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

Sekwon Yeom; Bahram Javidi

doi:10.1117/12.511205

26 November 2003 Three-dimensional object feature extraction and classification using computational holographic imaging

Sekwon Yeom, Bahram Javidi

Proceedings Volume 5243, Three-Dimensional TV, Video, and Display II; (2003) https://doi.org/10.1117/12.511205
Event: ITCom 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

Sekwon Yeom and Bahram Javidi "Three-dimensional object feature extraction and classification using computational holographic imaging", Proc. SPIE 5243, Three-Dimensional TV, Video, and Display II, (26 November 2003); https://doi.org/10.1117/12.511205

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KEYWORDS

Principal component analysis

3D image processing

Ferroelectric LCDs

Holography

Holograms

3D image reconstruction

Feature extraction

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