DocumentCode :
2757020
Title :
Robust fuzzy snakes
Author :
Kersten, Paul R. ; Keller, James M.
Author_Institution :
Weapons Div, Naval Air Warfare Center, China Lake, CA, USA
Volume :
2
fYear :
1998
fDate :
4-9 May 1998
Firstpage :
1554
Abstract :
Flexible closed deformable contours extracted from images to represent object outlines are often called snakes. The paper explores and extends the use of snakes to extract contour features from images for modeling and pattern recognition. Many of the contour extraction algorithms have a common formulation as a constrained optimization problem. One version, called the constrained fuzzy clustering algorithm, links fuzzy clustering and snakes-the resulting contour being called a fuzzy snake. These extraction procedures are vulnerable to noise, which makes their application to noisy sensor images problematic. By applying known robust fuzzy clustering algorithms, more noise resistant snakes can be derived thereby extending contour feature extraction to sensor images
Keywords :
feature extraction; fuzzy set theory; image recognition; nonparametric statistics; optimisation; constrained fuzzy clustering algorithm; constrained optimization problem; contour features; flexible closed deformable contours; modeling; noise resistant snakes; noisy sensor images; pattern recognition; robust fuzzy snakes; Clustering algorithms; Constraint optimization; Data mining; Feature extraction; Image recognition; Image sensors; Prototypes; Robustness; Target recognition; Weapons;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
Conference_Location :
Anchorage, AK
ISSN :
1098-7584
Print_ISBN :
0-7803-4863-X
Type :
conf
DOI :
10.1109/FUZZY.1998.686350
Filename :
686350
Link To Document :
بازگشت