Abstract
In this study, we present a Fast Marching (FM) - Shape Description integrated methodology that is capable both extracting object boundaries and recognizing shapes. A new speed formula is proposed, and the local front stopping algorithm in [1] is enhanced to freeze the active contour near real object boundaries. GBSD [2] is utilized as shape descriptor on evolving contour. Shape description process starts when a certain portion of the contour is stopped and continues with FM iterations. Shape description at each iteration is treated as a different source of shape information and they are fused to get better recognition results. This approach removes the limitation of traditional recognition systems that have only one chance for shape classification. Test results shown in this study prove that the voted decision result among these iterated contours outperforms the ordinary individual shape recognizers.
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References
Capar, A., Gokmen, M.: Concurrent segmentation and recognition with shape-driven fast marching methods. In: Proc. ICPR, Hong Kong, vol. 1, pp. 155–158 (2006)
Capar, A., Kurt, B., Gokmen, M.: Gradient-based shape descriptors. Machine Vision and Applications (2008), doi:10.1007/s00138-008-0131-5
Osher, S., Sethian, J.A.: Fronts propagating with curvaturedependent speed: Algorithms based on the Hamilton-Jacobi formulation. Journal of Computational Physics, 12–49 (1988)
Sethian, J.A.: A Fast Marching level set method for monotonically advancing fronts. Proc. Nat. Acad. Sci. 93(4), 1591–1595 (1996)
Yatziv, L., Bartesaghi, A., Sapiro, G.: O(N) implementation of the fast marching algorithm. Journal of Computational Physics 212(2), 393–399 (2006)
Wang, Y., Staib, L.: Boundary finding with correspondence using statistical shape models. In: Proc. CVPR, pp. 338–345 (1998)
Staib, L., Duncan, J.: Boundary finding with parametrically deformable contour methods. IEEE PAMI 14(11), 1061–1075 (1992)
Leventon, M., Grimson, W., Faugeras, O.: Statistical Shape Infuence in Geodesic Active Contours. In: Proc. CVPR, pp. 316–323 (2000)
Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K., Briggsand, R., Geiser, E.: Using Prior Shapes in Geometric Active Contours in a Variational Framework. IJCV 50(3), 315–328 (2002)
Gastaud, M., Barlaud, M., Aubert, G.: Combining Shape Prior and Statistical Features for Active Contour Segmentation. IEEE Transactions on Circuits And Systems for Video Technology 14(5), 726–734 (2004)
Cremers, D., Tischhaauser, F., Weickert, J., Schnörr, C.: Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional. IJCV 50(3), 295–313 (2002)
Cremers, D., Kohlberger, T., Schnörr, C.: Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition, Special Issue on Kernel and Subspace Methods in Computer Vision 36(9), 1929–1943 (2003)
Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42, 577–685 (1989)
Paragios, N., Rousson, M.: Shape Priors for Level Set Representations. In: European Conference in Computer Vision, pp. 78–92 (2002)
Sethian, J.A.: Level Set Methods and Fast Marching Methods. Cambridge University Press, New York (1999)
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Freeman, W.T., Adelson, E.H.: The Design and Use of Steerable Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 891–906 (1991)
Cremers, D.: Dynamical Statistical Shape Priors for Level Set-Based Tracking. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1262–1273 (2006)
Gokmen, M., Jain, A.K.: λτ-Space Representation of Images and Generalized Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(6), 545–563 (1997)
Capar, A., Kurt, B., Gokmen, M.: Affine-Invariant Gradient Based Shape Descriptor. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 514–521. Springer, Heidelberg (2006)
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Capar, A., Gokmen, M. (2009). Shape Recognition by Voting on Fast Marching Iterations. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_35
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DOI: https://doi.org/10.1007/978-3-642-04697-1_35
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