Industrial applications of colour texture classification based on anisotropic diffusion

Abstract

A novel method of colour texture classification based on anisotropic diffusion is proposed and is investigated with different colour spaces. The objective is to explore the colour spaces for their suitability in automatic classification of certain textures in industrial applications, namely, granite tiles and wood textures, using computer vision. The directional subbands of digital image of material samples are obtained using wavelet transform. The anisotropic diffusion is employed to obtain the texture components of directional subbands. Further, statistical features are extracted from the texture components. The linear discriminant analysis (LDA) is employed on feature space to achieve class separability. The proposed method has been experimented on RGB, HSV, YCbCr and Lab colour spaces. The k-NN classifier is used for texture classification. For experimentation, image samples from MondialMarmi database of granite tiles and Parquet database of hard wood are considered. The experimental results are encouraging due to reduced time complexity, reduced feature set size and improved classification accuracy as compared to the state-of-the-art-methods. © Springer Nature Singapore Pte Ltd. 2017.