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Proceedings Paper

Low-level image segmentation via texture recognition
Author(s): Devesh Patel; T. John Stonham
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Paper Abstract

In this paper, we propose a method for low-level unsupervised image segmentation via texture recognition and feature space clustering. The texture measure is based on the computation of n-tuple features of gray level values within the co-occurrence operator. These features are extracted from small local areas of the image. The strategy results in a feature vector transformation of the image. Self-evolving clustering is then used to group these feature vectors into clusters of homogeneous textured regions. The method as presented is applied to, and shown to be capable of, segmenting natural texture image composites. The method is computationally simple and can be implemented in hardware for real-time operation.

Paper Details

Date Published: 1 November 1991
PDF: 9 pages
Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); doi: 10.1117/12.50331
Show Author Affiliations
Devesh Patel, Brunel Univ. (United Kingdom)
T. John Stonham, Brunel Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 1606:
Visual Communications and Image Processing '91: Image Processing
Kou-Hu Tzou; Toshio Koga, Editor(s)

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