A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection

A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection

S. Hemalatha (School of Information Technology and Engineering, VIT University, Vellore, India) and S. Margret Anouncia (School of Computer Science and Engineering, VIT University, Vellore, India)
Copyright: © 2016 |Pages: 21
DOI: 10.4018/IJACI.2016070105
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This paper is dedicated to the modelling of textured images influenced by fractional derivatives for texture detection. As most of the images contain textures, texture analysis becomes the most important for image understanding and it is a key solution for many computer vision applications. Hence, texture must be suitably detected and represented. Nevertheless, existing texture detection algorithms consider texture as a unique feature from edges. The proposed model explores a novel way of developing texture detection algorithm by mimicking edge detection algorithms. The method assumes that texture feature is analogous to edges and thus, the time complexity is reduced significantly. The model proposed in this work is based on Gaussian kernel smoothing, Fractional partial derivatives and a statistical approach. It is justified to be robust to noisy images and possesses statistical interpretation. The model is validated by the classification experiments on different types of textured images from Brodatz album. It achieves higher classification accuracy than the existing methods.
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1. Introduction

Texture analysis is very much important in a wide range of image processing applications from medical imaging, remote sensing to defect detection. It has been an active research area in the field of computer vision for more than two decades. It is found to be very hard because one is usually not aware of the number and type of texture classes that an image may contain. Many techniques have been proposed in the literature for texture analysis (Haralick, 1973; He, 1990; Chang, 1993; Yoshimura, 1997; Van de Wouwer, 1999; Vese, 2003; Targhi, 2006; Liao, 2009; Li, 2010; Chakraborty, 2012; Karthikeyan, 2012; Zingman, 2013). The ultimate aim is to detect, characterize and represent texture feature in images. But, most of the texture analysis techniques end up with texture segmentation (Chen, 1995; Ojala, 2001; Li C. T., 2003; Liu, 2006; Ranjan, 2014). Only a few techniques perform texture detection, characterization and representation which lead to texture classification (Haralick, 1973; Chang, 1993; Liao, 2009; Chakraborty, 2012; Karthikeyan, 2012) in images. Thus, there is a need for a suitable scheme for texture characterization and representation in images.

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