# US 2013/0156301 A1 · Method and system for recognizing images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2013/0156301 A1 - Document ID: US20130156301A1 - Status: Published application - Kind: A1 - Application number: 13/431,993 - Filed: 2012-03-28 - Published: 2013-06-20 - Earliest claimed: 2011-12-19 - Assignees: Industrial Technology Research Institute - Inventors: Chung-Chia Kang; Jian-Ren Chen; Chih-Wei Chen; Leii H. Chang - Web page: https://patentgrep.com/patent/US20130156301A1 ## CPC - G06V 40/168: {Feature extraction; Face representation} - G06F 18/28: Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries - G06V 10/772: Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method and a system for recognizing at least one testing image according to classes are provided, wherein each of the classes includes sample images. The method includes generating an average image of each class according to the sample images, generating a feature enhancement mask according to differences between the average images of the classes, enhancing the sample images of each class by using the feature enhancement mask to generate a plurality of enhanced sample images corresponding to each class, and training a classifier according to the enhanced sample images of each class. The method also includes enhancing the at least one testing image by using the feature enhancement mask to generate an enhanced testing image, classifying the enhanced testing image into one of the classes by using the classifier, and recognizing that the testing image belongs to the classified class. Thereby, this method can effectively recognize the testing image. ## Claims ### Claim 1 An image recognizing method, recognizing at least one testing image according to a plurality of classes, wherein each of the classes comprises a plurality of sample images, the image recognizing method comprising: generating an average image of each of the classes according to the sample images; generating a feature enhancement mask according to differences between the average images of the classes; enhancing the sample images of each of the classes by using the feature enhancement mask to generate a plurality of enhanced sample images corresponding to the class; training a classifier according to the enhanced sample images corresponding to each of the classes; enhancing the at least one testing image by using the feature enhancement mask to generate at least one enhanced testing image; classifying the at least one enhanced testing image into one of the classes by using the classifier; and recognizing that the at least one testing image belongs to the one of the classes. ### Claim 2 (depends on 1) The image recognizing method according to claim 1, wherein each of the sample images has a plurality of pixels arranged into an array, each of the pixels is corresponding to a pixel value, and the average image of each of the classes has a plurality of average pixel values corresponding to the pixels of the sample images, wherein the average pixel value in an ith column and a jth row of the average image of the mth class is obtained by dividing a sum of the pixel values corresponding to the pixels in the ith columns and the jth rows of the sample images of the mth class by the number of the sample images of the mth class. ### Claim 3 (depends on 2) The image recognizing method according to claim 2, wherein the feature enhancement mask has a plurality of feature enhancement values arranged into the array, and the feature enhancement values are obtained according to a plurality of absolute differences between the average pixel values of the average images of the classes. ### Claim 4 (depends on 3) The image recognizing method according to claim 3, wherein a number of the classes is 2, wherein the feature enhancement value in an ith column and a jth row of the feature enhancement mask is obtained by dividing the absolute difference in the ith column and the jth row among the absolute differences by a maximum absolute difference among the absolute differences, wherein the absolute difference in the ith column and the jth row among the absolute differences is equal to an absolute value of a difference between the average pixel value in the ith column and the jth row of the average image of the 1st class and the average pixel value in the ith column and the jth row of the average image of the 2nd class. ### Claim 5 (depends on 3) The image recognizing method according to claim 3, wherein a number of the classes is greater than 2, wherein the feature enhancement value in an ith column and a jth row of the feature enhancement mask is obtained by dividing the absolute difference in the ith column and the jth row among the absolute differences by a maximum absolute difference among the absolute differences, wherein the absolute difference in the ith column and the jth row among the absolute differences is equal to an absolute value of a difference between a maximum average pixel value among the average pixel values in the ith column and the jth row of the average images of the classes and a minimum average pixel value among the average pixel values in the ith column and the jth row of the average images of the classes. ### Claim 6 (depends on 4) The image recognizing method according to claim 4, wherein each of the enhanced sample images has a plurality of pixels arranged into the array, and each of the pixels of each of the enhanced sample images is corresponding to a pixel value; wherein the pixel value corresponding to the pixel in an ith column and a jth row of the kth enhanced sample image of the mth class is obtained by multiplying the feature enhancement value in an ith column and a jth row of the feature enhancement mask by the pixel value corresponding to the pixel in an ith column and a jth row of the kth sample image of the mth class. ### Claim 7 (depends on 5) The image recognizing method according to claim 5, wherein each of the enhanced sample images has a plurality of pixels arranged into the array, and each of the pixels of each of the enhanced sample images is corresponding to a pixel value; wherein the pixel value corresponding to the pixel in an ith column and a jth row of the kth enhanced sample image of the mth class is obtained by multiplying the feature enhancement value in an ith column and a jth row of the feature enhancement mask by the pixel value corresponding to the pixel in an ith column and a jth row of the kth sample image of the mth class. ### Claim 8 (depends on 1) The image recognizing method according to claim 1, wherein the step of training the classifier according to the enhanced sample images corresponding to each of the classes comprises: training the classifier by using a K-means-clustering algorithm, a linear discriminant analysis (LDA), an adaptive boosting (AdaBoost) algorithm, a neural network, or a support vector machine (SVM). ### Claim 9 (depends on 4) The image recognizing method according to claim 4, wherein the enhanced testing image has a plurality of pixels arranged into the array, and each of the pixels of the enhanced testing image is corresponding to a pixel value, wherein the pixel value corresponding to the pixel in an ith column and a jth row of the enhanced testing image is obtained by multiplying the feature enhancement value in the ith column and the jth row of the feature enhancement mask by the pixel value corresponding to the pixel in the ith column and the jth row of the corresponding testing image. ### Claim 10 (depends on 5) The image recognizing method according to claim 5, wherein the enhanced testing image has a plurality of pixels arranged into the array, and each of the pixels of the enhanced testing image is corresponding to a pixel value, wherein the pixel value corresponding to the pixel in an ith column and a jth row of the enhanced testing image is obtained by multiplying the feature enhancement value in the ith column and the jth row of the feature enhancement mask by the pixel value corresponding to the pixel in the ith column and the jth row of the corresponding testing image. ### Claim 11 (depends on 10) The image recognizing method according to claim 10, further comprising: performing a histogram equalization on the sample images and the testing image. ### Claim 12 (depends on 11) The image recognizing method according to claim 11, further comprising: reducing a dimension of each of the enhanced sample images through a principal components analysis (PCA) and a LDA; and reducing a dimension of the enhanced testing image through the PCA and the LDA. ### Claim 13 An image recognizing system, recognizing at least one testing image according to a plurality of classes, wherein each of the classes comprises a plurality of sample images, the image recognizing system comprising: an image capturing unit, capturing the at least one testing image; an image recognizing unit, coupled to the image capturing unit; and a classifier training unit, coupled to the image capturing unit and the image recognizing unit; wherein the classifier training unit generates an average image of each of the classes according to the sample images; wherein the classifier training unit generates a feature enhancement mask according to differences between the average images of the classes, wherein the classifier training unit enhances the sample images of each of the classes by using the feature enhancement mask to generate a plurality of enhanced sample images corresponding to the class, wherein the classifier training unit trains a classifier according to the enhanced sample images corresponding to each of the classes, wherein the image recognizing unit enhances the at least one testing image by using the feature enhancement mask to generate at least one enhanced testing image, wherein the image recognizing unit classifies the at least one enhanced testing image into one of the classes by using the classifier; wherein the image recognizing unit recognizes that the at least one testing image which is belonged to the one of the classes. ### Claim 14 (depends on 13) The image recognizing system according to claim 13, wherein each of the sample images has a plurality of pixels arranged into an array, each of the pixels is corresponding to a pixel value, and the average image of each of the classes has a plurality of average pixel values corresponding to the pixels of the sample images, wherein the average pixel value in an ith column and a jth row of the average image of the mth class is obtained by dividing a sum of the pixel values corresponding to the pixels in the ith columns and the jth rows of the sample images of the mth class by the number of the sample images of the mth class. ### Claim 15 (depends on 14) The image recognizing system according to claim 14, wherein the feature enhancement mask has a plurality of feature enhancement values arranged into the array, and the feature enhancement values are obtained according to a plurality of absolute differences between the average pixel values of the average images of the classes. ### Claim 16 (depends on 15) The image recognizing system according to claim 15, wherein a number of the classes is 2, wherein the feature enhancement value in an ith column and a jth row of the feature enhancement mask is obtained by dividing the absolute difference in the ith column and the jth row among the absolute differences by a maximum absolute difference among the absolute differences, wherein the absolute difference in the ith column and the jth row among the absolute differences is equal to an absolute value of a difference between the average pixel value in the ith column and the jth row of the average image of the 1st class and the average pixel value in the ith column and the jth row of the average image of the 2nd class. ### Claim 17 (depends on 15) The image recognizing system according to claim 15, wherein a number of the classes is greater than 2, wherein the feature enhancement value in an ith column and a jth row of the feature enhancement mask is obtained by dividing the absolute difference in the ith column and the jth row among the absolute differences by a maximum absolute difference among the absolute differences, wherein the absolute difference in the ith column and the jth row among the absolute differences is equal to an absolute value of a difference between a maximum average pixel value among the average pixel values in the ith column and the jth row of the average images of the classes and a minimum average pixel value among the average pixel values in the ith column and the jth row of the average images of the classes. ### Claim 18 (depends on 16) The image recognizing system according to claim 16, wherein each of the enhanced sample images has a plurality of pixels arranged into the array, and each of the pixels of each of the enhanced sample images is corresponding to a pixel value, wherein the pixel value corresponding to the pixel in an ith column and a jth row of the kth enhanced sample image of the mth class is obtained by multiplying the feature enhancement value in an ith column and a jth row of the feature enhancement mask by the pixel value corresponding to the pixel in an ith column and a jth row of the kth sample image of the mth class. ### Claim 19 (depends on 17) The image recognizing system according to claim 17, wherein each of the enhanced sample images has a plurality of pixels arranged into the array, and each of the pixels of each of the enhanced sample images is corresponding to a pixel value, wherein the pixel value corresponding to the pixel in an ith column and a jth row of the kth enhanced sample image of the mth class is obtained by multiplying the feature enhancement value in an ith column and a jth row of the feature enhancement mask by the pixel value corresponding to the pixel in an ith column and a jth row of the kth sample image of the mth class. ### Claim 20 (depends on 13) The image recognizing system according to claim 13, wherein the classifier training unit trains the classifier by using a K-means-clustering algorithm, a LDA, an AdaBoost algorithm, a neural network, or a SVM. ### Claim 21 (depends on 16) The image recognizing system according to claim 16, wherein the enhanced testing image has a plurality of pixels arranged into the array, and each of the pixels of the enhanced testing image is corresponding to a pixel value, wherein the pixel value corresponding to the pixel in an ith column and a jth row of the enhanced testing image is obtained by multiplying the feature enhancement value in the ith column and the jth row of the feature enhancement mask by the pixel value corresponding to the pixel in the ith column and the jth row of the corresponding testing image. ### Claim 22 (depends on 17) The image recognizing system according to claim 17, wherein the enhanced testing image has a plurality of pixels arranged into the array, and each of the pixels of the enhanced testing image is corresponding to a pixel value, wherein the pixel value corresponding to the pixel in an ith column and a jth row of the enhanced testing image is obtained by multiplying the feature enhancement value in the ith column and the jth row of the feature enhancement mask by the pixel value corresponding to the pixel in the ith column and the jth row of the corresponding testing image. ### Claim 23 (depends on 22) The image recognizing system according to claim 22, wherein the image recognizing system further comprises a storage unit coupled to the classifier training unit and the image recognizing unit, wherein the storage unit stores the classifier and the feature enhancement mask. ### Claim 24 (depends on 23) The image recognizing system according to claim 23 further comprising a normalization unit coupled to the image capturing unit, the classifier training unit, and the image recognizing unit, wherein the normalization unit performs a histogram equalization on the sample images and the testing image. ### Claim 25 (depends on 24) The image recognizing system according to claim 24, wherein the normalization unit reduces a dimension of each of the enhanced sample images through a PCA and a LDA; and the normalization unit reduces a dimension of the enhanced testing image through the PCA and the LDA.