A vertex-based representation of objects in an image

Novel polygon evolution models are introduced in this paper for capturing polygonal object boundaries in images which have one or more objects that have statistically different distributions on the intensity values. The key idea in our approach is to design evolution equations for vertices of a polygon that integrate both local and global image characteristics. Our method naturally provides an efficient representation of an object through a few number of vertices, which also leads to a significant amount of compression of image content. This methodology can effectively be used in the context of MPEG-7. We also propose usage of the Jensen-Shannon criterion as an information measure between the densities of regions of an image to capture more general statistical characteristics of the data.

[1]  Kevin J. O'Connell.  Object-adaptive vertex-based shape coding method , 1997, IEEE Trans. Circuits Syst. Video Technol..

[2]  Aapo Hyvärinen,et al.  New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit , 1997, NIPS.

[3]  H. Krim,et al.  An information divergence measure for ISAR image registration , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

[4]  Si-Woong Lee,et al.  Vertex adjustment method using geometric constraint for polygon-based shape coding , 2001 .

[5]  H. Krim,et al.  Curve and polygon evolution techniques for image processing , 2002 .

[6]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  R. Deriche,et al.  Geodesic Active Regions for Texture Segmentation , 1998 .

[8]  Anthony J. Yezzi,et al.  A statistical approach to snakes for bimodal and trimodal imagery , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  Tony F. Chan,et al.  An Active Contour Model without Edges , 1999, Scale-Space.

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

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

[12]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.