A Histogram Matching Scheme for Color Pattern Classification 


Vol. 13,  No. 7, pp. 689-698, Dec.  2006
10.3745/KIPSTB.2006.13.7.689


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  Abstract

Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. Color image consists of various color patterns. And most pattern recognition methods use the information of color which has been trained and extract the feature of the color. This thesis extracts adaptively specific color feature from images with several limited colors. Because the number of the color patterns is limited, the distribution of the color in the image is similar. But, when there are some noises and distortions in the image, its distribution can be various. Therefore we cannot extract specific color regions in the standard image that is well expressed in special color patterns to extract, and special color regions of the image to test. We suggest new method to reduce the error of recognition by extracting the specific color feature adaptively for images with the low distortion, and six test images with some degree of noises and distortion. We consequently found that proposed method shouws more accurate results than those of statistical pattern recognition.

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[IEEE Style]

Y. M. Park and Y. W. Yoon, "A Histogram Matching Scheme for Color Pattern Classification," The KIPS Transactions:PartB , vol. 13, no. 7, pp. 689-698, 2006. DOI: 10.3745/KIPSTB.2006.13.7.689.

[ACM Style]

Young Min Park and Young Woo Yoon. 2006. A Histogram Matching Scheme for Color Pattern Classification. The KIPS Transactions:PartB , 13, 7, (2006), 689-698. DOI: 10.3745/KIPSTB.2006.13.7.689.

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