Using retinex for point selection in 3D shape registration

Liu, Yonghuai, Martin, Ralph Robert, de Dominicis, Luigi and Li, Baihua 2014. Using retinex for point selection in 3D shape registration. Pattern Recognition 47 (6) , pp. 2126-2142. 10.1016/j.patcog.2013.12.015

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	Retinex; Key point; Freeform shape; Adaptive smoothing; Registration
Additional Information:	PDF uploaded in accordance with publisher's policy http://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy [accessed 17/04/2015] NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, [VOL 47, ISSUE 6, 2014] DOI 10.1016/j.patcog.2013.12.015
Publisher:	Elsevier
ISSN:	0031-3203
Last Modified:	11 Nov 2023 10:21
URI:	https://orca.cardiff.ac.uk/id/eprint/57464

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