3-D feature point matching for object recognition based on estimation of local shape distinctiveness

Masanobu Nagase, Shuichi Akizuki, Manabu Hashimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we propose a reliable 3-D object recognition method that can statistically minimize object mismatching. Our method basically uses a 3-D object model that is represented as a set of feature points with 3-D coordinates. Each feature point also has an attribute value for the local shape around the point. The attribute value is represented as an orientation histogram of a normal vector calculated by using several neighboring feature points around each point. Here, the important thing is this attribute value means its local shape. By estimating the relative similarity of two points of all possible combinations in the model, we define the distinctiveness of each point. In the proposed method, only a small number of distinctive feature points are selected and used for matching with all feature points extracted from an acquired range image. Finally, the position and pose of the target object can be estimated from a number of correctly matched points. Experimental results using actual scenes have demonstrated that the recognition rate of our method is 93.8%, which is 42.2% higher than that of the conventional Spin Image method. Furthermore, its computing time is about nine times faster than that of the Spin Image method.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings
Pages473-481
Number of pages9
Volume8047 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013 - York, United Kingdom
Duration: 2013 Aug 272013 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8047 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013
CountryUnited Kingdom
CityYork
Period13/8/2713/8/29

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Keywords

  • 3-D descriptor
  • 3-D feature point matching
  • bin-picking
  • object recognition
  • point cloud data
  • robot vision

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Nagase, M., Akizuki, S., & Hashimoto, M. (2013). 3-D feature point matching for object recognition based on estimation of local shape distinctiveness. In Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings (PART 1 ed., Vol. 8047 LNCS, pp. 473-481). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8047 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-40261-6_57