Abstract
In detecting the boundary of an object in an image, if certain prior shape knowledge of the object is available, an effective approach is to have the intensity gradient information in the image and the prior shape knowledge be combined together to drive an active contour for the purpose. While in the classical methods the two terms are almost always summed with a certain weight between them to form the optimization functional, in the method we propose, they are multiplied together so as to avoid the need and thus design of the weight parameter. We show that the object detection result in the traditional formulation could indeed be very much affected by the weight value, and the proposed method, being without its presence, is therefore free from the influence of the important parameter. Experimental results on cells in real biological images, whose boundaries are blurred to very different degrees across the image by the inevitably uneven illumination, are shown to demonstrate the improvement in performance.
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References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int’l J. Comp. Vision 1(4), 321–331 (1988)
Amini, A., Weymouth, T., Jain, R.: Using dynamic programming for solving variational problems in vision. IEEE Trans. Pattern Anal. Mach. Intell. 12, 855–867 (1990)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int’l J. Computer Vision 22(1), 61–79 (1997)
Samson, C., Blanc-feraud, L., Aubert, G., Zerubia, J.: A level set model for image classification. Int’l J. Comp. Vision 40(3), 187–197 (2000)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)
Chan, T.F., Vese, L.A.: A level set algorithm for minimizing the mumford-shah functional in image processing. In: IEEE Workshop on Variational and Level Set Methods in Comp. Vision, pp. 161–168 (2001)
Zhu, S.C., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Transcations on Pattern Analysis and Machine Intelligence 18(9), 884–900 (1996)
Yezzi, A., Tsai, A., Willsky, A.: A statistical approach to snakes for bimodal and trimodal imagery. In: IEEE Int’l Conf. Comp. Vision, vol. 2, pp. 898–903 (1999)
Chen, Y., Hemant, D., Tagare, E.: Using prior shapes in geometric active contours in a variational framework. Int’l J. Computer Vision 50(3), 315–328 (2002)
Tsai, A., Anthony Yezzi, J., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W.E.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Medical Imaging 22(2), 137–154 (2003)
Bresson, X., Vandergheynst, P., Thiran, J.P.: A variational model for object segmentation using boundary information and shape prior driven by the mumford-shah functional. Int’l J. Computer Vision 68(2), 145–162 (2006)
Staib, L.H., Duncan, J.S.: Boundary finding with parametrically deformable models. IEEE Trans. on Pattern Analysis and Machine Intellignece 14(11), 1061–1075 (1992)
Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)
Leventon, M.E., Grimson, W.E.L., Faugeras, O.: Statistical shape influence in geodesic active contours. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 316–323 (2000)
Riklin-Raviv, T., Kiryati, N., Sochen, N.: Unlevel-sets: Geometry and prior-based segmentation. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 50–61. Springer, Heidelberg (2004)
Osher, S., Sethian, J.: Fronts propagation with curvature dependent speed: Algorithms based on hamilton-jacobi fomulations. J. Comput. Phys. 79, 12–49 (1988)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995)
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Wang, W., Chung, R. (2007). The Multiplicative Path Toward Prior-Shape Guided Active Contour for Object Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_53
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DOI: https://doi.org/10.1007/978-3-540-76856-2_53
Publisher Name: Springer, Berlin, Heidelberg
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