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1 November 1991 Low-level image segmentation via texture recognition
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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.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Devesh Patel and T. John Stonham "Low-level image segmentation via texture recognition", Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991);

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