Multiple Contour Finding and Perceptual Grouping using Minimal Paths

We present a new approach for finding a set of contour curves in an image. We consider the problem of perceptual grouping and contour completion, where the data is a set of points in the image. A new method to find complete curves from a set of contours or edge points is presented. Our approach is based on a previous work on finding contours as minimal paths between two end points using the fast marching algorithm (Cohen et al., 1997). Given a set of key points, we find the pairs of points that have to be linked. The paths that join them complete the initial set of contours and allow us to close them. In a second part, we propose a scheme that does not need key points for initialization. Key points are automatically selected from a larger set of admissible points. We illustrate the capability of our approach to close contours with synthetic examples.

[1]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[2]  Shimon Ullman,et al.  Structural Saliency: The Detection Of Globally Salient Structures using A Locally Connected Network , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[3]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[4]  Gérard G. Medioni,et al.  Inferring global perceptual contours from local features , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Alfred M. Bruckstein,et al.  Subpixel distance maps and weighted distance transforms , 1993, Optics & Photonics.

[7]  Alfred M. Bruckstein,et al.  Finding Shortest Paths on Surfaces Using Level Sets Propagation , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Lance R. Williams,et al.  Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience , 1995, Neural Computation.

[9]  Alok Gupta,et al.  Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Lance R. Williams,et al.  Local Parallel Computation of Stochastic Completion Fields , 1996, Neural Computation.

[12]  Alex M. Andrew,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition) , 2000 .

[13]  Laurent D. Cohen,et al.  Minimal Paths in 3D Images and Application to Virtual Endoscopy , 2000, ECCV.