at a distance from an observer or sensor to be used with the image detection algorithm. 
         FIG. 3  is a flow chart illustrating the steps to prepare an image for the image detection algorithm. 
         FIG. 4  is a flow chart illustrating a portion of the image detection algorithm. 
         FIG. 5  is a flow chart illustrating a portion of the image detection algorithm. 
         FIG. 6  is a flow chart illustrating a portion of the image detection algorithm. 
         FIG. 7  is a flow chart illustrating a portion of the image detection algorithm. 
         FIG. 8  is a flow chart illustrating a portion of the image detection algorithm. 
         FIG. 9  is a flow chart illustrating a portion of the image detection algorithm. 
         FIG. 10  illustrates a set of exemplary results from the image detection algorithm used to select the final image orientation. 
         FIG. 11  is a table representing results from images acquired from various sensors at various distances. 
         FIG. 12  is a graph of the data in the table in  FIG. 11 . 
     
    
    
     It should be understood that the appended drawings are not necessarily to scale, presenting a somewhat simplified representation of various features illustrative of the basic principles of the invention. The specific design features of the sequence of operations as disclosed herein, including, for example, specific dimensions, orientations, locations, and shapes of various illustrated components, will be determined in part by the particular intended application and use environment. Certain features of the illustrated embodiments have been enlarged or distorted relative to others to facilitate visualization and clear understanding. In particular, thin features may be thickened, for example, for clarity or illustration. 
     DETAILED DESCRIPTION OF THE INVENTION 
     Embodiments of the invention address the need in the art by providing a software architecture and procedure that allow automatic detection of digital images and quality using only a computer. This is in contrast to either simple “before” and “after” subjective visual comparisons or laborious and costly psychophysical procedures requiring extensive testing of multiple trained observers who are required to view and correctly judge the different orientation of many differently sized stimuli such as Landolt Cs or triangles. Embodiments of the invention utilize a software-implemented automatic Triangle Orientation Detection (“TOD”) model, which has been designed to produce a similar frequency of seeing function as those produced by real observers. Thus, the variations among different multispectral sensors as well as image registration, fusion, and/or enhancement algorithms can be relatively quickly, accurately, and automatically assessed in terms of human visual perception but without the need for human observers. 
     Turning to the drawings, wherein like numbers denote like parts throughout the several views,  FIG. 1  illustrates an exemplary hardware and software environment for an apparatus  10  suitable for implementing an image quality assessment system consistent with embodiments of the invention. For the purposes of the invention, apparatus  10  may represent practically any type of computer, computer system or other programmable electronic device, including a client computer, a server computer, a portable computer, a handheld computer, an embedded controller, etc. Moreover, apparatus  10  may be implemented using one or more networked computers, e.g., in a cluster or other distributed computing system. Apparatus  10  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 embodiments of the invention. 
     Computer  10  typically includes a central processing unit (CPU)  12  including one or more microprocessors coupled to a memory  14 , which may represent the random access memory (RAM) devices comprising the main storage of computer  10 , as well as any supplemental levels of memory, e.g., cache memories, non-volatile or backup memories (e.g., programmable or flash memories), read-only memories, etc. In addition, memory  14  may be considered to include memory storage physically located elsewhere in computer  10 , e.g., any cache memory in a processor in CPU  12 , as well as any storage capacity used as a virtual memory, e.g., as stored on a mass storage device  16  or on another computer coupled to computer  10 . 
     Computer  10  also typically receives a number of inputs and outputs for communicating information externally. For interface with a user or operator, computer  10  typically includes a user interface  18  incorporating one or more user input devices  20  (e.g., a keyboard, a mouse, a trackball, a joystick, a touchpad, and/or a microphone, among others) and a display  22  (e.g., a CRT monitor, an LCD display panel, and/or a speaker, among others). Otherwise, user input may be received via another computer or terminal, e.g., via a client or single-user computer (not shown) coupled to computer  10  over a network  24 . This latter implementation may be desirable where computer  10  is implemented as a server or other form of multi-user computer. However, it should be appreciated that computer  10  may also be implemented as a standalone workstation, desktop, laptop, hand-held, or other single-user computer in some embodiments. 
     For non-volatile storage, computer  10  typically includes one or more mass storage devices  16 , e.g., a floppy or other removable disk drive, a hard disk drive, a direct access storage device (DASD), an optical drive (e.g., a CD drive, a DVD drive, etc.), flash memory data storage devices (USB flash drive) and/or a tape drive, among others. Furthermore, computer  10  may also include an interface  26  with one or more networks  24  (e.g., a LAN, a WAN, a wireless network, and/or the Internet, among others) to permit the communication of information with other computers and electronic devices. It should be appreciated that computer  10  typically includes suitable analog and/or digital interfaces (e.g., BUS) between CPU  12  and each of components  14 ,  16 ,  18 , and  26 , as is well known in the art. 
     Computer  10  operates under the control of an operating system  28 , and executes or otherwise relies upon various computer software applications, components, programs, objects, modules, data structures, etc. For example, an image detection algorithm  30  may be resident in memory  14  to analyze image  32  also in memory or alternately resident in mass storage  16 . Moreover, various applications, components, programs, objects, modules, etc. may also execute on one or more processors in another computer coupled to computer  10  via the network  24 , e.g., in a distributed or client-server computing environment, whereby the processing required to implement the functions of a computer program, such as the image detection algorithm  30 , may be allocated to multiple computers over the network  24 . 
     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, or even a subset thereof, will be referred to herein as “computer program code,” or simply “program code.” Program code typically comprises one or more instructions that are resident at various times in various memory and storage devices in a computer, and that, when read and executed by one or more processors in a 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 computer readable signal bearing media used to actually carry out the distribution. Examples of computer readable 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. 
     In addition, various program code described hereinafter may be identified based upon the application within which it is 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. Furthermore, given the typically endless number of manners in which computer programs may be organized into routines, procedures, methods, modules, objects, and the like, as well as the various manners in which program functionality may be allocated among various software layers that are resident within a typical computer (e.g., operating systems, libraries, API&#39;s, applications, applets, etc.), it should be appreciated that the invention is not limited to the specific organization and allocation of program functionality described herein. 
     Those skilled in the art will recognize that the exemplary environment illustrated in  FIG. 1  is 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. 
     Embodiments of the invention implement an algorithm  30  configured to detect Triangles as a resolution target. As illustrated in  FIG. 2 , a sensor, such as an Infrared (IR), Near Infrared (NIR), or Visual (VIS) sensor,  34  is directed at a target  36 . Images  32  of the target  36  were acquired at multiple distances  38  from the sensor  34  to the target  36  as the method for probing the resolution of the sensor. As discussed above and in an alternate embodiment, the size of the Target  36  may also be adjusted, holding the distance  38  constant. Each of the images  32  is prepared as seen in the flowchart  40  in  FIG. 3 , prior to being analyzed. Turning now to  FIG. 3 , the process starts at block  42 . The image is first enlarged at block  44 , with the factor of enlargement being dependent on the sensor. For example, in a specific embodiment, the image  32  is enlarged by a factor of four. After the image is enlarged, the image is cropped around an area of interest at block  46  to assist in reducing the amount of computation time necessary to analyze the image. In a specific embodiment with a sensor having a resolution of 640×480, the image may be cropped to a size of 180×120, which is sufficient to encompass the target  36 . The image is then sent through a denoising algorithm (block  48 ) and a sharpening algorithm (block  50 ). Any standard denoise and sharpen algorithm as are known to those of ordinary skill in the art may be used for blocks  48  ad  50 . The process ends at block  52 . 
     The algorithm associated with embodiments of the invention then uses two different methods to identify and locate the target  36  in the prepared image. The first method finds contours that are then used to identify and locate the target  36 . The second method finds potential corners of the target  36  that are then used to identify and locate the target  36 . While the description of these methodologies may suggest that they be performed serially, there is a potential in each of the methodologies for a parallel implementation as well. Beginning first with the contour methodology and turning now to flowchart  60  in  FIG. 4 , the process starts at block  62 . The image is blurred at block  64 . In some embodiments, the image may be blurred using a Gaussian kernel of size ranging from about 5 to about 15. In a particular embodiment, a Gaussian kernel of size 11 was used. The blurred image is then posterized at block  66 . 
     Initially the image is posterized at level 2 creating an image of only black and white pixels. Additional levels of posterizing may also be included for some embodiments, with the posterizing level increasing for each subsequent level. For example, in some embodiments, up to seven posterizing levels (2,3,4,5,6,7,8) may be used, though other embodiments may use fewer posterized levels. While it was determined that posterized levels in excess of about seven did not add any appreciable advantage, additional posterized levels past seven may be utilized. If additional posterized levels are available (“YES” branch of decision block  68 ), then the blurred image is posterized at the next level at block  70 . Otherwise, if there are no additional levels (“NO” branch of decision block  68 ), then contours are determined for the first posterized image at block  72 . If there are additional posterized images (“YES” branch of decision block  74 ), then contours are determined for the next posterized image at block  76  until all images are processed. If there are no additional posterized images (“NO” branch of decision block  74 ), then the process ends at block  78 . 
     In some embodiments and as seen in flowchart  80  in  FIG. 5 , the pixel boundaries of the posterized images are used to determine contour lines of the posterized image. The process begins at block  82 . An approximate center of the image is determined at block  84 . A color boundary is located at block  86 . In some embodiments that image  32  is a grey scale image with pixel values ranging from 0 (black) to 255 (white), and for simplicity, this algorithm will be further described with reference to grey scale images, though other embodiments may employ a full color palate. The boundary is then traversed in a counter-clockwise direction at block  88 , though in other embodiments a clockwise direction may be used. 
     If the boundary being traversed for the contour does not end at the point where the traverse started (“NO” branch of decision block  90 ), i.e. and open contour, the contour is discarded at block  92 . If the contour does end on at the point where the traverse started (“YES” branch of decision block  90 ), then a check is made to determine if the contour encloses the center of the image. If the contour does not enclose the center of the image (“NO” branch of decision block  94 ), then the contour is discarded at block  92 . Otherwise, if the contour does enclose the center of the image (“YES” branch of decision block  94 ), the then contour is kept at block  96  for further processing. The process ends at block  96  after either keeping or discarding the contour. The process of following the boundary to determine contours may be performed multiple times to capture each contour when multiple color boundaries are present in the image. 
     Before completing the analysis with the contours determined above, the second method utilizing corners of the target  36  is set out in  FIGS. 6 and 7 . Starting with the flowchart  100  in  FIG. 6 , the process begins at block  102 . Instead of blurring the image  32  as was done for the contours above, a Harris Corners algorithm as is known in the art is used to determine the corners in the image  32  at block  104 . The Harris corners algorithm may produce stray corners or real unwanted corners in the image. To assist in reducing the corners to only those of the target  36 , the image is cropped. To assist with the cropping, in some embodiments, the contour images above are combined into a single image and a bounding rectangle is determined which encompasses all of the contours at block  106 . In a specific embodiment, the contour images are combined using a logical OR function, though other methods of combining the images into a single image may also be used. The bounding rectangle is now used to crop the Harris corner image in block  108 . In one embodiment, the cropping of the image is accomplished using a logical AND function. A threshold ranging from about 32 to about 96 is applied to the cropped image to better define the corners in block  110 . In a particular embodiment, a threshold value of 64 is used, where any pixel values below 64 become black and any pixel values above 64 become white. A convex hull is then generated connecting each of the corners loaded in block  112 . The process ends at block  114 . 
     Similarly, and as seen in flowchart  120  in  FIG. 7 , the process starts at block  122 . Instead of blurring the image or using the Harris corners algorithm, an eigenvalue corners algorithm as known in the art is utilized in order to determine the corners of the image  32  at block  124 . The rectangle used above to crop the Harris corner image is also found at block  126  and used to also crop the eigenvalue corner image at block  128 . The same threshold is applied to the cropped image at block  130  and a convex hull is generated through each of the corner points at block  132 . The process ends at block  134 . 
     Now that each of the images containing either contours or convex hulls is generated, triangles may be fit to each of the images which will then be used to determine the location and orientation of the target triangle  36 . Specifically, and with reference to flowchart  140  in  FIG. 8 , the process begins at block  142 . The contour image generated from the first posterized image is retrieved in block  144  and an equilateral triangle oriented either upward, downward, to the left, or to the right is fit to the determined contour in block  146 . If there are additional contour images from additional posterized images (“YES” branch of decision block  148 ), then those contoured images are received in block  150  and an equilateral triangle oriented either upward, downward, to the left, or to the right is fit to the determined contour in block  146 . If there are no further contoured images (“NO” branch of decision block  148 ), then the convex hull generated from the Harris corners is retrieved at block  152 . An equilateral triangle oriented either upward, downward, to the left, or to the right is fit to the convex hull in block  154 . Similarly, the convex hull from the eigenvalue corners is retrieve at block  156 . An equilateral triangle oriented either upward, downward, to the left, or to the right is fit to the convex hull in block  158 . Finally, all of the fit triangles are analyzed to determine the orientation of the triangle in the target  36  at block  160 . In some embodiments, this orientation may be determined by the majority of the triangles oriented in the same direction. In other embodiments, other methods for determination may be used. The process ends at block  162 . 
     In order to fit the triangles in some embodiments, and as shown in flowchart  170  in  FIG. 9 , each of the four orientations is tried with the best orientation being selected. The process starts at block  172 . An upward directed triangle is fit to the contour or convex hull in block  176 . If the triangle does not encompass the contour or the convex hull (“NO” branch of decision block  176 ), then the size of the triangle is increased at block  178  until the triangle does encompass the contour or convex hull. If the triangle does encompass the contour or convex hull (“YES” branch of decision block  176 ), then the next triangle selected from a downward directed, right directed, or left directed triangle is fit to the contour or convex hull at block  180 . If this triangle does not encompass the contour or convex hull (“NO” branch of decision block  182 ) then the size of the triangle is increased at block  184  until the triangle does encompass the contour or convex hull. If the triangle does encompass the contour or convex hull (“YES” branch of decision block  182 ) then the process repeats at block  180  until all four orientations of the triangle have been fit. If there are no other triangles to fit (“NO” branch of decision block  186 ), then the smallest of the four triangles is selected for the contour or convex hull at block  188 . The process ends at block  190 . 
       FIG. 10  illustrates an example of the triangles  192 - 208  that may have been fit for a particular embodiment having seven contoured images with the Harris and eigenvalue corner images, resulting in nine triangles. As set forth above with respect to flowchart  140  in  FIG. 8 , six  192 ,  194 ,  196 ,  200 ,  204 ,  206  of the nine triangles  192 - 208  are oriented in a upward direction. Based on that majority, the determination from the image detection algorithm is that the triangle in the target  36  is an upward directed triangle. The table in  FIG. 11  illustrates exemplary data  214 ,  216 ,  216  for images generated respectively by an IR, NIR, and VIS sensor at several distances  212 . A series of images from each of these sensors may be generated at each of the distances  212  and evaluated with the image detection algorithm set out above. The results (in percentage correct)  214 - 218  may be determined based on the output of the algorithm and the actual orientation of the target  36 . This data can then be plotted on graph  220  as illustrated in  FIG. 12  showing “frequency of seeing” curves  222 ,  224 ,  226  for each of the respective IR, NIR, and VIS sensors. 
     This data may now be used as an initial screening of sensors in order to reduce the number sensors to a select few that may then be subjected to testing by multiple human observers. Alternately, the data may then be used as a “quick and dirty” evaluation of a number of sensors to assist in selecting a sensor when the funding or time does not permit an exhaustive test by human observers. Furthermore, the data can be used to evaluate digital images combined, overlaid, or otherwise enhanced to again limit the combinations before presenting these enhanced images to actual human observers. Additionally, the triangle detection algorithm may also be used in conjunction with other image detection algorithms, such as a Landolt C recognition algorithm as discussed in co-pending U.S. application Ser. No. 13/025,624. The use of multiple detection algorithms may present a better evaluation of a sensor or image resolution or quality. 
     While the present invention has been illustrated by a description of one or more embodiments thereof and while these embodiments have been described in considerable detail, they are 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 scope of the general inventive concept.

Technology Classification (CPC): 6