Model-Based Recognition of 3D Objects using Intersecting Lines

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 35)

Abstract

Exploiting geometric features, such as points, straight or curved lines and corners, plays an important role in object recognition. In this paper, we present a model-based recognition of 3D objects using intersecting lines. We concentrate on using perpendicular line pairs to test recognition of a parallelepiped model and represent the visible face of the object. From 2D images and point clouds, first, 3D line segments are extracted, and then intersecting lines are selected from them. By estimating the coverage ratio, we find the most accurate matching between detected perpendicular line pairs and the model database. Finally, the position and the pose of the object are determined. The experimental results show the performance of the proposed algorithm.

Keywords

Line matching Model-based recognition Intersecting line 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. Giessen and J. Schmidhuber, Fast color-based object recognition independent of position and orientation, ICANN 2005, LNCS 3696, pp. 469–474, 2005.Google Scholar
  2. 2.
    Z. Zhang and O. Faugeras, Determining motion from 3D line segment matches: A comparative study, Image and Vision Computing, 9(1), 10–19, 1991.CrossRefGoogle Scholar
  3. 3.
    C. Guerra and V. Pascucci, On matching sets of 3D segments, Proceedings of SPIE Conference on Vision Geometry VIII, 3811, 157–167, 1999.Google Scholar
  4. 4.
    B. Kamgar-Parsi, Algorithm for matching 3D line sets, IEEE Transactions on Pattern Analysis and Machine Intelligent, 26(5), 582–593, 2004.Google Scholar
  5. 5.
    O. D Faugeras and M. Hebert, The representation, recognition, and locating of 3-D object, International Journalof Robotics Research, 5(3), 27–52, 1986.CrossRefGoogle Scholar
  6. 6.
    B. Kamgar-Parsi and B. Kamgar-Parsi, An open problem in matching sets of 3D lines, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1, 651–656, 2001.Google Scholar
  7. 7.
    J. Košecká and W. Zhang, Extraction, matching, and pose recovery based on dominant rectangular structures, Computer Vision and Image Understanding, 100(3), 274–293, 2005.Google Scholar
  8. 8.
    B. Kamgar-Parsi and B. Kamgar-Parsi, Matching sets of 3D line segments with application to polygonal arc matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 1090–1099, 1997.CrossRefGoogle Scholar
  9. 9.
    N. Ayache and O. Faugeras, A new approach for the recognition and positioning of two dimensional objects, IEEE Transactions on Pattern Analysis Machine Intelligence, 8(1), 44–54, 1986.CrossRefGoogle Scholar
  10. 10.
    S. Lee, E. Y. Kim, and Y. C. Park, 3D object recognition using multiple feature for robotics manipulation, IEEE International Conference on Robotics and Automation, pp. 3768–3774, 2006.Google Scholar
  11. 11.
    D. Lowe, Object recognition from local scale invariant features, In Proceedings of the Seventh International Conference on Computer Vision (ICCV’99), pp. 116–128, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.School of Information and Communication Engineering, Sungkyunkwan UniversitySuwonRepublic of Korea
  2. 2.Intelligent Systems Research Center, Sungkyunkwan UniversityJangan-gu, SuwonSouth Korea
  3. 3.School of Information and Communication Engineering, Sungkyunkwan UniversityKorea

Personalised recommendations