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Independent Component Analysis features of Color & Stereo images

Independent Component Analysis features of Color & Stereo images. Authors: Patrik O. Hoyer Aapo Hyvarinen CIS 526: Neural Computation. Presented by: Ajay Kumar Yadav. Overview. Introduction Background Study Data Preprocessing Color Image Experiment Stereo Image Experiment

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Independent Component Analysis features of Color & Stereo images

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  1. Independent Component Analysis features of Color & Stereo images Authors: Patrik O. Hoyer Aapo Hyvarinen CIS 526: Neural Computation Presented by: Ajay Kumar Yadav

  2. Overview • Introduction • Background Study • Data Preprocessing • Color Image Experiment • Stereo Image Experiment • Conclusion

  3. Introduction • Visual Cortex: part of the cerebral cortex responsible for processing visual stimuli. (Static, Moving & Pattern Recognition) • Receptive fields are divided as: • Sub-regions that exert an excitatory influence. (light grey) • Sub-regions that exert an inhibitory influence. (dark grey) • Stimulus Influence also depends on size, orientation and position (Hubel & Wiesel’s -1962, DeValois-1982, DeAngelis-1993)

  4. Contd.. • Cones consist of three cell each responsible for each RGB component. (tuned at wavelength of 430, 535, 590 nanometer) • The degree to which the images are non-corresponding is defined as binocular disparity. It is used to determine the distance of an object from oneself, and its relation to the fixation plane, is called stereopsis.

  5. Background Study • The sparseness-maximization network and ICA are closely related. (Olshausen and Field 1997) • Hateren and Vander Schaaf qualitatively compared the filter learned by ICA to measurements of neural receptive fields. • Van Hateren and Ruderman proved ICA also fit the receptive field properties for video images.

  6. Data Preprocessing • ICA preprocess the data in two steps: • The mean of the data is subtracted to center the data on the origin. • Whiten the data z = Vx, so that • Goal: ICA transform W to minimize the statistical dependencies between the estimated sources. • After convergence

  7. Color Image Experiment • Standard RGB values are considered as input data assuming the transformation to cone outputs to be roughly linear. • A total of 50,000 12 by 12 pixel image patches were sampled randomly with dimensionality of 432. • Data is preprocessed and correlation matrix and eigen vectors are calculated. • Constant RGB value is used in the display.

  8. Correlation matrix • Data is projected in 160 principle component before whitening. Two reasons are: • To emulate the real neuron functionality • Dimension is dropped to lower computational cost.

  9. Results **ICA basis of color images** **Color content of three ICA filters** **Percentage of achromatic**

  10. Stereo Image Experiment • Stereo image data: • 5 focus points at random from each image are selected and estimated the disparities. • Randomly sampled 16*16 pixel corresponding image in patch area of 300*300 pixels centered on each focus point. • Due to the fluctuation patches are often similar but horizontally shifted. • During the preprocessing local mean was removed from each component and correlation matrix and eigenvalue decomposition are calculated.

  11. Stereo Images Equal Response Varying Response **PCA Basis of Stereo Image** **ICA Basis of Stereo Image**

  12. Ocular Dominance • The shift from one eye to the other takes place over a distance of less than 50 microns, therefore column dominated by one eye. • If the sampling areas is smaller, correlation between the patches would be higher. • If the area gets larger, the dependencies between the left and right patches get weaker

  13. Disparity Tuning Analysis • To analyze the disparity tuning several ICA basis were estimated using different number random seeds. • Only relatively high frequency well localized binocular vectors are selected

  14. Disparity Tuning Curves • Each patch is shown to both the eyes to get the tuning curve and the mean is considered as final curve. • These curves are defined in two parts: • Tuned excitatory • Tuned inhibitory • Tuned excitatory shows a strong peak at zero. • Tuned inhibitory shows opposite polarity. • Near unit’s right receptive slightly shifted giving positive preferred disparity. • Far unit has opposite positional offset with negative disparities.

  15. Conclusion • ICA could be applied in denoising, compression or pattern recognition of color or stereo data. • ICA can be used to model computational properties of visual cortex (V1) cell. • Limitation: • Since ICA emulate the behavior of cones it may fail in dark or un-illuminated images. • To get better correlation basis patch needed to be small which may vary. • Nonlinearities inherent in the conversion from RGB to cones response will affect the ICA result.

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