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

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.