Abstract:
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, augmenting a preselected area of the image in a manner to decrease a number of token regions representing the preselected area, and identifying token regions in the augmented 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 and effectiveness of computer performance. Significant pioneer inventions related to the illumination and material aspects of an image are disclosed in U.S. Pat. No. 7,873,219 to Richard Mark Friedhoff, entitled Differentiation Of Illumination And Reflection Boundaries and U.S. Pat. No. 7,672,530 to Richard Mark Friedhoff et al., entitled Method And System For Identifying Illumination Flux In An Image (hereinafter the Friedhoff patents). 
     SUMMARY OF THE INVENTION 
     The present invention provides an improvement and enhancement to the fundamental teachings of the Friedhoff patents, and includes a method and system comprising image techniques that accurately and correctly identify intrinsic images corresponding to an original images of a scene. The intrinsic images include an illumination image and a material reflectance 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, augmenting a preselected area of the image in a manner to decrease a number of token regions representing the preselected area, and identifying token regions in the augmented image. 
     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 augment a preselected area of the image in a manner to decrease a number of token regions representing the preselected area, and identify token regions in the augmented image. 
     In a third exemplary embodiment of the present invention, a computer program product is provided. According to a feature of the present invention, the computer program product is disposed on a computer readable media, and the product includes computer executable process steps operable to control a computer to: provide an image file depicting an image, in a computer memory, augment a preselected area of the image in a manner to decrease a number of token regions representing the preselected area, and identify token regions in the augmented image. 
     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 programed 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  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. 4   a  is an original image used as an example in the identification of Type C tokens. 
         FIG. 4   b  shows Type C token regions in the image of  FIG. 4   a.    
         FIG. 5  shows a mask arrangement for identifying an important area of an image, in an example of a face. 
         FIG. 6  is a flow chart for expediting image processing according to an exemplary embodiment 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, for example, a set of color bands, for example, red, green and blue color components (RGB) of the picture element, or a single grayscale value. The present invention is applicable to any multi-band image, where each band corresponds to a piece of the electro-magnetic spectrum, or to a single grayscale image depiction. 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). Thus, each pixel can be uniquely identified by p(r,g,b,x,y) wherein the r,g,b values provide the pixel color, and x,y the position within the n×m pixel array of  FIG. 2 . 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, as disclosed in the Friedhoff patents, 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. 
     According to a feature of the present invention, regions of an image are defined as tokens. 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 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. 
     Type C tokens can be readily identified in an image, according to a feature of the present invention, and then analyzed and processed to construct Type B tokens, as will be described below.  FIG. 3  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. 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. 
     In an exemplary embodiment of the present invention, the color values comparison can be with respect to multiple color band values (RGB in our example) of the selected pixel. For example, the Euclidean distance between the selected pixel and a neighboring pixel in the N×N array, in a log color space, relative to a threshold value. In the exemplary embodiment, for pixels P 1  and P 2 , the distance D is defined as follows:
 
 D =square root(( R   1   −R   2 ) 2 +( G   1   −G   2 ) 2 +( B   1   −B   2 ) 2 ))
 
then: D≦threshold value
 
     According to a feature of the present invention, the threshold value is set as a function of the location of the N×N array within the image being analyzed. For example, in a face recognition process, certain areas of the image, such as the skin of a face, are considered more important than other areas of the image, such as the eyes and background areas of the image. Thus, a more precise analysis of the more important areas of an image can be supported by a tighter threshold value for pixel comparison, in the important areas, to provide a more discriminative token identification in the important areas. For less important areas of the image, looser threshold values are used, resulting in less precise and larger, less discriminative Type C tokens. 
     If the comparison does not result in approximately equal values (within the noise levels of the recording device, as represented by the threshold value) 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. 
     According to a further feature of the present invention, a comparison for good neighbors of a pixel from the queue (step  118 ) is made as a function of a Euclidean distance relative to a second threshold value, different from the threshold value used in the seed analysis of step  104 . Again, the threshold values used in step  118  vary according to the importance of the area of the image where the tokenization is being performed, for example, as set during the performance of step  104 . Thus there is a seed threshold value, for use during performance of step  104 , and a grow threshold, for use during performance of step  118 . 
       FIG. 5  shows a mask arrangement for identifying an important area of an image, for example, a face. A known eye locator can be used to locate eyes in an image including a human face. A simple oval-shaped mask is then generated around the eye locations to generally conform to what should correspond (determined empirically) to skin regions of the face. An example for the seed threshold and grow threshold values used in pixel color comparisons in a tokenization process can be as follows:
     Face region (for pixels within the oval mask):   Seed Threshold: 0.10   Grow Threshold: 0.15   Eye Region (for pixels for the identified eyes, cut out from the mask):   Seed Threshold: 0.15   Grow Threshold: 0.25   Background Region (for pixels in the image outside the mask):   Seed Threshold: 0.20   Grow Threshold: 0.35   

     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 , or completion of a region map that coincides with the image, the routine will have completed the token building task.  FIG. 4   a  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. 4   b  shows token regions corresponding to the region map, for example, as identified through execution of the routine of  FIG. 3  (Type C tokens), in respect to the image of  FIG. 4   a . The token regions are color coded to illustrate the token makeup of the image of  FIG. 4   a , including penumbra regions between the full color blue and teal areas of the image and the shadow of the colored areas. 
     In accordance with yet another feature of the present invention, the overall time for execution of an image process is decreased by processing selected areas of the image, for example, the less important regions outside the face mask arrangement of  FIG. 5 , in a manner to augment the image so as to decrease the number of, for example, Type C tokens corresponding to the selected areas. A lower number of token regions over a preselected area of the image results in a decrease in the overall CPU execution time for carrying out routines such as, for example, a constraint/solver model for segregating illumination and material in an image, for example, as taught in U.S. Patent Publication No. US 2010/0142825. 
     For example, the regions outside the face mask are blurred to lessen the resolution of the image in those areas of the image. Accordingly, the number of tokens corresponding to the selected blurred areas will be less than if the image remained un-blurred. 
     In an exemplary embodiment of the present invention, the CPU  12  operates to dilate and blur the face mask ( FIG. 5 ). The CPU  12  then utilizes the blurred face mask as an alpha mask. To that end, the CPU  12  accesses an image file  18  to be tokenized, and generates a blurred version of the image file  18 . The CPU  12  then merges the original image of the image file  18  with the blurred version of the image using the blurred, dilated mask as the alpha. The output of the merge is an image, with the areas of the image corresponding to the areas covered by the mask being in focus, as in the original image, and the areas of the output image outside of the mask being blurred. 
     According to the exemplary embodiment of the present invention, the CPU  12  executes the routine of  FIG. 3 , utilizing an augmented image such as the partially blurred image generated in the merge described above, to tokenize the image. Due to the partial blurring, the total execution time for an overall image process can be significantly reduced because of the reduced number of tokens identified in the lower resolution blurred areas of the image. 
     For example, as noted above, U.S. Patent Publication No. US 2010/0142825 teaches a constraint/solver model for segregating illumination and material in an image, including, for example, 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, as can be determined according to the teachings of the present invention, and Type B tokens, as identified according to the teachings of U.S. Patent Publication No. US 2010/0142825. 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 Type C tokens and Type B tokens, 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. Moreover, the overall processing time for execution of the image segregation operation is reduced when utilizing an augmented image such as the partially blurred image generated in the merge described above, to tokenize the image, due to the reduced number of token regions that need to be processed by the CPU  12 . 
       FIG. 6  is a flow chart for expediting image processing according to an exemplary embodiment of the present invention. In step  600 , the CPU  12  receives as an input an image to be processed, with preselected areas designated for low-resolution illumination. The areas selected for low resolution can be designated by a mask, as described above. The criteria for selecting certain regions includes, for example, situations when the accuracy of the illumination analysis is not important, such as in a face recognition application, regions outside the face are not critical to the face recognition function. Another example would be when a user has prior knowledge that illumination varies slowly in certain regions of an image, for example, regions with low gradient content. 
     In step  604 , the CPU  12  operates to blur the regions selected for low resolution illumination, to provide a partially blurred image. In step  608 , the CPU  12  performs a tokenization process on the partially blurred image, such that the number of tokens in the preselected regions is reduced, pursuant to the teachings of the present invention. In step  616 , the CPU  12  performs an image segregation operation, for example, pursuant to the techniques and teachings of U.S. Patent Publication No. US 2010/0142825, to generate initial material reflectance and illumination intrinsic images corresponding to the partially blurred image. 
     In step  620 , the CPU  12  operates to re-calculate the intrinsic material reflectance image. Each of the initial material reflectance and illumination intrinsic images generated in the method according to an exemplary embodiment of the present invention, is at a high resolution in the non-blurred regions of the image and at a low resolution in the preselected blurred regions. Since the criteria for implementing the blurring technique in the exemplary embodiment of the present invention, is based upon either prior knowledge that illumination varies slowly in certain regions of an image, or that the accuracy of the illumination analysis is not important in the selected regions, such as in a face recognition application, regions outside the face, the low resolution in the illumination image is not critical to further processing. 
     However, a user may still want a full, high resolution material reflectance image. Thus, in step  620 , the CPU  12  executes a calculation based upon an image=material*illumination relationship, to re-calculate the material reflectance image. According to the exemplary embodiment of the present invention, the CPU  12  uses the original image and divides the original image by the intrinsic illumination image, to obtain a re-calculated material reflectance image that is clear and sharp across the entire image. 
     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.