Image segmentation method based on extended co-occurrence matrix for multidimensional features and multiple observation windows

Terumoto Komori; Jun Ohmura; Yoshihiko Nomura; James F. Boyce

doi:10.1117/12.444204

5 October 2001 Image segmentation method based on extended co-occurrence matrix for multidimensional features and multiple observation windows

Terumoto Komori, Jun Ohmura, Yoshihiko Nomura, James F. Boyce

Author Affiliations +

Proceedings Volume 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision; (2001) https://doi.org/10.1117/12.444204
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

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.

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Terumoto Komori, Jun Ohmura, Yoshihiko Nomura, and James F. Boyce "Image segmentation method based on extended co-occurrence matrix for multidimensional features and multiple observation windows", Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); https://doi.org/10.1117/12.444204

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KEYWORDS

Image segmentation

Expectation maximization algorithms

Image processing algorithms and systems

Image processing

Statistical analysis

Image analysis

Infrared imaging

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