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Segmentation of Textured Images Based on Fractals and Image Filtering by T. Kasparis; D. Charalampidis; M. Georgiopoulos; J. Rolland is a Computer Science article available to read on EtoBox.

What is Segmentation of Textured Images Based on Fractals and Image Filtering about?

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.

Who reads Segmentation of Textured Images Based on Fractals and Image Filtering?

Author

T. Kasparis; D. Charalampidis; M. Georgiopoulos; J. Rolland

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0031-3203)

Published

2001

Language

EN

Field

Computer Science (Physical Sciences)