Presentation 2000/6/16
An Adaptive Weighted Mean Filter Using Fuzzy Clustering
Mitsuji MUNEYASU, Tetsuya ODA, Takao HINAMOTO,
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Abstract(in English) This paper proposes a novel adaptive weighed mean filter using fuzzy clustering. The fuzzy c-means algorithm is used for classification of feature vectors whose elements are the difference value between the processing pixel and the other pixel in the filter mask. When a feature vector is given, the memberships corresponding to the predefined clusters are decided and the weights are selected. Then, the weighted mean of the inputs in the filter mask and weights is calculated and the weighted mean of these results and the memberships is also calculated as the filter output. By the proposed technique, the fine noise reduction filters for the mixed noise can be designed. Finally, simulation examples show the effectiveness of the proposed technique.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) data-dependent type filter / weighted mean filter / mixed noise removal / fuzzy clustering / membership
Paper # CAS2000-22,VLD2000-31,DSP2000-43
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Committee CAS
Conference Date 2000/6/16(1days)
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Registration To Circuits and Systems (CAS)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An Adaptive Weighted Mean Filter Using Fuzzy Clustering
Sub Title (in English)
Keyword(1) data-dependent type filter
Keyword(2) weighted mean filter
Keyword(3) mixed noise removal
Keyword(4) fuzzy clustering
Keyword(5) membership
1st Author's Name Mitsuji MUNEYASU
1st Author's Affiliation Faculty of Engineering, Hiroshima University()
2nd Author's Name Tetsuya ODA
2nd Author's Affiliation Faculty of Engineering, Hiroshima University
3rd Author's Name Takao HINAMOTO
3rd Author's Affiliation Faculty of Engineering, Hiroshima University
Date 2000/6/16
Paper # CAS2000-22,VLD2000-31,DSP2000-43
Volume (vol) vol.100
Number (no) 119
Page pp.pp.-
#Pages 8
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