Mixture models for dynamic statistical pressure snakes

Author

Abd-Almageed, Wael ; Smith, Christopher E.

Author_Institution

Dept. of Electr. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA

Volume

2

fYear

2002

fDate

2002

Firstpage

721

Abstract

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.

Keywords

Bayes methods; Gaussian distribution; computer vision; decision making; image colour analysis; image segmentation; statistical analysis; Bayesian theory; active contour models; complex colored object; computer vision; decision making mechanism; dynamic statistical pressure snakes; expectation maximization algorithm; mixture models; mixture of Gaussians; mixture pressure model; robust pressure model; segmentation results; statistical modeling; Active contours; Artificial intelligence; Bayesian methods; Computer vision; Equations; Gaussian distribution; Image edge detection; Intelligent robots; Robot vision systems; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2002. Proceedings. 16th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-1695-X

Type

conf

DOI

10.1109/ICPR.2002.1048404

Filename

1048404