A hierarchical edge-stressing algorithm for adaptive image segmentation

Author

Tolias, Yannis A. ; Kanlis, Nikolaos A. ; Panas, Stavros M.

Author_Institution

Telecommun. Div., Aristotelian Univ. of Thessaloniki, Greece

Volume

1

fYear

1996

fDate

13-16 Oct 1996

Firstpage

199

Abstract

In this paper we present a new multiresolution edge-stressing approach for segmenting images. Our algorithm utilises the wavelet transform to obtain a multiresolution representation of the image. The low frequency residuals of each stage of the wavelet transform are being segmented using an enhanced Gibbs Random Fields model that incorporates edge information provided by the high frequency residuals. The results of the application of our algorithm are visually more attractive than the segmentation results obtained by applying both the K-means algorithm and the Adaptive Clustering Segmentation algorithm by Pappas (1992)

Keywords

adaptive signal processing; image representation; image segmentation; wavelet transforms; adaptive image segmentation; edge information; enhanced Gibbs Random Fields model; hierarchical edge-stressing algorithm; high frequency residuals; low frequency residuals; multiresolution representation; wavelet transform; Clustering algorithms; Discrete wavelet transforms; Educational institutions; Frequency; Image processing; Image resolution; Image segmentation; Iterative algorithms; Markov random fields; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits, and Systems, 1996. ICECS '96., Proceedings of the Third IEEE International Conference on

Conference_Location

Rodos

Print_ISBN

0-7803-3650-X

Type

conf

DOI

10.1109/ICECS.1996.582776

Filename

582776