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Proceedings Paper

Image segmentation method based on extended co-occurrence matrix for multidimensional features and multiple observation windows
Author(s): Terumoto Komori; Jun Ohmura; Yoshihiko Nomura; James F. Boyce
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Paper Abstract

Image segmentation is an important component of image processing which is necessary in the early stages of image analysis. Typical methods of image segmentation are utilizing region information. They use statistics, such as the mean and standard deviation of the pixel intensity within sub-images, with the final segmentation being obtained by a succession of splitting and merging processes of sub-images in order to create regions with quasi- homogeneous properties. In this paper, we propose a co- occurrence matrix based method of image segmentation in region-based techniques. It utilizes the observation that features of the multiple windows neighboring a pixel do not differ significantly from one another, and that features corresponding to pixels belonging to the same object form a cluster in the feature space, which may frequently be approximated by a Gaussian distribution. This paper extends the co-occurrence matrix based method. The definition of co- occurrence features is extended from one dimension to many dimensions: the number of observation windows is extended from two to an arbitrary number.

Paper Details

Date Published: 5 October 2001
PDF: 11 pages
Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); doi: 10.1117/12.444204
Show Author Affiliations
Terumoto Komori, Mie Univ. (Japan)
Jun Ohmura, Nagoya Univ. (Japan)
Yoshihiko Nomura, Mie Univ. (Japan)
James F. Boyce, King's College London (United Kingdom)

Published in SPIE Proceedings Vol. 4572:
Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall, Editor(s)

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