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Proceedings Paper

3D object recognition with photon-counting integral imaging using independent component analysis
Author(s): Cuong Manh Do
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Paper Abstract

The author presents an overview of 3D object recognition with photon-counting integral imaging using Independent Component Analysis (ICA). High resolution elemental images of 3D objects are captured at different angles to allow object recognition at different orientations using synthetic aperture integral imaging (SAII). Generated photon-counting elemental images are used to reconstruct the 3D images at different distances from the camera lens using a maximum a posteriori estimation method. The kurtosis maximization-based algorithm is applied as a non-gaussian maximization method to extract the independent features from the training data set. High dimensional data is pre-processed using Principal Component Analysis (PCA) to reduce the number of dimensions. The author demonstrates how this method can effectively recognize 3D objects despite a small expected number of photons. This may be important for low light applications in medical or other settings.

Paper Details

Date Published: 7 September 2010
PDF: 7 pages
Proc. SPIE 7799, Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII, 77990A (7 September 2010); doi: 10.1117/12.859429
Show Author Affiliations
Cuong Manh Do, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 7799:
Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII
Mark S. Schmalz; Gerhard X. Ritter; Junior Barrera; Jaakko T. Astola, Editor(s)

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