Colour Texture Classification Using Anisotropic Diffusion and Wavelet Transform

This chapter proposes the framework for computer vision algorithm for industrial application. The proposed framework uses wavelet transform to obtain the multiresolution images. Anisotropic diffusion is employed to obtain the texture component. Various feature sets and their combinations are considered obtained from texture component. Linear discriminant analysis is employed to get the distinguished features. The k-NN classifier is used for classification. The proposed method is experimented on benchmark datasets for texture classification. Further, the method is extended to exploration of different color spaces for finding reference standard. The thrust area of industrial applications for machine intelligence in computer vision is considered. The industrial datasets, namely, MondialMarmi dataset for granite tiles and Parquet dataset for wood textures are experimented. It was observed that the combination of features performs better in YCbCr and HSV color spaces for MondialMarmi and Parquet datasets as compared to the other methods in literature.

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Colour Texture Classification Using Anisotropic Diffusion and Wavelet Transform

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LDA Based Discriminant Features for Texture Classification Using WT and PDE Approach

LDA Based Discriminant Features for Texture Classification Using WT and PDE Approach

Anisotropic Diffusion-Based Color Texture Analysis for Industrial Application

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