21 September 2001 Pyramidal edge detector based on adaptive weighted fuzzy mean filters
Author Affiliations +
Proceedings Volume 4550, Image Extraction, Segmentation, and Recognition; (2001) https://doi.org/10.1117/12.441452
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China
A new unsupervised multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean (AWFM) filters, is presented in this paper. The algorithm first constructs a pyramidal structure by repetitive AWFM filtering and subsampling of original image. Then it utilizes multiple heuristic linking criteria between the edge nodes of two adjacent levels and considers the linkage as a fuzzy model, which is trained offline. Through this fuzzy linking model, the boundaries detected at coarse resolution are propagated and refined to the bottom level from the coarse-to-fine edge detection. The validation experiments results demonstrate that the proposed approach has superior performance compared with standard fixed resolution detector and previous multiresolution approach, especially in impulse noise environment.
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Zhi-Gang Wang, Zhi-Gang Wang, Dong Wang, Dong Wang, Wei Wang, Wei Wang, Xiaoming Xu, Xiaoming Xu, } "Pyramidal edge detector based on adaptive weighted fuzzy mean filters", Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); doi: 10.1117/12.441452; https://doi.org/10.1117/12.441452


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