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15 September 2004 Computational holographic 3D imaging for object recognition and classification
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Object recognition and identification is one of essential parts for Homeland Security. There have been numerous researches dealing with object recognition using two-dimensional (2D) or three-dimensional (3D) imaging. In this paper, we address 3D object classification with 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. The presented technique substantially reduces the dimensionality of the 3D classification problem. Experimental and simulation results are presented for regional filtering concentrated at specific positions, and for overall grid filtering.
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Sekwon Yeom and Bahram Javidi "Computational holographic 3D imaging for object recognition and classification", Proc. SPIE 5403, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III, (15 September 2004);

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