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International Journal of Mechanical and Industrial Engineering

Abstract

Textures play important roles in many image processing applications, since images of real objects often do not exhibit regions of uniform and smooth intensities, but variations of intensities with certain repeated structures or patterns, referred to as visual texture. The textural patterns or structures mainly result from the physical surface properties, such as roughness or oriented structured of a tactile quality. It is widely recognized that a visual texture, which can easily perceive, is very difficult to define. The difficulty results mainly from the fact that different people can define textures in applications dependent ways or with different perceptual motivations, and they are not generally agreed upon single definition of texture [1]. The development in multi-resolution analysis such as Gabor and wavelet transform help to overcome this difficulty. In this paper it describes that, texture classification using Wavelet Statistical Features (WSF), Wavelet Co-occurrence Features (WCF) and a combination of wavelet statistical features and co-occurrence features of wavelet transformed images with different feature databases can results better [2]. Several Image degrading parameters are introduced in the image to be classified for verifying the features. Wavelet based decomposing is used to classify the image with code prepared in MATLAB.

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