Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated optical motion detection
Integrated optical motion detection
Visual reconstruction
The connection machine
Analog VLSI and neural systems
Analog VLSI and neural systems
Comparison of the Efficiency of Deterministic and Stochastic Algorithms for Visual Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analog VLSI chip for thin-plate surface interpolation
Advances in neural information processing systems 1
Resistive networks for computer vision: an overview
An introduction to neural and electronic networks
Measurement of Visual Motion
Robot Vision
A Regularized Solution to Edge Detection
A Regularized Solution to Edge Detection
Parallel Algorithms for Computer Vision on the Connection Machine
Parallel Algorithms for Computer Vision on the Connection Machine
Parallel Networks for Machine Vision
Parallel Networks for Machine Vision
A 590,000 transistor 48,000 pixel, contrast sensitive, edge enhancing, CMOS imager-silicon retina
ARVLSI '95 Proceedings of the 16th Conference on Advanced Research in VLSI (ARVLSI'95)
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Vision is simple. We open our eyes and, instantly, the world surrounding us is perceived in all its splendor. Yet Artificial Intelligence has been trying with very limited success for over 20 years to endow machines with similar abilities. A large van, filled with computers and driving unguided at a mile per hour across gently sloping hills in Colorado and using a laser-range system to see is the most we have accomplished so far. On the other hand, computers can play a decent game of chess or prove simple mathematical theorems. It is ironic that we are unable to reproduce perceptual abilities which we share with most animals while some of the features distinguishing us from even our closest cousins, chimpanzees, can be carried out by machines. Vision is difficult.