Kumar V., Gopalakrishnan P.S., Kanal L.N. (eds.) Parallel Algorithms for Machine Intelligence and Vision

Springer, 1990. — 445 p.Many algorithms for solving machine intelligence and vision problems are computationally very demanding. Algorithms used for decision making, path planning, machine vision, speech recognition, and pattern recognition require substantially more power than is available today from commercially feasible sequential computers. Although the speed of sequential computers has been been increasing over time, there are indications that solid state physics will impose limits that cannot be circumvented except through parallel processing. Parallel processing can also be very cost effective, as the advances in VLSI technology have made it easy and inexpensive to construct large parallel processing systems. Hence, there has been a great interest in the development of parallel algorithms for these problems. This volume brings together some of the recent research on parallel algorithms for machine intelligence and vision. It includes papers on subjects such as combinatorial search, problem solving, logic programming, and computer vision.Scalable Parallel Formulations of Depth-First Search
Parallel Heuristic Search: Two Approaches
Distributed Game Tree Search
Multiprocessing of Combinatorial Search Problems
Parallel Problem Solving
PRISM: A Testbed for Parallel Control
Or-Parallelism in Natural Language Parsing
Parallelism in Computer Vision: A Review
Real-Time, Parallel Motion Tracking of Three Dimensional Objects from Spatiotemporal Sequences
Parallel Simulation of a Connectionist Stereo Algorithm on a Shared-Memory Multiprocessor
Parallel Algorithms for Image Template Matching
Image Computations on Fine Grain Electro-Optical Arrays