Directional geodesic active contour for image segmentation

By incorporating the image gradient directional information into the geodesic active contour model, we propose a novel active contour model called directional geodesic active contour, which has the advantage of selectively detecting the image edges with different gradient directions. The experiment results show the high performance of the proposed active contour in image segmentation, especially when multiple edges with different gradient directions are present near the object boundary to confuse the active contour.

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

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

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

[4]  Yongmin Kim,et al.  Active contour model with gradient directional information: directional snake , 2001, IEEE Trans. Circuits Syst. Video Technol..

[5]  Cheng Wang,et al.  Simultaneously improving global and local properties of virtual electric field , 2006 .

[6]  P. Olver,et al.  Conformal curvature flows: From phase transitions to active vision , 1996, ICCV 1995.

[7]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[8]  Alfred M. Bruckstein,et al.  Regularized Laplacian Zero Crossings as Optimal Edge Integrators , 2003, International Journal of Computer Vision.

[9]  Thierry Blu,et al.  Efficient energies and algorithms for parametric snakes , 2004, IEEE Transactions on Image Processing.

[10]  Say Wei Foo,et al.  Dynamic directional gradient vector flow for snakes , 2006, IEEE Transactions on Image Processing.

[11]  Kaleem Siddiqi,et al.  Flux maximizing geometric flows , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  Guopu Zhu,et al.  Dual geometric active contour for image segmentation , 2006 .

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