Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6939098 | Pattern Recognition | 2018 | 37 Pages |
Abstract
We propose several cost functions for registration of shapes encoded with Euclidean and/or non-Euclidean information (unit vectors). Our framework is assessed for estimation of both rigid and non-rigid transformations between the target and model shapes corresponding to 2D contours and 3D surfaces. The experimental results obtained confirm that using the combination of a point's position and unit normal vector in a cost function can enhance the registration results compared to state of the art methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Mairéad Grogan, Rozenn Dahyot,