Advertisement

A New Recognition and Pose Estimation of Tiny Electronic Parts Using Principal Component Analysis and Harris Corner Features

  • Jiheon Lee
  • Wangheon LeeEmail author
Original Article
  • 3 Downloads

Abstract

The center coordinates and rotated angle of the electronic part must be precisely estimated in case of palletizing the parts by picking and placing the parts to output tray by a robot, especially, the tiny parts like surface mounting device (SMD). A machine vision algorithm must be used in recognizing the type as well as estimating the pose of the parts. In this paper, we propose not only a recognition algorithm of electronic part using Principal Component Analysis [PCA] combined with Harris corner feature (HCF) and least mean square error (LMSE) for the matching of the part with data base, but also a precise pose estimation of the recognized part by using the extracted four corners from HCF combined with lines from Hough transformation (LHT). Applying the proposed algorithm to the test setup of five kinds of SMDs, we verified the usefulness of the proposed algorithm in accurate pose estimation as well as in recognition of type of the parts even in environment with illumination changes.

Keywords

PCA Harris corner features Hough transformation Part recognition 

Notes

References

  1. 1.
    Bruce ND, Kornprobst P (2008) Harris corners in the real world: a principled selection criterion for interest points based on ecological statistics. INRIA, No 6745Google Scholar
  2. 2.
    Gonzalez RE et al (2002) Digital image processing, 2nd edn. Prentice-Hall, Prentice, pp 586–591Google Scholar
  3. 3.
    Harris C, Stephens M (1988) A combined corner and edge detector. In: 4th Alvey vision conference, pp 147–151Google Scholar
  4. 4.
    Derpanis KG (2004) The harris corner detector.orn. http://www.scientificcommons.org/42365971
  5. 5.
    Jolliffe IT (2002) Principal component analysis, chapter 7, 2nd edn. Springer, New YorkzbMATHGoogle Scholar
  6. 6.
    Bishop CM (2006) Pattern recognition and machine vision. Springer, New York, pp 569–574Google Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyHansei University of KoreaGunpoSouth Korea

Personalised recommendations