Recognition of rotated images using the multi-valued neuron and rotation-invariant 2D Fourier descriptors
Evgeni Aizenberg, Irving J. Bigio, Eladio Rodriguez‐Diaz · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
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.