DocumentCode :
397570
Title :
Kernel snakes: non-parametric active contour models
Author :
Abd-Almageed, Wael ; Smith, Christopher E. ; Ramadan, Samah
Author_Institution :
Dept. of Electr. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA
Volume :
1
fYear :
2003
fDate :
5-8 Oct. 2003
Firstpage :
240
Abstract :
In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.
Keywords :
Bayes methods; decision theory; image segmentation; nonparametric statistics; target tracking; Bayesian decision theory; kernel snakes; multicolored target tracking; nonparametric active contour models; nonparametric generalized formulation; nonparametric model; nonparametric techniques; real time performance; statistical pressure snakes; Active contours; Artificial intelligence; Bayesian methods; Decision theory; Deformable models; Image edge detection; Intelligent robots; Kernel; Laboratories; Robot vision systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2003. IEEE International Conference on
ISSN :
1062-922X
Print_ISBN :
0-7803-7952-7
Type :
conf
DOI :
10.1109/ICSMC.2003.1243822
Filename :
1243822
Link To Document :
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