Authors: Kah-Bin Lim, Tie-Hua Du, Qing Wang
Addresses: Faculty of Engineering, Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, 117576 Singapore. ' Bioinformatics Institute (BII), Agency for Science, Technology and Research, (A*STAR), #07-01, Matrix Building, 30 Biopolis Street, 138671 Singapore. ' Mechanical Engineering Department, National University of Singapore, Control and Mechatronics lab 1, Block EA 04-06 9 Engineering Drive 1, 117575 Singapore
Abstract: Partially occluded object recognition is considered as one of the most difficult problems in machine vision; it has significant importance in industrial environment. In this paper, a 2-D object recognition algorithm applicable for both stand-alone and partially occluded objects is presented. The main contributions are the development of a scale and partial occlusion invariant boundary partition algorithm and a multi-resolution feature extraction algorithm using wavelet. We also implemented a hierarchical matching strategy for feature matching to reduce computational load, but with higher matching accuracy. Experiment results show that the proposed recognition algorithm is robust to similarity transformation and partial occlusion.
Keywords: partial occlusion; object recognition; wavelet coefficients; Lipschitz exponent; wavelet descriptors; similarity transformation; machine vision.
International Journal of Computer Applications in Technology, 2011 Vol.40 No.1/2, pp.122 - 131
Published online: 10 Feb 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article