Wavelet Multiscale Products based Genetic Fuzzy Clustering for Image Edge Detection Analysis
A new edge detection algorithm by combining multiscate 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.
Wavelet multiscale products genetic fuzzy clustering edge detection.
Yishu Zhai Xiaoming Liu
School of Information Engineering, Dalian Maritime University Dalian, Liaoning Province, 116026, China
国际会议
Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)
北京
英文
413-417
2006-07-17（万方平台首次上网日期，不代表论文的发表时间）