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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, S. Margret Anouncia
Copyright: © 2016 |Volume: 7 |Issue: 2 |Pages: 21
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781466690172|DOI: 10.4018/IJACI.2016070105
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MLA

Hemalatha, S., and S. Margret Anouncia. "A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection." IJACI vol.7, no.2 2016: pp.93-113. http://doi.org/10.4018/IJACI.2016070105

APA

Hemalatha, S. & Anouncia, S. M. (2016). A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection. International Journal of Ambient Computing and Intelligence (IJACI), 7(2), 93-113. http://doi.org/10.4018/IJACI.2016070105

Chicago

Hemalatha, S., and S. Margret Anouncia. "A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection," International Journal of Ambient Computing and Intelligence (IJACI) 7, no.2: 93-113. http://doi.org/10.4018/IJACI.2016070105

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Abstract

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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