Using Retinex for Point Selection in 3D Shape Registration

Yonghuai Liu, Ralph R. Martin, Luigi De Dominicis, Li Baihua

Research output: Contribution to journalArticle

12 Citations (Scopus)
74 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)2126-2142
JournalPattern Recognition
Volume47
Issue number6
Early online date15 Jan 2014
DOIs
Publication statusPublished - Jun 2014

Keywords

  • Retinex
  • Key point
  • Freeform shape
  • Adaptive smoothing
  • Registration

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