On the relationship between parametric and geometric active contours

Geometric active contours have many advantages over parametric active contours, such as computational simplicity and the ability to change the curve topology during deformation. While many of the capabilities of the older parametric active contours have been reproduced in geometric active contours, the relationship between the two has not always been clear. We develop a precise relationship between the two which includes spatially-varying coefficients, both tension and rigidity, and non-conservative external forces. The result is a very general geometric active contour formulation for which the intuitive design principles of parametric active contours can be applied. We demonstrate several novel applications in a series of simulations.

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

[2]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[3]  Ramesh C. Jain,et al.  Using Dynamic Programming for Solving Variational Problems in Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

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

[5]  James S. Duncan,et al.  Boundary Finding with Parametrically Deformable Models , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  P. Lions,et al.  Axioms and fundamental equations of image processing , 1993 .

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

[8]  Ross T. Whitaker,et al.  Embedded active surfaces for volume visualization , 1994, Medical Imaging.

[9]  J. Sethian,et al.  A Fast Level Set Method for Propagating Interfaces , 1995 .

[10]  Demetri Terzopoulos,et al.  Topologically adaptable snakes , 1995, Proceedings of IEEE International Conference on 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]  P. Olver,et al.  Conformal curvature flows: From phase transitions to active vision , 1996, ICCV 1995.

[13]  James S. Duncan,et al.  Deformable boundary finding in medical images by integrating gradient and region information , 1996, IEEE Trans. Medical Imaging.

[14]  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..

[15]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[16]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Anthony J. Yezzi,et al.  A geometric snake model for segmentation of medical imagery , 1997, IEEE Transactions on Medical Imaging.

[18]  M Braun,et al.  Image segmentation by a deformable contour model incorporating region analysis , 1997, Physics in medicine and biology.

[19]  Kaleem Siddiqi,et al.  Area and length minimizing flows for shape segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Rachid Deriche,et al.  Detecting multiple moving targets using deformable contours , 1997, Proceedings of International Conference on Image Processing.

[21]  Jerry L. Prince,et al.  An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging , 1997, Int. J. Pattern Recognit. Artif. Intell..

[22]  Jerry L. Prince,et al.  Generalized gradient vector flow external forces for active contours , 1998, Signal Process..

[23]  Josiane Zerubia,et al.  A Level Set Model for Image Classification , 1999, Scale-Space.

[24]  James A. Sethian,et al.  The Fast Construction of Extension Velocities in Level Set Methods , 1999 .

[25]  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.

[26]  Rachid Deriche,et al.  Unifying boundary and region-based information for geodesic active tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[27]  J. Sethian,et al.  Motion by intrinsic Laplacian of curvature , 1999 .

[28]  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 .

[29]  Jerry L Prince,et al.  Image Segmentation Using Deformable Models , 2000 .

[30]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

[33]  R. Millman,et al.  Elements of Differential Geometry , 2018, Applications of Tensor Analysis in Continuum Mechanics.

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