%0 Conference Paper
%B 16th International Conference on Pattern Recognition, 2002. Proceedings
%D 2002
%T Mixture models for dynamic statistical pressure snakes
%A Abd-Almageed, Wael
%A Smith,C.E.
%K active contour models
%K Active contours
%K Artificial intelligence
%K Bayes methods
%K Bayesian methods
%K Bayesian theory
%K complex colored object
%K Computer vision
%K decision making
%K decision making mechanism
%K dynamic statistical pressure snakes
%K Equations
%K expectation maximization algorithm
%K Gaussian distribution
%K image colour analysis
%K Image edge detection
%K Image segmentation
%K Intelligent robots
%K mixture models
%K mixture of Gaussians
%K mixture pressure model
%K Robot vision systems
%K robust pressure model
%K Robustness
%K segmentation results
%K statistical analysis
%K statistical modeling
%X This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is used to model the data into a Mixture of Gaussians (MoG). Bayesian theory is then employed as a decision making mechanism. Experimental results using the traditional pressure model and the new mixture pressure model demonstrate the effectiveness of the new models.
%B 16th International Conference on Pattern Recognition, 2002. Proceedings
%I IEEE
%V 2
%P 721- 724 vol.2 - 721- 724 vol.2
%8 2002///
%@ 0-7695-1695-X
%G eng
%R 10.1109/ICPR.2002.1048404