Wavelet Multiscale Products Based Genetic Fuzzy Clustering for Image Edge Detection Analysis

Author

Zhai, Yishu ; Liu, Xiaoming

Author_Institution

Sch. of Inf. Eng., Dalian Maritime Univ.

Volume

1

fYear

2006

fDate

17-19 July 2006

Firstpage

413

Lastpage

417

Abstract

A new edge detection algorithm by combining multiscale wavelet technique and genetic fuzzy clustering algoithm is proposed in this paper (called WGFCA), which can realize edge detection of input image automatically. Based on the theory that signals and noise have different characters along wavelet decomposition scales, WGFCA firstly multiply the wavelet coefficient of input image at adjacent scales to enhance edge structure and suppress noise, then, in order to restrain noise further, WGFCA apply fuzzy median filter to the result obtained above. Finally, edge map of input image is obtained by the great unsupervised classifying technique-genetic fuzzy clustering based on an effective feature extraction algorithm. Experiment results demonstrated promising performance of the proposed edge detection algorithm

Keywords

edge detection; feature extraction; fuzzy set theory; genetic algorithms; image classification; pattern clustering; wavelet transforms; feature extraction; fuzzy median filter; genetic fuzzy clustering; image edge detection analysis; multiscale wavelet technique; unsupervised classification; wavelet decomposition; wavelet multiscale products; Acoustic noise; Clustering algorithms; Feature extraction; Fuzzy sets; Genetics; Image analysis; Image edge detection; Noise reduction; Wavelet analysis; Wavelet transforms; Wavelet multiscale products; edge detection; genetic fuzzy clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

1-4244-0475-4

Type

conf

DOI

10.1109/COGINF.2006.365525

Filename

4216442