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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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창의ICT공과대학 (전자전기공학부)
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