Kernel snakes: non-parametric active contour models

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.

[1]  J. Kender,et al.  Nonparametric training of snakes to find indistinct boundaries , 2001, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001).

[2]  Douglas P. Perrin,et al.  Rethinking classical internal forces for active contour models , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Charles R. Dyer,et al.  Perception-Based 2D Shape Modeling by Curvature Shaping , 2001, IWVF.

[4]  John Porrill,et al.  Active region models for segmenting medical images , 1994, Proceedings of 1st International Conference on Image Processing.

[5]  Andrew Blake,et al.  Visually guided grasping in 3D , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[6]  Wael Abd-Almageed,et al.  Mixture models for dynamic statistical pressure snakes , 2002, Object recognition supported by user interaction for service robots.

[7]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[8]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[9]  Yong-Ho Cho,et al.  3D segmentation of a medical image using the geometric active contour model , 1999, Medical Imaging.

[10]  T. Bayes.  An essay towards solving a problem in the doctrine of chances , 2003 .

[11]  Silko Kruse,et al.  Fine segmentation of image objects by means of active contour models using information derived from morphological transformations , 1996, Other Conferences.