Title

Segmentation of textured images based on multiple fractal feature combinations

Keywords

Fractal dimension; Gabor filters; K-means; Segmentation; Texture

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.

Publication Date

7-6-1998

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

3387

Number of Pages

25-35

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.316413

Socpus ID

0002965592 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0002965592

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