Switching-based clustering algorithms for segmentation of low-level salt-and-pepper noise–corrupted images
This paper presents new clustering-based segmentation algorithms. The proposed switching-based clustering algorithms can minimize salt-and-pepper noise during segmentation without degrading the images’ fine details. The proposed algorithms incorporate the salt-and-pepper noise detection stage into t...
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| Vydané v: | Signal, image and video processing Ročník 9; číslo 2; s. 387 - 398 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
Springer London
01.02.2015
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| Predmet: | |
| ISSN: | 1863-1703, 1863-1711 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | This paper presents new clustering-based segmentation algorithms. The proposed switching-based clustering algorithms can minimize salt-and-pepper noise during segmentation without degrading the images’ fine details. The proposed algorithms incorporate the salt-and-pepper noise detection stage into the clustering algorithm, producing an adaptive technique specifically for segmentation of noisy images. Experimental results show that the proposed switching-based clustering algorithms produce better segmentation with fewer noise effects than conventional clustering algorithms. Quantitative and qualitative analyses show positive results for the proposed switching-based clustering algorithms, which consistently outperform conventional clustering algorithms in segmenting up to 50 % of salt-and-pepper noise density. Thus, these switching-based clustering algorithms can be used as pre- or post-processing task (i.e., segmenting images into regions of interest) in electronic products such as televisions and monitors. |
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| ISSN: | 1863-1703 1863-1711 |
| DOI: | 10.1007/s11760-013-0455-0 |