Abstract
This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided.
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This work was supported by the Spanish Government under Research Program Consolider Ingenio 2010: MIPRCV (CSD2007-00018) and Projects TIN2011-25606 and TIN2011-29494-C03-02.
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Rouhani, M., Sappa, A.D. (2012). Non-rigid Shape Registration: A Single Linear Least Squares Framework. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33786-4_20
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DOI: https://doi.org/10.1007/978-3-642-33786-4_20
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