On parallel stereo

We review some of the open issues in computational stereo. In particular, we will discuss the problem of extracting better matching primitives and of dealing with occlusions. Markov Random Field models - an extension of standard regularization - suggest sophisticated stereo matching algorithms. They are, however, ill-suited to efficient, real-time applications. We will conclude reviewing a new simple but fast algorithm implemented by one of us (Drumheller, 1986) on the TMC Connection Machine (TM) computer. Some of its features are: (a) the potential for combining different primitives, including color information; (b) the use of a stronger and new formulation of the uniqueness constraint; and (c) its disparity representation that maps efficiently into the architecture of the Connection Machine computer.

Authors

• Michael Drumheller
• Tomaso A. Poggio

Keywords

• Humans
• Application software
• Feature extraction
• Computational intelligence
• Machine intelligence
• Laboratories
• Markov random fields
• Computer architecture
• Robots
• Robustness
• Markov Random Field
• Stereo Matching
• Volume Change
• Typical Features
• Convolution
• Local Information
• Line-of-sight
• Natural Images
• Functional Support
• Bitplane
• Feature Matching
• Drop In The Number
• Geometric Distortion
• Parallel Algorithm
• Specific Hardware
• Correct Matches
• Potential Matches
• Entire Zone
• Stereo Pairs
• Memory Block
• Disparity Map
• Matching Rule