Active Contours with Prior Corner Detection
Commenced in January 2007
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Active Contours with Prior Corner Detection

Authors: U.A.A. Niroshika, Ravinda G.N. Meegama

Abstract:

Deformable active contours are widely used in computer vision and image processing applications for image segmentation, especially in biomedical image analysis. The active contour or “snake" deforms towards a target object by controlling the internal, image and constraint forces. However, if the contour initialized with a lesser number of control points, there is a high probability of surpassing the sharp corners of the object during deformation of the contour. In this paper, a new technique is proposed to construct the initial contour by incorporating prior knowledge of significant corners of the object detected using the Harris operator. This new reconstructed contour begins to deform, by attracting the snake towards the targeted object, without missing the corners. Experimental results with several synthetic images show the ability of the new technique to deal with sharp corners with a high accuracy than traditional methods.

Keywords: Active Contours, Image Segmentation, Harris Operator, Snakes

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1327676

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References:


[1] T.McInerney and D.Terzopoulos. "Deformable models in medical image analysis": A survey. Medical Image Analysis, vol. 1, no. 2, pp. 91-108, 1996.
[2] A. Singh, D. Goldgof, and D. Terzopoulos, editors. "Deformable models in Medical Image Analysis". IEEE Computer Society Press, 1998.
[3] M. Kaas, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models", Int. Journal of Computer Vision, vol. 1, no. 4, pp. 321-331, 1988.
[4] T. McInerny, D. Terzopoulos, "T-sankes: Topology adaptive snakes", in Proc. International Conference on Computer Vision, pp. 840-845, 1995.
[5] Wai-Pak Choi, Kin-Man Lam an Wan-Chi Siu, "An adaptive active contour model for highly irregular boundaries", Pattern Recognition, vol. 34, pp. 323-331, 2001.
[6] L. D. Cohen, "On active contour models and balloons", CVGIP: Image Understanding, 53(2), pp. 221-218, 1991.
[7] K. M. Lam and H. Yan, "Fast greedy algorithm for active contours", Electronic Letters, vol. 30, no.1, pp. 21-23, 1994.
[8] C. Xu and J. L. Prince, "Gradient Vector Flow: A New External Force for Snakes", in Proc. IEEE Conf. on Computer Vision and pattern Recognition (CVPR), Los Alamitos: Comp. Soc. Press, pp. 66-71, June 1997.
[9] S. Menet, P. Saint-Marc, and G. Medioni. "B-snakes: Implementation and application to stereo". In proceedings DARPA, pp. 720-726, 1990.
[10] R. G. N. Meegama, J. C. Rajapakse, "NURBS snakes", Image and Vision Computing, vol. 21, no. 6, pp. 551-562, 2003.
[11] J. S. Duncan, N. Ayache, "Medical Image Analysis: Progress over Two Decades and the Challenges Ahead", in Proc. IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 90-92, 2000.
[12] C. Harris and M. Stephens, "A combined corner and edge detector", in Proc. 4th Alvey Vision Conference, pp. 147-151, 1988.
[13] J. Ahlberg, "An Active Contour in Three Dimensions", Thesis project at Computer Vision Laboratory, Linkoping University, pp. 17, 1996.
[14] L. Staib, J. Duncan, "Model-based deformable surface for medical images", IEEE Transactions on Medical Imaging, 15 (6), pp. 859-870, 1996.