Three-dimensional Distortion-tolerant Object Recognition using Computational Integral Imaging and Statistical Pattern Analysis 


Vol. 34,  No. 10, pp. 1111-1116, Oct.  2009


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  Abstract

In this paper, we discuss distortion-tolerant pattern recognition using computational integral imaging reconstruction. Three-dimensional object information is captured by the integral imaging pick-up process. The captured information is numerically reconstructed at arbitrary depth-levels by averaging the corresponding pixels. We apply Fisher linear discriminant analysis combined with principal component analysis to computationally reconstructed images for the distortion-tolerant recognition. Fisher linear discriminant analysis maximizes the discrimination capability between classes and principal component analysis reduces the dimensionality with the minimum mean squared errors between the original and the restored images. The presented methods provide the promising results for the classification of out-of-plane rotated objects.

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  Cite this article

[IEEE Style]

S. Yeom, D. Lee, J. Son, S. Kim, "Three-dimensional Distortion-tolerant Object Recognition using Computational Integral Imaging and Statistical Pattern Analysis," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 10, pp. 1111-1116, 2009. DOI: .

[ACM Style]

Seokwon Yeom, Dong-Su Lee, Jung-Young Son, and Shin-Hwan Kim. 2009. Three-dimensional Distortion-tolerant Object Recognition using Computational Integral Imaging and Statistical Pattern Analysis. The Journal of Korean Institute of Communications and Information Sciences, 34, 10, (2009), 1111-1116. DOI: .

[KICS Style]

Seokwon Yeom, Dong-Su Lee, Jung-Young Son, Shin-Hwan Kim, "Three-dimensional Distortion-tolerant Object Recognition using Computational Integral Imaging and Statistical Pattern Analysis," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 10, pp. 1111-1116, 10. 2009.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880