Cooperative algorithms for solving random-dot stereograms

This report examines a number of parallel . algorithms for solving random-dot stereograms. A new class of algorithms based on the Boltzmann Machine is introduced and compared to previously developed algorithms. The report includes a review of the stereo correspondence problem and of cooperative techniques for solving this problem. The use of energy functions for characterizing the computational problem, and the use of stochastic optimization techniques for solving the problem are explained.

[1]  John E. W. Mayhew,et al.  Psychophysical and Computational Studies Towards a Theory of Human Stereopsis , 1981, Artif. Intell..

[2]  Takeo Kanade,et al.  Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[4]  B. Julesz Binocular depth perception of computer-generated patterns , 1960 .

[5]  J P Frisby,et al.  PMF: A Stereo Correspondence Algorithm Using a Disparity Gradient Limit , 1985, Perception.

[6]  Steven W. Zucker,et al.  On the Foundations of Relaxation Labeling Processes , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[8]  K P HornBerthold,et al.  The variational approach to shape from shading , 1986 .

[9]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.

[10]  David Marr,et al.  Vision: A computational investigation into the human representation , 1983 .

[11]  David L. Waltz,et al.  Understanding Line drawings of Scenes with Shadows , 1975 .

[12]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[13]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[14]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Geoffrey E. Hinton Relaxation and its role in vision , 1977 .

[16]  Patrick Henry Winston,et al.  The psychology of computer vision , 1976, Pattern Recognit..

[17]  Michael J. Brooks,et al.  The variational approach to shape from shading , 1986, Comput. Vis. Graph. Image Process..

[18]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[19]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[20]  T. Poggio,et al.  A generalized ordering constraint for stereo correspondence , 1984 .

[21]  J. Marroquín Surface Reconstruction Preserving Discontinuities , 1984 .

[22]  Richard Szeliski,et al.  Solving Random- Dot Ste reog rams Using the Heat Equation , 2020, CVPR 1985.

[23]  T. Poggio,et al.  Ill-Posed Problems and Regularization Analysis in Early Vision , 1984 .

[24]  Tomaso Poggio,et al.  Cooperative computation of stereo disparity , 1988 .

[25]  G. Sperling Binocular Vision: A Physical and a Neural Theory , 1970 .

[26]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[28]  B. Julesz Foundations of Cyclopean Perception , 1971 .

[29]  J P Frisby,et al.  The Computation of Binocular Edges , 1980, Perception.

[30]  W E Grimson,et al.  A computer implementation of a theory of human stereo vision. , 1981, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[31]  Berthold K. P. Horn Understanding Image Intensities , 1977, Artif. Intell..

[32]  J. L. Marroquin,et al.  Design of Cooperative Networks , 1983 .

[33]  Thomas O. Binford,et al.  Depth from Edge and Intensity Based Stereo , 1981, IJCAI.

[34]  C Koch,et al.  Analog "neuronal" networks in early vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[35]  R. D. Arnold Automated stereo perception , 1983 .

[36]  Demetri Terzopoulos,et al.  Multiresolution computation of visible-surface representations , 1984 .

[37]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.