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Proceedings Paper

Recognition of rotated images using the multi-valued neuron and rotation-invariant 2D Fourier descriptors
Author(s): Evgeni Aizenberg; Irving J. Bigio; Eladio Rodriguez-Diaz
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Paper Abstract

The Fourier descriptors paradigm is a well-established approach for affine-invariant characterization of shape contours. In the work presented here, we extend this method to images, and obtain a 2D Fourier representation that is invariant to image rotation. The proposed technique retains phase uniqueness, and therefore structural image information is not lost. Rotation-invariant phase coefficients were used to train a single multi-valued neuron (MVN) to recognize satellite and human face images rotated by a wide range of angles. Experiments yielded 100% and 96.43% classification rate for each data set, respectively. Recognition performance was additionally evaluated under effects of lossy JPEG compression and additive Gaussian noise. Preliminary results show that the derived rotation-invariant features combined with the MVN provide a promising scheme for efficient recognition of rotated images.

Paper Details

Date Published: 2 February 2012
PDF: 8 pages
Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 82950A (2 February 2012); doi: 10.1117/12.909123
Show Author Affiliations
Evgeni Aizenberg, Boston Univ. (United States)
Irving J. Bigio, Boston Univ. (United States)
Eladio Rodriguez-Diaz, Boston Univ. Medical Campus (United States)


Published in SPIE Proceedings Vol. 8295:
Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Karen O. Egiazarian; John Recker; Guijin Wang; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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