articulated shape matching using contour-point signatures (abstract)
DESCRIPTION
(CLAM 2012)TRANSCRIPT
Articulated shape matching using Contour-Point Signatures1
Waldemar Villamayor-Venialboa, Christian E. Schaererb, and Horacio Legal-Ayalac
Laboratorio de Computacion Cientıfica y AplicadaFacultad Politecnica, Universidad Nacional de Asuncion
The shape matching problem is of great interest due to the current market needs of objectsrecognition systems in the ever evolving field of automated processes. The Contour-PointSignature (CPS), introduced in [6], is a 1-D function that allows the matching of corre-sponding points on the contours of two similar 2-D shapes. By the definition, the CPSdepends on a distance function, used to measure the length of a path connecting twopoints on the boundary of a silhouette. In [6], the authors used the Euclidean distanceto demonstrate the effectiveness of the CPS to match shapes undergoing rigid transfor-mations. In this work, we propose the use of a geodesic distance [1, 5] to show that theCPS-based matching technique can also be used to find correspondences among points onthe contours of articulated shapes, objects composed of rigid parts, each of which has acertain freedom to move. In an articulated shape, each part transforms in a rigid mannerand preserves the topology of the whole shape; an assumption is made that articulationscan be modeled as near-isometries [1]. We have tested our approach on shape databasescommonly used in standard benchmark tests, including the articulated shape dataset [4, 5]and the tools dataset [2, 3] to show the insensitivity of the CPS to articulation.
References
[1] A. Bronstein, M. Bronstein, A. Bruckstein, and R. Kimmel. Matching two-dimensionalarticulated shapes using generalized multidimensional scaling. In F. Perales andR. Fisher, editors, Articulated Motion and Deformable Objects, volume 4069 of LectureNotes in Computer Science, pages 48–57. Springer Berlin / Heidelberg, 2006.
[2] A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, and R. Kimmel. Analysis oftwo-dimensional non-rigid shapes. Intl. J. Computer Vision, 78(1):67–88, June 2008.
[3] A. M. Bronstein, M. M. Bronstein, and R. Kimmel. Numerical geometry of non-rigidshapes. Springer, 2008.
[4] H. Ling and D. W. Jacobs. Using the inner-distance for classification of articulatedshapes. In Proc. IEEE Conf. Comp. Vision Patt. Recogn., volume II, pages 719–726,2005.
[5] H. Ling and D. W. Jacobs. Shape classification using the inner-distance. IEEE Trans.Pattern Anal. Mach. Intell., 29:286–299, Feb. 2007.
[6] W. Villamayor-Venialbo, H. Legal-Ayala, and C. E. Schaerer. Contour-point signature:A new descriptor for matching rigid shapes with a single closed contour. In Proc. Cong.de Matem. Aplic. e Comp. Regiao Sudeste, pages 524–526, 2011.
1Acknowledgment. This work is partially supported by CONACyT (Program 1698 OC/PR).
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