Texture Images Segmentation by Combination of Moment & Homogeneity Features 


Vol. 7,  No. 11, pp. 3592-3602, Nov.  2000
10.3745/KIPSTE.2000.7.11.3592


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  Abstract

Image processing consist of image analysis and classification. The one is extracting of feature value in the image. The other is segmentation of image that have same property. A novel approach for the analysis and classification of texture images based on statistical texture-primitive extraction are proposed. In this approach, feature vector extracting is based on statistical method using spatial dependence of grey level and use general texture property. It is advantageous that not effected on structure and type of texture. These components describe the amount of roughness and softness of texture images. Two features, Moment and Homogeneity, are computed from GLCM(gray level co-occurrence matrices) of the texture primitive to characterize statistical properties of the image. We show the successful experimental results by consideration of these two components for the analysis and classification to regular and irregular texture images.

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[IEEE Style]

M. J. Mo, J. S. Lim, W. B. Lee, W. H. Kim, "Texture Images Segmentation by Combination of Moment & Homogeneity Features," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 11, pp. 3592-3602, 2000. DOI: 10.3745/KIPSTE.2000.7.11.3592.

[ACM Style]

Moon Jung Mo, Jong Seok Lim, Woo Beom Lee, and Wook Hyun Kim. 2000. Texture Images Segmentation by Combination of Moment & Homogeneity Features. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 11, (2000), 3592-3602. DOI: 10.3745/KIPSTE.2000.7.11.3592.

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