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Sensors 2011, 11(8), 8028-8044; doi:10.3390/s110808028

A Noise-Aware Coding Scheme for Texture Classification

1
Department of Computer Engineering, Kyung Hee University, Yongin 446-701, Korea
2
Department of Industrial and Management Engineering, Hankuk University of Foreign Studies, Yongin 449-791, Korea
*
Author to whom correspondence should be addressed.
Received: 15 June 2011 / Revised: 1 August 2011 / Accepted: 11 August 2011 / Published: 15 August 2011
(This article belongs to the Section Physical Sensors)
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Abstract

Texture-based analysis of images is a very common and much discussed issue in the fields of computer vision and image processing. Several methods have already been proposed to codify texture micro-patterns (texlets) in images. Most of these methods perform well when a given image is noise-free, but real world images contain different types of signal-independent as well as signal-dependent noises originated from different sources, even from the camera sensor itself. Hence, it is necessary to differentiate false textures appearing due to the noises, and thus, to achieve a reliable representation of texlets. In this proposal, we define an adaptive noise band (ANB) to approximate the amount of noise contamination around a pixel up to a certain extent. Based on this ANB, we generate reliable codes named noise tolerant ternary pattern (NTTP) to represent the texlets in an image. Extensive experiments on several datasets from renowned texture databases, such as the Outex and the Brodatz database, show that NTTP performs much better than the state-of-the-art methods. View Full-Text
Keywords: noise tolerant ternary pattern; adaptive noise band; texlet noise tolerant ternary pattern; adaptive noise band; texlet
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Shoyaib, M.; Abdullah-Al-Wadud, M.; Chae, O. A Noise-Aware Coding Scheme for Texture Classification. Sensors 2011, 11, 8028-8044.

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