Properties of independent components of self-motion optical flow

In this paper we describe the properties of independent components of optical flow of moving objects. Video sequences of objects seen by an observer moving at various angles, directions and distances are used to produce optical flow maps. These maps are then, recessed using independent component analysis, which yields filters that resemble the receptive fields of dorsal medial superior temporal cells of the primate brain. Contraction, expansion, rotation and translation receptive fields have been identified. Our results support Barlow's sensory coding theory and are in-line with other work on independent components of image and video intensities.

[1]  R. Wurtz,et al.  Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity to large-field stimuli. , 1991, Journal of neurophysiology.

[2]  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.

[3]  T. Sejnowski,et al.  A selection model for motion processing in area MT of primates , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[4]  Kechen Zhang,et al.  Emergence of Position-Independent Detectors of Sense of Rotation and Dilation with Hebbian Learning: An Analysis , 1999, Neural Computation.

[5]  T J Sejnowski,et al.  A Model for Encoding Multiple Object Motions and Self-Motion in Area MST of Primate Visual Cortex , 1998, The Journal of Neuroscience.

[6]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[7]  J. H. Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .

[8]  Ralph Linsker,et al.  Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network , 1992, Neural Computation.

[9]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[10]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[11]  Hans-Hellmut Nagel,et al.  On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results , 1987, Artif. Intell..

[12]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[13]  D. Field,et al.  Natural image statistics and efficient coding. , 1996, Network.

[14]  S. Grossberg,et al.  A neural model of motion processing and visual navigation by cortical area MST. , 1999, Cerebral cortex.

[15]  Steven S. Beauchemin,et al.  The computation of optical flow , 1995, CSUR.

[16]  Terrence J. Sejnowski,et al.  Blind separation and blind deconvolution: an information-theoretic approach , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[17]  Te-Won Lee,et al.  Independent Component Analysis , 1998, Springer US.

[18]  J. Nadal.  Non linear neurons in the low noise limit : a factorial code maximizes information transferJean , 1994 .

[19]  J. Nadal,et al.  Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer Network 5 , 1994 .

[20]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[21]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.