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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:49237", author = "U.A.A. Niroshika and Ravinda G.N. Meegama", title = "Active Contours with Prior Corner Detection", 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", volume = "6", number = "2", pages = "145-5", }