Stereoscopic Depth Perception Using a Model Based on the Primary Visual Cortex

This work describes an approach inspired by the primary visual cortex using the stimulus response of the receptive field profiles of binocular cells for disparity computation. Using the energy model based on the mechanism of log-Gabor filters for disparity encodings, we propose a suitable model to consistently represent the complex cells by computing the wide bandwidths of the cortical cells. This way, the model ensures the general neurophysiological findings in the visual cortex (V1), emphasizing the physical disparities and providing a simple selection method for the complex cell response. The results suggest that our proposed approach can achieve better results than a hybrid model with phase-shift and position-shift using position disparity alone.

[1]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[2]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[3]  Ning Qian,et al.  Computing Stereo Disparity and Motion with Known Binocular Cell Properties , 1994, Neural Computation.

[4]  M. Carandini,et al.  Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex. , 2000, Journal of neurophysiology.

[5]  Dennis Gabor,et al.  Theory of communication , 1946 .

[6]  Ning Qian,et al.  Physiological computation of binocular disparity , 1997, Vision Research.

[7]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[8]  Bruce G. Cumming,et al.  Adaptation to Natural Binocular Disparities in Primate V1 Explained by a Generalized Energy Model , 2008, Neuron.

[9]  M. Jenkin.  Physiologically based models of binocular depth perception , 2011 .

[10]  I. Ohzawa,et al.  Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. , 1990, Science.

[11]  Helder Araújo,et al.  Bio-inspired Binocular Disparity with Position-Shift Receptive Field , 2010, DoCEIS.

[12]  G. Poggio,et al.  Binocular interaction and depth sensitivity in striate and prestriate cortex of behaving rhesus monkey. , 1977, Journal of neurophysiology.

[13]  Ning Qian,et al.  Vision in 3D Environments: Physiologically based models of binocular depth perception , 2011 .

[14]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[15]  Heiko Hirschmüller,et al.  Evaluation of Cost Functions for Stereo Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  I. Ohzawa,et al.  Neural mechanisms for processing binocular information II. Complex cells. , 1999, Journal of neurophysiology.

[17]  A. Parker.  Binocular depth perception and the cerebral cortex , 2007, Nature Reviews Neuroscience.

[18]  Christopher Joseph Pal,et al.  Learning Conditional Random Fields for Stereo , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  D. Burr,et al.  Feature detection in human vision: a phase-dependent energy model , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[20]  I. Ohzawa,et al.  Neural mechanisms for processing binocular information I. Simple cells. , 1999, Journal of neurophysiology.

[21]  G C DeAngelis,et al.  The physiology of stereopsis. , 2001, Annual review of neuroscience.

[22]  Peter Kovesi,et al.  Image Features from Phase Congruency , 1995 .

[23]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[24]  N. Qian.  Binocular Disparity and the Perception of Depth , 1997, Neuron.

[25]  Bruce G Cumming,et al.  Sensors for impossible stimuli may solve the stereo correspondence problem , 2007, Nature Neuroscience.

[26]  Bertram E. Shi,et al.  Estimating disparity with confidence from energy neurons , 2007, NIPS.

[27]  Ning Qian,et al.  A Coarse-to-Fine Disparity Energy Model with Both Phase-Shift and Position-Shift Receptive Field Mechanisms , 2004, Neural Computation.

[28]  I. Ohzawa,et al.  Encoding of binocular disparity by simple cells in the cat's visual cortex. , 1996, Journal of neurophysiology.

[29]  I. Ohzawa,et al.  Encoding of binocular disparity by complex cells in the cat's visual cortex. , 1996, Journal of neurophysiology.

[30]  Izumi Ohzawa,et al.  Mechanisms of stereoscopic vision: the disparity energy model , 1998, Current Opinion in Neurobiology.

[31]  Bruce G Cumming,et al.  A simple model accounts for the response of disparity-tuned V1 neurons to anticorrelated images , 2002, Visual Neuroscience.

[32]  Gabriel Cristóbal,et al.  Self-Invertible 2D Log-Gabor Wavelets , 2007, International Journal of Computer Vision.

[33]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.