Title :
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.
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;
Conference_Titel :
Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0475-4
DOI :
10.1109/COGINF.2006.365525
