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

    4 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
    EditionPART 1
    DOIs
    Publication statusPublished - 2013 Sep 26
    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

    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)

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  • 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., 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