Non-rigid registration using distance functions |
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Authors: | Nikos Paragios Mikael Rousson Visvanathan Ramesh |
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Affiliation: | a Siemens Corporate Research, Imaging and Visualization Department, 755 College Road East, Princeton, NJ 08540, USA;b I.N.R.I.A. BP. 93, 2004 route des Lucioles, 06902, Sophia Antipolis Cedex, France |
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Abstract: | This paper deals with the registration of geometric shapes. Our primary contribution is the use of a simple and robust shape representation (distance functions) for global-to-local alignment. We propose a rigid-invariant variational framework that can deal as well with local non-rigid transformations. To this end, the registration map consists of a linear motion model and a local deformations field, incrementally recovered. In order to demonstrate the performance of the selected representation a simple criterion is considered, the sum of square differences. Empirical validation and promising results were obtained on examples that exhibit large global motion as well as important local deformations and arbitrary topological changes. |
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Keywords: | Distance functions Shape matching Level set representations Variational methods Sum of squared differences Joint registration and learning |
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