Self-Organized three dimensional feature extraction of MRI and CT
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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A computational theory concept generating saliency maps from feature maps generated in the bottom-up using various filters such as Fourier transformation was discussed. We proposed saliency map related to motion based on self-organized feature extracting not using general filter such as Fourier transform. We introduce the ICA base function to realize the self-organized Saliency Map. We extend the ICA base function estimation to apply for the non-uniform positioned photoreceptor cells which receives the current image and the previous image to get the motion information. We show the effectiveness of our model by applying this model for real images.