Active Contour Method with Separate Global Translation and Local Deformation

Active Contour can describe targets accurately and has been widely used in image segmentation and target tracking. Its main drawback is huge computation that is still not well resolved. In this paper, by analyzing curve gradient flow, the evolution of active contour is divided into two steps: global translation and local deformation. When the curve is far away from the object, the curve just does the translation motion. This method can optimize the curve evolving path and efficiency, and then the computation cost is largely reduced. Our experiments show that our method can segment and track object effectively.

[1]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[2]  Junaed Sattar.  Snakes , Shapes and Gradient Vector Flow , 2022 .

[3]  Anthony J. Yezzi,et al.  More-than-topology-preserving flows for active contours and polygons , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[4]  Jean-Philippe Pons,et al.  Generalized Gradients: Priors on Minimization Flows , 2007, International Journal of Computer Vision.

[5]  Tai Sing Lee,et al.  Region competition: unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

[7]  Rachid Deriche,et al.  A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape , 2007, International Journal of Computer Vision.

[8]  Yunmei Chen,et al.  Using Prior Shapes in Geometric Active Contours in a Variational Framework , 2002, International Journal of Computer Vision.

[9]  Xiao Han,et al.  A Topology Preserving Level Set Method for Geometric Deformable Models , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  O. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[11]  John W. Fisher,et al.  Submitted to Ieee Transactions on Image Processing a Nonparametric Statistical Method for Image Segmentation Using Information Theory and Curve Evolution , 2022 .

[12]  Anthony J. Yezzi,et al.  Sobolev Active Contours , 2005, VLSM.

[13]  Tony F. Chan,et al.  Some Recent Developments in Variational Image Segmentation , 2007 .

[14]  Daniel Cremers,et al.  Dynamical statistical shape priors for level set-based tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[16]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[17]  Anthony J. Yezzi,et al.  A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations , 2002, J. Vis. Commun. Image Represent..

[18]  Anthony J. Yezzi,et al.  Gradient flows and geometric active contour models , 1995, Proceedings of IEEE International Conference on Computer Vision.