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- W2754092020 abstract "In this paper, we describe a set of robust algorithms for group-wise registration using both rigid and non-rigid transformations of multiple unlabelled point-sets with no bias toward a given set. These methods mitigate the need to establish a correspondence among the point-sets by representing them as probability density functions where the registration is treated as a multiple distribution alignment. Holders and Jensens inequalities provide a notion of similarity/distance among point-sets and Rényis second order entropy yields a closed-form solution to the cost function and update equations. We also show that the methods can be improved by normalizing the entropy with a scale factor. These provide simple, fast and accurate algorithms to compute the spatial transformation function needed to register multiple point-sets. The algorithms are compared against two well-known methods for group-wise point-set registration. The results show an improvement in both accuracy and computational complexity." @default.
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- W2754092020 date "2017-07-01" @default.
- W2754092020 modified "2023-10-15" @default.
- W2754092020 title "Group-Wise Point-Set Registration Based on Rényi's Second Order Entropy" @default.
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- W2754092020 doi "https://doi.org/10.1109/cvpr.2017.263" @default.
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