Visual learning and recognition of 3D objects using two-dimensional principal component analysis:a robust and an efficient approach

Nagabhushan, P. and Guru, D. S. and Shekar, B. H. (2006) Visual learning and recognition of 3D objects using two-dimensional principal component analysis:a robust and an efficient approach. Pattern Recognition, 39 (4). 721 - 725. ISSN 0031-3203

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Abstract

Inspired by the conviction that the successful model employed for face recognition M. Turk, A. Pentland, Eigenfaces for recognition, J. Cogn. Neurosci. 3(1) (1991) 71–86 should be extendable for object recognition H. Murase, S.K. Nayar, Visual learning and recognition of 3-D objects from appearance, International J. Comput. Vis. 14(1) (1995) 5–24, in this paper, a new technique called two-dimensional principal component analysis (2D-PCA) J. Yang et al., Two-dimensional PCA: a new approach to appearance based face representation and recognition, IEEE Trans. Patt. Anal. Mach. Intell. 26(1) (2004) 131–137 is explored for 3D object representation and recognition. 2D-PCA is based on 2D image matrices rather than 1D vectors so that the image matrix need not be transformed into a vector prior to feature extraction. Image covariance matrix is directly computed using the original image matrices, and its eigenvectors are derived for feature extraction. The experimental results indicate that the 2D-PCA is computationally more efficient than conventional PCA (1D-PCA) H. Murase, S.K. Nayar, Visual learning and recognition of 3-D objects from appearance, International J. Comput. Vis. 14(1) (1995) 5–24. It is also revealed through experimentation that the proposed method is more robust to noise and occlusion.

Item Type:	Article
Additional Information:	Graph-based Representations
Uncontrolled Keywords:	Principal component analysis, Appearance based model, Object recognition
Subjects:	D Physical Science > Computer Science
Divisions:	Department of > Computer Science
Depositing User:	LA manjunath user
Date Deposited:	26 Aug 2019 06:09
Last Modified:	26 Aug 2019 06:09
URI:	http://eprints.uni-mysore.ac.in/id/eprint/7016

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