Recognition of rotated images using the multi-valued neuron and rotation-invariant 2D Fourier descriptors

Evgeni Aizenberg; Irving J. Bigio; Eladio Rodriguez-Diaz

doi:10.1117/12.909123

2 February 2012 Recognition of rotated images using the multi-valued neuron and rotation-invariant 2D Fourier descriptors

Evgeni Aizenberg, Irving J. Bigio, Eladio Rodriguez-Diaz

Author Affiliations +

Proceedings Volume 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II; 82950A (2012) https://doi.org/10.1117/12.909123
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

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.

Citation Download Citation

Evgeni Aizenberg, Irving J. Bigio, and Eladio Rodriguez-Diaz "Recognition of rotated images using the multi-valued neuron and rotation-invariant 2D Fourier descriptors", Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 82950A (2 February 2012); https://doi.org/10.1117/12.909123

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KEYWORDS

Image compression

Neurons

Photonic integrated circuits

Image classification

Satellite imaging

Satellites

Feature extraction

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