Three-dimensional object feature extraction and classification with computational holographic imaging.
We address three-dimensional (3D) object classification with computational holographic imaging. A 3D object can be reconstructed at different planes by use of a single hologram. We apply principal component and Fisher linear discriminant analyses 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. To the best of our knowledge, this is the first report on the use of the proposed technique for 3D object classification.
Yeom
Sekwon
S
Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Road, Unit 1157, Storrs, Connecticut 06269-1157, USA.
Javidi
Bahram
B