Parallel split-level relaxation
Ashok Samal,Thomas C. Henderson +1 more
TL;DR: This paper gives a framework for solving the scene analysis problem in a parallel processing environment, using split-level relaxation, and shows that it is indeed advantageous to use multiprocessors to solve this problem.
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Abstract: The goal of high level vision is to identify a set of regions in a given image. This has been called by various names: the scene labeling problem’, the consistent labeling problem2, the constraint satisfaction problem3, Waltz filtering4, the satisfying assignment problem5, etc. There are several approaches to solve this problem, including backtracking, graph matching and relaxation. A new method called split-level relaxation, which is based on discrete relaxation was proposed in Ref. 6. It takes care of multiple semantic constraints by considering each of them independently. The problem is known to be NP-complete, so it takes a long time to solve. With the advent of multiprocessors, it is now imperative to see if the problem can be solved faster in the average case. In this paper we give a framework for solving the scene analysis problem in a parallel processing environment, using split-level relaxation. Experiments done on a multiprocessor show that it is indeed advantageous to use multiprocessors to solve this problem.
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Citations
Algorithms for the Satisfiability (SAT) Problem: A Survey,
Jun Gu,Paul Walton Purdom,John Franco,Benjamin W. Wah +3 more
- 01 Jan 1996
TL;DR: This survey presents a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective and describes sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms.
Parallel consistent labeling algorithms
Ashok Samal,Thomas C. Henderson +1 more
TL;DR: It is shown that any parallel algorithm for enforcing are consistency in the worst case must have O(na) sequential steps, wheren is number of nodes, anda is the number of labels per node.
55
•Book
Algorithms for the Satisfiability Problem
Jun Gu,Paul Walton Purdom,John Franco,Benjamin W. Wah +3 more
- 31 Dec 2018
TL;DR: An instance of the satisfiability (SAT) problem is a Boolean formula that has three components: A set of n variables, a set of literals, and a setOf literals combined by just logical or (V) connectives.
A novel discrete relaxation architecture
TL;DR: A fine-grained, massively parallel hardware computer architecture has been designed for the DRA5 algorithm, and it is shown that many orders of magnitude of efficiency improvement can be reached on such a hardware architecture.
25
Parallel path consistency
Steven Y. Susswein,Thomas C. Henderson,Joseph L. Zachary,Charles Hansen,Paul Hinker,Gary C. Marsden +5 more
TL;DR: Preliminary work has shown linear performance increases for parallelized path consistency and also shown that in many cases performance is significantly better than the theoretical worst case, leading to the belief that parallel path consistency may be a superior filtering technique.
13
References
Reduction operations for constraint satisfaction
TL;DR: The problem of labeling a set of units in such a way as to satisfy an order-N compatibility relation is discussed and shown to be NP-complete.
56
Networks of constraints: Fundamental properties and applications to picture processing
TL;DR: Constraints are treated algebraically, and the solution of a system of linear equations in this algebra provides an approximation of the minimal network, and this solution is proved exact in special cases, e.g., for tree-like and series-parallel networks and for classes of relations for which a distributive property holds.