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Using retinex for point selection in 3D shape registration

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journal contribution
posted on 08.02.2016, 15:19 authored by Yonghuai Liu, Ralph R. Martin, Luigi De Dominicis, Baihua LiBaihua Li
Inspired by retinex theory, we propose a novel method for selecting key points from a depth map of a 3D freeform shape; we also use these key points as a basis for shape registration. To find key points, first, depths are transformed using the Hotelling method and normalized to reduce their dependence on a particular viewpoint. Adaptive smoothing is then applied using weights which decrease with spatial gradient and local inhomogeneity; this preserves local features such as edges and corners while ensuring smoothed depths are not reduced. Key points are those with locally maximal depths, faithfully capturing shape. We show how such key points can be used in an efficient registration process, using two state-of-the-art iterative closest point variants. A comparative study with leading alternatives, using real range images, shows that our approach provides informative, expressive, and repeatable points leading to the most accurate registration results. © 2014 Elsevier Ltd.

History

School

  • Science

Department

  • Computer Science

Published in

Pattern Recognition

Volume

47

Issue

6

Pages

2126 - 2142

Citation

LIU, Y. ... et al, 2014. Using retinex for point selection in 3D shape registration. Pattern Recognition, 47 (6), pp.2126-2142

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

20/12/2013

Publication date

2014-01-15

ISSN

0031-3203

Language

en