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Proceedings Paper

Noise-tolerant texture classification and image segmentation
Author(s): Bradley Pryor Kjell; Pearl Y. Wang
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Paper Abstract

Most previous work on image texture has not studied the effect of noise on texture classification or on textured image segmentation. Classification studies typically use samples of unknown textures that were digitized under the same noise-free conditions as the samples in the training set . In this paper noise-tolerant texture classification is demonstrated. Noise-tolerant texture classification allows the samples of unknown textures to be digitized under different noise and illumination conditions than the samples in the training set. The features extracted from the texture samples are relatively tolerant of noise and illumination gradients resulting in reliable classification. The method is based on edges found in texture samples using a noise-tolerant edge detector similar to the Canny operator [4]. Texture features are average distances separating edges in various orientations [10 11]. Since the edges are reliably extracted from noisy samples texture features based on these edges are noise-tolerant. This method is investigated experimentally and compared with cooccurrence matrix features [6] and cooccurrence matrix features of directionally-smoothed samples. The edge-based features may be used for texture-based image segmentation. This is demonstrated by partitioning an image into areas of uniform texture even when there are illumination and noise gradients over the image.

Paper Details

Date Published: 1 February 1991
PDF: 8 pages
Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); doi: 10.1117/12.25186
Show Author Affiliations
Bradley Pryor Kjell, George Mason Univ. (United States)
Pearl Y. Wang, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 1381:
Intelligent Robots and Computer Vision IX: Algorithms and Techniques
David P. Casasent, Editor(s)

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