Texture Analysis: Representation and Matching

Texture has found many applications in computer vision. Examples where texture analysis methods are being used include: (i) classifying images and browsing images based on their texture; (ii) segmenting an input image into regions of homogeneous texture; (iii) extracting surface shape information from ‘texture gradient’; and (iv) synthesizing textures that resemble natural images for various computer graphics applications. Image texture is characterized by the gray value or color ‘pattern’ in a neighborhood surrounding the pixel. Different methods of texture analysis capture this gray-level pattern by extracting textural features in a localized input region. Practical texture-based image processing methods define texture in a manner that is most appropriate for achieving a given goal and ignore the issue whether the input image really contains any texture. This paper describes attempts to learn ‘optimal’ texture discrimination masks using neural networks.

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