Image segmentation based on fuzzy flood fill mean shift algorihm
- Authors
- Kang, H.; Lee, S.H.; Lee, J.
- Issue Date
- 2010
- Keywords
- Component; Floodfill; Kernel density estimation; Mean shift; Robot vision; Segmentation
- Citation
- Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
- Journal Title
- Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/50196
- DOI
- 10.1109/NAFIPS.2010.5548413
- ISSN
- 0000-0000
- Abstract
- In this paper, the fuzzy flood fill mean shift algorithm is introduced. This algorithm is developed for the methodology of robust segmentation by improving the mean shift algorithm through the fuzzy kernels and the flood fill technique, instead of those based on the spatial bandwidth. Due to this exchange, the flood fill mean shift involves only one parameter, the range bandwidth, which is less sensitive and is able to acquire the global characteristics. If the image parts affected by the illumination changes are sufficiently small and their boundaries are not clear, the illumination effects do not have an influence on the mode seeking procedure of the proposed fuzzy flood fill mean shift. To prove the usefulness and the validity of our algorithm, we present several experiments and analysis of the results. © 2010 IEEE.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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