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Proceedings Paper

Three-dimensional object feature extraction and classification using computational holographic imaging
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Paper Abstract

This paper deals with 3D object classification using computational holographic imaging. A 3D object can be reconstructed at different planes using a single hologram. We apply Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD) analysis based on Gabor-wavelet feature vectors to classify 3D objects measured by digital interferometry. Experimental and simulation results are presented for regional filtering concentrated at specific positions, and for overall grid filtering. The proposed technique substantially reduces the dimensionality of the 3D classification problem.

Paper Details

Date Published: 26 November 2003
PDF: 7 pages
Proc. SPIE 5243, Three-Dimensional TV, Video, and Display II, (26 November 2003); doi: 10.1117/12.511205
Show Author Affiliations
Sekwon Yeom, Univ. of Connecticut (United States)
Bahram Javidi, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 5243:
Three-Dimensional TV, Video, and Display II
Bahram Javidi; Fumio Okano, Editor(s)

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