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Segmentation of textured images based on fractals and image filtering

✍ Scribed by T. Kasparis; D. Charalampidis; M. Georgiopoulos; J. Rolland

Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
490 KB
Volume
34
Category
Article
ISSN
0031-3203

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✦ Synopsis

This paper describes a new approach to the segmentation of textured gray-scale images based on image pre-"ltering and fractal features. Traditionally, "lter bank decomposition methods consider the energy in each band as the textural feature, a parameter that is highly dependent on image intensity. In this paper, we use fractal-based features which depend more on textural characteristics and not intensity information. To reduce the total number of features used in the segmentation, the signi"cance of each feature is examined using a test similar to the F-test, and less signi"cant features are not used in the clustering process. The commonly used K-means algorithm is extended to an iterative K-means by using a variable window size that preserves boundary details. The number of clusters is estimated using an improved hierarchical approach that ignores information extracted around region boundaries.

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