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Proceedings Paper

Segmentation of textured images based on multiple fractal feature combinations
Author(s): Dimitrios Charalampidis; Takis Kasparis; Jannick P. Rolland
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Paper Abstract

This paper describes an approach to segmentation of textured grayscale images using a technique based on image filtering and the fractal dimension (FD). Twelve FD features are computed based on twelve filtered versions of the original image using directional Gabor filters. Features are computed in a window and mapped to the central pixel of this window. An iterative K-means-based algorithm which includes feature smoothing and takes into consideration the boundaries between textures is used to segment an image into a desired number of clusters. This approach is partially supervised since the number of clusters has to be predefined. The fractal features are compared to Gabor energy features and the iterative K- means algorithm is compared to the original K-means clustering approach. The performance of segmentation for noisy images is also studied.

Paper Details

Date Published: 6 July 1998
PDF: 11 pages
Proc. SPIE 3387, Visual Information Processing VII, (6 July 1998); doi: 10.1117/12.316413
Show Author Affiliations
Dimitrios Charalampidis, Univ. of Central Florida (United States)
Takis Kasparis, Univ. of Central Florida (United States)
Jannick P. Rolland, CREOL/Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 3387:
Visual Information Processing VII
Stephen K. Park; Richard D. Juday, Editor(s)

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