Patent Publication Number: US-8542917-B2

Title: System and method for identifying complex tokens in an image

Description:
BACKGROUND OF THE INVENTION 
     Many significant and commercially important uses of modern computer technology relate to images. These include image processing, image analysis and computer vision applications. In computer vision applications, such as, for example, object recognition and optical character recognition, it has been found that a separation of illumination and material aspects of an image can significantly improve the accuracy of computer performance. 
     SUMMARY OF THE INVENTION 
     The present invention provides a method and system comprising image techniques that accurately and correctly identify regions of an image that each correspond to a single material reflectance in a scene depicted in the image. 
     In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, determining log chromaticity representations for the image, clustering the log chromaticity representations to provide clusters of similar log chromaticity representations and identifying regions of uniform reflectance in the image as a function of the clusters of similar log chromaticity representations. 
     In a second exemplary embodiment of the present invention, a computer system is provided. The computer system comprises a CPU and a memory storing an image file containing an image. According to a feature of the present invention, the CPU is arranged and configured to execute a routine to determine log chromaticity representations for the image, cluster the log chromaticity representations to provide clusters of similar log chromaticity representations and identify regions of uniform reflectance in the image as a function of the clusters of similar log chromaticity representations. 
     In a third exemplary embodiment of the present invention, a computer program product, disposed on a computer readable media is provided. The computer program product includes computer executable process steps operable to control a computer to: provide an image file depicting an image, in a computer memory, determine log chromaticity representations for the image, cluster the log chromaticity representations to provide clusters of similar log chromaticity representations and identify regions of uniform reflectance in the image as a function of the clusters of similar log chromaticity representations. 
     In accordance with yet further embodiments of the present invention, computer systems are provided, which include one or more computers configured (e.g., programmed) to perform the methods described above. In accordance with other embodiments of the present invention, non-transitory computer readable media are provided which have stored thereon computer executable process steps operable to control a computer(s) to implement the embodiments described above. The present invention contemplates a computer readable media as any product that embodies information usable in a computer to execute the methods of the present invention, including instructions implemented as a hardware circuit, for example, as in an integrated circuit chip. The automated, computerized methods can be performed by a digital computer, analog computer, optical sensor, state machine, sequencer, integrated chip or any device or apparatus that can be designed or programmed to carry out the steps of the methods of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a computer system arranged and configured to perform operations related to images. 
         FIG. 2  shows an n×m pixel array image file for an image stored in the computer system of  FIG. 1 . 
         FIG. 3   a  is a flow chart for identifying Type C token regions in the image file of  FIG. 2 , according to a feature of the present invention. 
         FIG. 3   b  is an original image used as an example in the identification of Type C tokens. 
         FIG. 3   c  shows Type C token regions in the image of  FIG. 3   b.    
         FIG. 3   d  shows Type B tokens, generated from the Type C tokens of  FIG. 3   c , according to a feature of the present invention. 
         FIG. 4  is a flow chart for a routine to test Type C tokens identified by the routine of the flow chart of  FIG. 3   a , according to a feature of the present invention. 
         FIG. 5  is a graphic representation of a log color space chromaticity plane according to a feature of the present invention. 
         FIG. 6  is a flow chart for determining a list of colors depicted in an input image. 
         FIG. 7  is a flow chart for determining an orientation for a log chromaticity space, according to a feature of the present invention. 
         FIG. 8  is a flow chart for determining log chromaticity coordinates for the colors of an input image, as determined through execution of the routine of  FIG. 6 , according to a feature of the present invention. 
         FIG. 9  is a flow chart for augmenting the log chromaticity coordinates, as determined through execution of the routine of  FIG. 8 , according to a feature of the present invention. 
         FIG. 10  is a flow chart for clustering the log chromaticity coordinates, according to a feature of the present invention. 
         FIG. 11  is a flow chart for assigning the log chromaticity coordinates to clusters determined through execution of the routine of  FIG. 10 , according to a feature of the present invention. 
         FIG. 12  is a flow chart for detecting regions of uniform reflectance based on the log chromaticity clustering according to a feature of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Referring now to the drawings, and initially to  FIG. 1 , there is shown a block diagram of a computer system  10  arranged and configured to perform operations related to images. A CPU  12  is coupled to a device such as, for example, a digital camera  14  via, for example, a USB port. The digital camera  14  operates to download images stored locally on the camera  14 , to the CPU  12 . The CPU  12  stores the downloaded images in a memory  16  as image files  18 . The image files  18  can be accessed by the CPU  12  for display on a monitor  20 , or for print out on a printer  22 . 
     Alternatively, the CPU  12  can be implemented as a microprocessor embedded in a device such as, for example, the digital camera  14  or a robot. The CPU  12  can also be equipped with a real time operating system for real time operations related to images, in connection with, for example, a robotic operation or an interactive operation with a user. 
     As shown in  FIG. 2 , each image file  18  comprises an n×m pixel array. Each pixel, p, is a picture element corresponding to a discrete portion of the overall image. All of the pixels together define the image represented by the image file  18 . Each pixel comprises a digital value corresponding to a set of color bands, for example, red, green and blue color components (RGB) of the picture element. The present invention is applicable to any multi-band image, where each band corresponds to a piece of the electro-magnetic spectrum. The pixel array includes n rows of m columns each, starting with the pixel p ( 1 , 1 ) and ending with the pixel p(n, m). When displaying or printing an image, the CPU  12  retrieves the corresponding image file  18  from the memory  16 , and operates the monitor  20  or printer  22 , as the case may be, as a function of the digital values of the pixels in the image file  18 , as is generally known. 
     In an image operation, the CPU  12  operates to analyze the RGB values of the pixels of a stored image file  18  to achieve various objectives, such as, for example, to identify regions of an image that correspond to a single material depicted in a scene recorded in the image file  18 . A fundamental observation underlying a basic discovery of the present invention, is that an image comprises two components, material and illumination. All changes in an image are caused by one or the other of these components. A method for detecting of one of these components, for example, material, provides a mechanism for distinguishing material or object geometry, such as object edges, from illumination and shadow boundaries. 
     Such a mechanism enables techniques that can be used to generate intrinsic images. The intrinsic images correspond to an original image, for example, an image depicted in an input image file  18 . The intrinsic images include, for example, an illumination image, to capture the intensity and color of light incident upon each point on the surfaces depicted in the image, and a material reflectance image, to capture reflectance properties of surfaces depicted in the image (the percentage of each wavelength of light a surface reflects). The separation of illumination from material in the intrinsic images provides the CPU  12  with images optimized for more effective and accurate further processing. 
     Pursuant to a feature of the present invention, a token is a connected region of an image wherein the pixels of the region are related to one another in a manner relevant to identification of image features and characteristics such as an identification of materials and illumination. The pixels of a token can be related in terms of either homogeneous factors, such as, for example, close correlation of color among the pixels, or inhomogeneous factors, such as, for example, differing color values related geometrically in a color space such as RGB space, commonly referred to as a texture. The present invention utilizes spatio-spectral information relevant to contiguous pixels of an image depicted in an image file  18  to identify token regions. The spatio-spectral information includes spectral relationships among contiguous pixels, in terms of color bands, for example the RGB values of the pixels, and the spatial extent of the pixel spectral characteristics relevant to a single material. 
     According to one exemplary embodiment of the present invention, tokens are each classified as either a Type A token, a Type B token or a Type C token. A Type A token is a connected image region comprising contiguous pixels that represent the largest possible region of the image encompassing a single material in the scene (uniform reflectance). A Type B token is a connected image region comprising contiguous pixels that represent a region of the image encompassing a single material in the scene, though not necessarily the maximal region of uniform reflectance corresponding to that material. A Type B token can also be defined as a collection of one or more image regions or pixels, all of which have the same reflectance (material color) though not necessarily all pixels which correspond to that material color. A Type C token comprises a connected image region of similar image properties among the contiguous pixels of the token, where similarity is defined with respect to a noise model for the imaging system used to record the image. 
     Referring now to  FIG. 3   a , there is shown a flow chart for identifying Type C token regions in the scene depicted in the image file  18  of  FIG. 2 , according to a feature of the present invention. Type C tokens can be readily identified in an image, utilizing the steps of  FIG. 3   a , and then analyzed and processed to construct Type B tokens, according to a feature of the present invention. 
     A 1 st  order uniform, homogeneous Type C token comprises a single robust color measurement among contiguous pixels of the image. At the start of the identification routine, the CPU  12  sets up a region map in memory. In step  100 , the CPU  12  clears the region map and assigns a region ID, which is initially set at 1. An iteration for the routine, corresponding to a pixel number, is set at i=0, and a number for an N×N pixel array, for use as a seed to determine the token, is set an initial value, N=N start . N start  can be any integer&gt;0, for example it can be set at set at 11 or 15 pixels. 
     At step  102 , a seed test is begun. The CPU  12  selects a first pixel, i=1, pixel ( 1 ,  1 ) for example (see  FIG. 2 ), the pixel at the upper left corner of a first N×N sample of the image file  18 . The pixel is then tested in decision block  104  to determine if the selected pixel is part of a good seed. The test can comprise a comparison of the color value of the selected pixel to the color values of a preselected number of its neighboring pixels as the seed, for example, the N×N array. The color values comparison can be with respect to multiple color band values (RGB in our example) of the pixel. If the comparison does not result in approximately equal values (within the noise levels of the recording device) for the pixels in the seed, the CPU  12  increments the value of i (step  106 ), for example, i=2, pixel ( 1 ,  2 ), for a next N×N seed sample, and then tests to determine if i=i max  (decision block  108 ). 
     If the pixel value is at i max , a value selected as a threshold for deciding to reduce the seed size for improved results, the seed size, N, is reduced (step  110 ), for example, from N=15 to N=12. In an exemplary embodiment of the present invention, i max  can be set at a number of pixels in an image ending at pixel (n, m), as shown in  FIG. 2 . In this manner, the routine of  FIG. 3   a  parses the entire image at a first value of N before repeating the routine for a reduced value of N. 
     After reduction of the seed size, the routine returns to step  102 , and continues to test for token seeds. An N stop  value (for example, N=2) is also checked in step  110  to determine if the analysis is complete. If the value of N is at N stop , the CPU  12  has completed a survey of the image pixel arrays and exits the routine. 
     If the value of i is less than i max , and N is greater than N stop , the routine returns to step  102 , and continues to test for token seeds. 
     When a good seed (an N×N array with approximately equal pixel values) is found (block  104 ), the token is grown from the seed. In step  112 , the CPU  12  pushes the pixels from the seed onto a queue. All of the pixels in the queue are marked with the current region ID in the region map. The CPU  12  then inquires as to whether the queue is empty (decision block  114 ). If the queue is not empty, the routine proceeds to step  116 . 
     In step  116 , the CPU  12  pops the front pixel off the queue and proceeds to step  118 . In step  118 , the CPU  12  marks “good” neighbors around the subject pixel, that is neighbors approximately equal in color value to the subject pixel, with the current region ID. All of the marked good neighbors are placed in the region map and also pushed onto the queue. The CPU  12  then returns to the decision block  114 . The routine of steps  114 ,  116 ,  118  is repeated until the queue is empty. At that time, all of the pixels forming a token in the current region will have been identified and marked in the region map as a Type C token. 
     When the queue is empty, the CPU  12  proceeds to step  120 . At step  120 , the CPU  12  increments the region ID for use with identification of a next token. The CPU  12  then returns to step  106  to repeat the routine in respect of the new current token region. 
     Upon arrival at N=N stop , step  110  of the flow chart of  FIG. 3   a , or completion of a region map that coincides with the image, the routine will have completed the token building task.  FIG. 3   b  is an original image used as an example in the identification of tokens. The image shows areas of the color blue and the blue in shadow, and of the color teal and the teal in shadow.  FIG. 3   c  shows token regions corresponding to the region map, for example, as identified through execution of the routine of  FIG. 3   a  (Type C tokens), in respect to the image of  FIG. 3   b . The token regions are color coded to illustrate the token makeup of the image of  FIG. 3   b , including penumbra regions between the full color blue and teal areas of the image and the shadow of the colored areas. 
     While each Type C token comprises a region of the image having a single robust color measurement among contiguous pixels of the image, the token may grow across material boundaries. Typically, different materials connect together in one Type C token via a neck region often located on shadow boundaries or in areas with varying illumination crossing different materials with similar hue but different intensities. A neck pixel can be identified by examining characteristics of adjacent pixels. When a pixel has two contiguous pixels on opposite sides that are not within the corresponding token, and two contiguous pixels on opposite sides that are within the corresponding token, the pixel is defined as a neck pixel. 
       FIG. 4  shows a flow chart for a neck test for Type C tokens. In step  122 , the CPU  12  examines each pixel of an identified token to determine whether any of the pixels under examination forms a neck. The routine of  FIG. 4  can be executed as a subroutine directly after a particular token is identified during execution of the routine of  FIG. 3   a . All pixels identified as a neck are marked as “ungrowable.” In decision block  124 , the CPU  12  determines if any of the pixels were marked. 
     If no, the CPU  12  exits the routine of  FIG. 4  and returns to the routine of  FIG. 3   a  (step  126 ). 
     If yes, the CPU  12  proceeds to step  128  and operates to regrow the token from a seed location selected from among the unmarked pixels of the current token, as per the routine of  FIG. 3   a , without changing the counts for seed size and region ID. During the regrowth process, the CPU  12  does not include any pixel previously marked as ungrowable. After the token is regrown, the previously marked pixels are unmarked so that other tokens may grow into them. 
     Subsequent to the regrowth of the token without the previously marked pixels, the CPU  12  returns to step  122  to test the newly regrown token. Neck testing identifies Type C tokens that cross material boundaries, and regrows the identified tokens to provide single material Type C tokens suitable for use in creating Type B tokens. 
       FIG. 3   d  shows Type B tokens generated from the Type C tokens of  FIG. 3   c , according to a feature of the present invention. The present invention provides a novel exemplary technique using log chromaticity clustering, for constructing Type B tokens for an image file  18 . Log chromaticity is a technique for developing an illumination invariant chromaticity space. 
     A method and system for separating illumination and reflectance using a log chromaticity representation is disclosed in U.S. Pat. No. 7,596,266, which is hereby expressly incorporated by reference. The techniques taught in U.S. Pat. No. 7,596,266 can be used to provide illumination invariant log chromaticity representation values for each color of an image, for example, as represented by Type C tokens. Logarithmic values of the color band values of the image pixels are plotted on a log-color space graph. The logarithmic values are then projected to a log-chromaticity projection plane oriented as a function of a bi-illuminant dichromatic reflection model (BIDR model), to provide a log chromaticity value for each pixel, as taught in U.S. Pat. No. 7,596,266. The BIDR Model predicts that differing color measurement values fall within a cylinder in RGB space, from a dark end (in shadow) to a bright end (lit end), along a positive slope, when the color change is due to an illumination change forming a shadow over a single material of a scene depicted in the image. 
       FIG. 5  is a graphic representation of a log color space, bi-illuminant chromaticity plane according to a feature of the invention disclosed in U.S. Pat. No. 7,596,266. The alignment of the chromaticity plane is determined by a vector N, normal to the chromaticity plane, and defined as N=log(Bright vector )−log(Dark vector )=log(1+1/S vector ). The co-ordinates of the plane, u, v can be defined by a projection of the green axis onto the chromaticity plane as the u axis, and the cross product of u and N being defined as the v axis. In our example, each log value for the materials A, B, C is projected onto the chromaticity plane, and will therefore have a corresponding u, v co-ordinate value in the plane that is a chromaticity value, as shown in  FIG. 5 . 
     Thus, according to the technique disclosed in U.S. Pat. No. 7,596,266, the RGB values of each pixel in an image file  18  can be mapped by the CPU  12  from the image file value p(n, m, R, G, B) to a log value, then, through a projection to the chromaticity plane, to the corresponding u, v value, as shown in  FIG. 5 . Each pixel p(n, m, R, G, B) in the image file  18  is then replaced by the CPU  12  by a two dimensional chromaticity value: p(n, m, u, v), to provide a chromaticity representation of the original RGB image. In general, for an N band image, the N color values are replaced by N−1 chromaticity values. The chromaticity representation is a truly accurate illumination invariant representation because the BIDR model upon which the representation is based, accurately and correctly represents the illumination flux that caused the original image. 
     According to a feature of the present invention, log chromaticity values are calculated for each color depicted in an image file  18  input to the CPU  12  for identification of regions of the uniform reflectance (Type B tokens). For example, each pixel of a Type C token will be of approximately the same color value, for example, in terms of RGB values, as all the other constituent pixels of the same Type C token, within the noise level of the equipment used to record the image. Thus, an average of the color values for the constituent pixels of each particular Type C token can be used to represent the color value for the respective Type C token in the log chromaticity analysis. 
       FIG. 6  is a flow chart for determining a list of colors depicted in an input image, for example, an image file  18 . In step  200 , an input image file  18  is input to the CPU  12  for processing. In steps  202  and  204 , the CPU  12  determines the colors depicted in the input image file  18 . In step  202 , the CPU  12  calculates an average color for each Type C token determined by the CPU  12  through execution of the routine of  FIG. 3   a , as described above, for a list of colors. The CPU  12  can be operated to optionally require a minimum token size, in terms of the number of constituent pixels of the token, or a minimum seed size (the N×N array) used to determine Type C tokens according to the routine of  FIG. 3   a , for the analysis. The minimum size requirements are implemented to assure that color measurements in the list of colors for the image are an accurate depiction of color in a scene depicted in the input image, and not an artifact of blend pixels. 
     Blend pixels are pixels between two differently colored regions of an image. If the colors between the two regions are plotted in RGB space, there is a linear transition between the colors, with each blend pixel, moving from one region to the next, being a weighted average of the colors of the two regions. Thus, each blend pixel does not represent a true color of the image. If blend pixels are present, relatively small Type C tokens, consisting of blend pixels, can be identified for areas of an image between two differently colored regions. By requiring a size minimum, the CPU  12  can eliminate tokens consisting of blend pixel from the analysis. 
     In step  204 , the CPU  12  can alternatively collect colors at the pixel level, that is, the RGB values of the pixels of the input image file  18 , as shown in  FIG. 2 . The CPU  12  can be operated to optionally require each pixel of the image file  18  used in the analysis to have a minimum stability or local standard deviation via a filter output, for a more accurate list of colors. For example, second derivative energy can be used to indicate the stability of pixels of an image. 
     In this approach, the CPU  12  calculates a second derivative at each pixel, or a subset of pixels disbursed across the image to cover all illumination conditions of the image depicted in an input image file  18 , using a Difference of Gaussians, Laplacian of Gaussian, or similar filter. The second derivative energy for each pixel examined can then be calculated by the CPU  12  as the average of the absolute value of the second derivative in each color band (or the absolute value of the single value in a grayscale image), the sum of squares of the values of the second derivatives in each color band (or the square of the single value in a grayscale image), the maximum squared second derivative value across the color bands (or the square of the single value in a grayscale image), or any similar method. Upon the calculation of the second derivative energy for each of the pixels, the CPU  12  analyzes the energy values of the pixels. There is an inverse relationship between second derivative energy and pixel stability, the higher the energy, the less stable the corresponding pixel. 
     In step  206 , the CPU  12  outputs a list or lists of color (after executing one or both of steps  202  and/or  204 ). According to a feature of the present invention, all of the further processing can be executed using the list from either step  202  or  204 , or vary the list used (one or the other of the lists from steps  202  or  204 ) at each subsequent step. 
       FIG. 7  is a flow chart for determining an orientation for a log chromaticity representation, according to a feature of the present invention. For example, the CPU  12  determines an orientation for the normal N, for a log chromaticity plane, as shown in  FIG. 5 . In step  210 , the CPU  12  receives a list of colors for an input file  18 , such as a list output in step  206  of the routine of  FIG. 6 . In step  212 , the CPU  12  determines an orientation for a log chromaticity space. 
     As taught in U.S. Pat. No. 7,596,266, and as noted above, alignment of the chromaticity plane is represented by N, N being a vector normal to the chromaticity representation, for example, the chromaticity plane of  FIG. 5 . The orientation is estimated by the CPU  12  thorough execution of any one of several techniques. For example, the CPU  12  can determine estimates based upon entropy minimization, manual selection by a user or the use of a characteristic spectral ratio for an image of an input image file  18 , as fully disclosed in U.S. Pat. No. 7,596,266. 
     For a higher dimensional set of colors, for example, an RYGB space (red, yellow, green, blue), the log chromaticity normal, N, defines a sub-space with one less dimension than the input space. Thus, in the four dimensional RYGB space, the normal N defines a three dimensional log chromaticity space. When the four dimensional RYGB values are projected into the three dimensional log chromaticity space, the projected values within the log chromaticity space are unaffected by illumination variation. 
     In step  214 , the CPU  12  outputs an orientation for the normal N. As illustrated in the example of  FIG. 5 , the normal N defines an orientation for a u, v plane in a three dimensional RGB space. 
       FIG. 8  is a flow chart for determining log chromaticity coordinates for the colors of an input image, as identified in steps  202  or  204  of the routine of  FIG. 6 , according to a feature of the present invention. In step  220 , a list of colors is input to the CPU  12 . The list of colors can comprise either the list generated through execution of step  202  of the routine of  FIG. 6 , or the list generated through execution of step  204 . In step  222 , the log chromaticity orientation for the normal, N, determined through execution of the routine of  FIG. 7 , is also input to the CPU  12 . 
     In step  224 , the CPU  12  operates to calculate a log value for each color in the list of colors and plots the log values in a three dimensional log space at respective (log R, log G, log B) coordinates, as illustrated in  FIG. 5 . Materials A, B and C denote log values for specific colors from the list of colors input to the CPU  12  in step  220 . A log chromaticity plane is also calculated by the CPU  12 , in the three dimensional log space, with u, v coordinates and an orientation set by N, input to the CPU  12  in step  222 . Each u, v coordinate in the log chromaticity plane can also be designated by a corresponding (log R, log G, log B) coordinate in the three dimensional log space. 
     According to a feature of the present invention, the CPU  12  then projects the log values for the colors A, B and C onto the log chromaticity plane to determine a u, v log chromaticity coordinate for each color. Each u, v log chromaticity coordinate can be expressed by the corresponding (log R, log G, log B) coordinate in the three dimensional log space. The CPU  12  outputs a list of the log chromaticity coordinates in step  226 . The list cross-references each color to a u, v log chromaticity coordinate and to the pixels (or a Type C tokens) having the respective color (depending upon the list of colors used in the analysis (either step  202  (tokens) or  204  (pixels))). 
       FIG. 9  is a flow chart for optionally augmenting the log chromaticity coordinates for pixels or Type C tokens with extra dimensions, according to a feature of the present invention. In step  230 , the list of log chromaticity coordinates, determined for the colors of the input image through execution of the routine of  FIG. 8 , is input to the CPU  12 . In step  232 , the CPU  12  accesses the input image file  18 , for use in the augmentation. 
     In step  234 , the CPU  12  optionally operates to augment each log chromaticity coordinate with a tone mapping intensity for each corresponding pixel (or Type C token). The tone mapping intensity is determined using any known tone mapping technique. An augmentation with tone mapping intensity information provides a basis for clustering pixels or tokens that are grouped according to both similar log chromaticity coordinates and similar tone mapping intensities. This improves the accuracy of a clustering step. 
     In step  236 , the CPU  12  optionally operates to augment each log chromaticity coordinate with x, y coordinates for the corresponding pixel (or an average of the x, y coordinates for the constituent pixels of a Type C token) (see  FIG. 2  showing a P ( 1 , 1 ) to P (N, M) pixel arrangement). Thus, a clustering step with x, y coordinate information will provide groups in a spatially limited arrangement, when that characteristic is desired. 
     In each of steps  234  and  236 , the augmented information can, in each case, be weighted by a factor w 1  and w 2 , w 3  respectively, to specify the relative importance and scale of the different dimensions in the augmented coordinates. The weight factors w 1  and w 2 , w 3  are user-specified. Accordingly, the (log R, log G, log B) coordinates for a pixel or Type C token is augmented to (log R, log G, log B, T*w 1 , x*w 2 , y*w 3 ) where T, x and y are the tone mapped intensity, the x coordinate and the y coordinate, respectively. 
     In step  238 , the CPU  12  outputs a list of the augmented coordinates. The augmented log chromaticity coordinates provide accurate illumination invariant representations of the pixels, or for a specified regional arrangement of an input image, such as, for example, Type C tokens. According to a feature of the present invention, the illumination invariant characteristic of the log chromaticity coordinates is relied upon as a basis to identify regions of an image of a single material or reflectance, such as, for example, Type B tokens. 
       FIG. 10  is a flow chart for clustering the log chromaticity coordinates, according to a feature of the present invention. In step  240 , the list of augmented log chromaticity coordinates is input the CPU  12 . In step  242 , the CPU  12  operates to cluster the log chromaticity coordinates. The clustering step can be implemented via, for example, a known k-means clustering. Any known clustering technique can be used to cluster the log chromaticity coordinates to determine groups of similar log chromaticity coordinate values. The CPU  12  correlates each log chromaticity coordinate to the group to which the respective coordinate belongs. The CPU  12  also operates to calculate a center for each group identified in the clustering step. For example, the CPU  12  can determine a center for each group relative to a (log R, log G, log B, log T) space. 
     In step  244 , the CPU  12  outputs a list of the cluster group memberships for the log chromaticity coordinates (cross referenced to either the corresponding pixels or Type C tokens) and/or a list of cluster group centers. 
     As noted above, in the execution of the clustering method, the CPU  12  can use the list of colors from either the list generated through execution of step  202  of the routine of  FIG. 6 , or the list generated through execution of step  204 . In applying the identified cluster groups to an input image, the CPU  12  can be operated to use the same set of colors as used in the clustering method (one of the list of colors corresponding to step  202  or to the list of colors corresponding to step  204 ), or apply a different set of colors (the other of the list of colors corresponding to step  202  or the list of colors corresponding to step  204 ). If a different set of colors is used, the CPU  12  proceeds to execute the routine of  FIG. 11 . 
       FIG. 11  is a flow chart for assigning the log chromaticity coordinates to clusters determined through execution of the routine of  FIG. 10 , when a different list of colors is used after the identification of the cluster groups, according to a feature of the present invention. In step  250 , the CPU  12  once again executes the routine of  FIG. 8 , this time in respect to the new list of colors. For example, if the list of colors generated in step  202  (colors based upon Type C tokens) was used to identify the cluster groups, and the CPU  12  then operates to classify log chromaticity coordinates relative to cluster groups based upon the list of colors generated in step  204  (colors based upon pixels), step  250  of the routine of  FIG. 11  is executed to determine the log chromaticity coordinates for the colors of the pixels in the input image file  18 . 
     In step  252 , the list of cluster centers is input to the CPU  12 . In step  254 , the CPU  12  operates to classify each of the log chromaticity coordinates identified in step  250 , according to the nearest cluster group center. In step  256 , the CPU  12  outputs a list of the cluster group memberships for the log chromaticity coordinates based upon the new list of colors, with a cross reference to either corresponding pixels or Type C tokens, depending upon the list of colors used in step  250  (the list of colors generated in step  202  or the list of colors generated in step  204 ). 
       FIG. 12  is a flow chart for detecting regions of uniform reflectance based on the log chromaticity clustering according to a feature of the present invention. In step  260 , the input image file  18  is once again provided to the CPU  12 . In step  262 , one of the pixels or Type C tokens, depending upon the list of colors used in step  250 , is input to the CPU  12 . In step  264 , the cluster membership information, form either steps  244  or  256 , is input to the CPU  12 . 
     In step  266 , the CPU  12  operates to merge each of the pixels, or specified regions of an input image, such as, for example, Type C tokens, having a same cluster group membership into a single region of the image to represent a region of uniform reflectance (Type B token). The CPU  12  performs such a merge operation for all of the pixels or tokens, as the case may be, for the input image file  18 . In step  268 , the CPU  12  outputs a list of all regions of uniform reflectance (and also of similar tone mapping intensities and x, y coordinates, if the log chromaticity coordinates were augmented in steps  234  and/or  236 ). It should be noted that each region of uniform reflectance (Type B token) determined according to the features of the present invention, potentially has significant illumination variation across the region. 
     U.S. Patent Publication No. US 2010/0142825 teaches a constraint/solver model for segregating illumination and material in an image, including an optimized solution based upon a same material constraint. A same material constraint, as taught in U.S. Patent Publication No. US 2010/0142825, utilizes Type C tokens and Type B tokens, as can be determined according to the teachings of the present invention. The constraining relationship is that all Type C tokens that are part of the same Type B token are constrained to be of the same material. This constraint enforces the definition of a Type B token, that is, a connected image region comprising contiguous pixels that represent a region of the image encompassing a single material in the scene, though not necessarily the maximal region corresponding to that material. Thus, all Type C tokens that lie within the same Type B token are by the definition imposed upon Type B tokens, of the same material, though not necessarily of the same illumination. The Type C tokens are therefore constrained to correspond to observed differences in appearance that are caused by varying illumination. 
     Implementation of the constraint/solver model according to the techniques and teachings of U.S. Patent Publication No. US 2010/0142825, utilizing the Type C tokens and Type B tokens obtained via a log chromaticity clustering technique according to the present invention, provides a highly effective and efficient method for generating intrinsic images corresponding to an original input image. The intrinsic images can be used to enhance the accuracy and efficiency of image processing, image analysis and computer vision applications. 
     In the preceding specification, the invention has been described with reference to specific exemplary embodiments and examples thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative manner rather than a restrictive sense.