Facilitating anomaly detection for a product having a pattern

Abstract

A method for facilitating detection of at least one anomaly in a representation of a product having a pattern is provided. The method involves causing at least one processor to receive image data representing the product during processing, identify from the image data generally similar images representing respective instances of a repeated aspect of the pattern, each of the images including image element values, generate a set of corresponding image element values including an image element value from each image, identify at least one image element value from the set of corresponding image element values to be excluded from a subset of the set of corresponding image element values, generate at least one criterion based on the subset, and cause the at least one criterion to be used to facilitate identification of the at least one anomaly. Other methods, apparatuses, systems, and computer readable media are also provided.

What is claimed: 
     
         1 . A method for facilitating detection of at least one anomaly in a representation of a product having a pattern, the method comprising:
 causing at least one processor to receive image data representing the product during processing of the product;   causing the at least one processor to identify from the image data generally similar images representing respective instances of a repeated aspect of the pattern, each of the images including image element values;   causing the at least one processor to generate a set of corresponding image element values, said set including an image element value from each of the images;   causing the at least one processor to identify at least one image element value from the set of corresponding image element values to be excluded from a subset of the set of corresponding image element values;   causing the at least one processor to generate at least one criterion based on the subset of the set of corresponding image element values; and   causing the at least one processor to cause the at least one criterion to be used to facilitate identification of the at least one anomaly.   
     
     
         2 . The method of  claim 1  wherein causing the at least one processor to cause the at least one criterion to be used to facilitate identification of the at least one anomaly comprises:
 causing the at least one processor to receive image data representing a subject image representing at least a portion of an instance of the repeated aspect of the pattern; 
 causing the at least one processor to apply the at least one criterion to a subject image element value of the subject image, the subject image element value corresponding to the subset of corresponding image element values. 
 
     
     
         3 . The method of  claim 2  wherein causing the at least one processor to generate the at least one criterion comprises causing the at least one processor to determine an upper limit value from the subset of the set of corresponding image element values and wherein causing the at least one processor to apply the at least one criterion comprises causing the at least one processor to compare the subject image element value to the upper limit value. 
     
     
         4 . The method of  claim 2  wherein causing the at least one processor to generate the at least one criterion comprises causing the at least one processor to determine a lower limit value from the subset of the set of corresponding image element values and wherein causing the at least one processor to apply the at least one criterion comprises causing the at least one processor to compare the subject image element value to the lower limit value. 
     
     
         5 . The method of  claim 2  wherein causing the at least one processor to generate the at least one criterion comprises causing the at least one processor to determine an upper limit value and a lower limit value from the subset of the set of corresponding image element values and wherein causing the at least one processor to apply the at least one criterion comprises causing the at least one processor to compare the subject image element value to the upper limit value and the lower limit value. 
     
     
         6 . The method of  claim 5  wherein causing the at least one processor to determine the upper limit value and the lower limit value comprises causing the at least one processor to set the upper limit value and the lower limit value to a greatest value and a lowest value respectively of the subset of the set of corresponding image element values. 
     
     
         7 . The method of  claim 5  wherein causing the at least one processor to determine the upper limit value and the lower limit value comprises causing the at least one processor to determine a standard deviation and a mean from the subset of the set of corresponding image element values and to derive the upper limit value and the lower limit value from the standard deviation and the mean. 
     
     
         8 . The method of  claim 1  further comprising causing the at least one processor to produce signals for causing at least one display to display a representation of the at least one anomaly to a user. 
     
     
         9 . The method of  claim 1  wherein causing the at least one processor to identify the at least one image element value to be excluded from the subset comprises causing the at least one processor to identify at least one extreme image element value as the at least one image element value to be excluded. 
     
     
         10 . The method of  claim 9  wherein the set of corresponding image element values comprise pixel values and wherein causing the at least one processor to identify the at least one extreme image element value to be excluded comprises causing the at least one processor to identify at least one lowest pixel value of the pixel values to be excluded and at least one greatest pixel value of the pixel values to be excluded. 
     
     
         11 . The method of  claim 10  wherein causing the at least one processor to identify the at least one lowest pixel value and the at least one greatest pixel value comprises causing the at least one processor to identify a first percentage of the pixel values having lowest values as the at least one lowest pixel value and to identify a second percentage of the pixel values having greatest values as the at least one greatest pixel value. 
     
     
         12 . The method of  claim 11  wherein the first and second percentages are each between 5% and 15%. 
     
     
         13 . A system for facilitating detection of at least one anomaly in a representation of a product having a pattern, the system comprising:
 means for receiving image data representing the product during processing of the product;   means for identifying from the image data generally similar images representing respective instances of a repeated aspect of the pattern, each of the images including image element values;   means for generating a set of corresponding image element values, said set including an image element value from each of the images;   means for identifying at least one image element value from the set of corresponding image element values to be excluded from a subset of the set of corresponding image element values;   means for generating at least one criterion based on the subset of the set of corresponding image element values; and   means for causing the at least one criterion to be used to facilitate identification of the at least one anomaly.   
     
     
         14 . An apparatus for facilitating detection of at least one anomaly in a representation of a product having a pattern, the apparatus comprising at least one processor configured to:
 receive image data representing the product during processing of the product;   identify from the image data generally similar images representing respective instances of a repeated aspect of the pattern, each of the images including image element values;   generate a set of corresponding image element values, said set including an image element value from each of the images;   identify at least one image element value from the set of corresponding image element values to be excluded from a subset of the set of corresponding image element values;   generate at least one criterion based on the subset of the set of corresponding image element values; and   cause the at least one criterion to be used to facilitate identification of the at least one anomaly.   
     
     
         15 . The apparatus of  claim 14  wherein the at least one processor is configured to:
 receive image data representing a subject image representing at least a portion of an instance of the repeated aspect of the pattern; 
 apply the at least one criterion to a subject image element value of the subject image, the subject image element value corresponding to the subset of corresponding image element values. 
 
     
     
         16 . The apparatus of  claim 15  wherein the at least one processor is configured to:
 determine an upper limit value from the subset of the set of corresponding image element values; and 
 compare the subject image element value to the upper limit value. 
 
     
     
         17 . The apparatus of  claim 15  wherein the at least one processor is configured to:
 determine a lower limit value from the subset of the set of corresponding image element values; and 
 compare the subject image element value to the lower limit value. 
 
     
     
         18 . The apparatus of  claim 15  wherein the at least one processor is configured to:
 determine an upper limit value and a lower limit value from the subset of the set of corresponding image element values; and 
 compare the subject image element value to the upper limit value and the lower limit value. 
 
     
     
         19 . The apparatus of  claim 18  wherein the at least one processor is configured to set the upper limit value and the lower limit value to a greatest value and a lowest value respectively of the subset of the set of corresponding image element values. 
     
     
         20 . The apparatus of  claim 18  wherein the at least one processor is configured to determine a standard deviation and a mean from the subset of the set of corresponding image element values and to derive the upper limit value and the lower limit value from the standard deviation and the mean. 
     
     
         21 . The apparatus of  claim 14  further comprising at least one display and wherein the at least one processor is configured to produce signals for causing the at least one display to display a representation of the at least one anomaly to a user. 
     
     
         22 . The apparatus of  claim 14  wherein the at least one processor is configured to identify at least one extreme image element value as the at least one image element value to be excluded. 
     
     
         23 . The apparatus of  claim 22  wherein the set of corresponding image element values comprise pixel values and wherein the at least one processor is configured to identify at least one lowest pixel value of the pixel values to be excluded and at least one greatest pixel value of the pixel values to be excluded. 
     
     
         24 . The apparatus of  claim 23  wherein the at least one processor is configured to identify a first percentage of the pixel values having lowest values as the at least one lowest pixel value and to identify a second percentage of the pixel values having greatest values as the at least one greatest pixel value. 
     
     
         25 . The apparatus of  claim 24  wherein the first and second percentages are each between 5% and 15%. 
     
     
         26 . A non-transitory computer readable medium having stored thereon codes which, when executed by at least one processor, cause the at least one processor to:
 receive image data representing the product during processing of the product;   identify from the image data generally similar images representing respective instances of a repeated aspect of the pattern, each of the images including image element values;   generate a set of corresponding image element values, said set including an image element value from each of the images;   identify at least one image element value from the set of corresponding image element values to be excluded from a subset of the set of corresponding image element values;   generate at least one criterion based on the subset of the set of corresponding image element values; and   cause the at least one criterion to be used to facilitate identification of the at least one anomaly.