Proceedings Article10.1109/ICSMC.1991.169754
Object recognition on the hypercube
Suchendra M. Bhandarkar,L.-C. Sung +1 more
- 13 Oct 1991
- pp 625-630
TL;DR: The authors present a parallel interpretation tree search algorithm for object recognition using sparse range or tactile data on the Intel iPSC/2 hypercube multicomputer and it has shown that the requirement for uniform load sharing leads to increased inter-processor communication.
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Abstract: The authors present a parallel interpretation tree search algorithm for object recognition using sparse range or tactile data on the Intel iPSC/2 hypercube multicomputer. The objects are typically those which can be approximated as a piecewise combination of polyhedra and which a robot would encounter in an industrial scene during the process of automated inspection or automated assembly. Three strategies for mapping the interpretation tree search process on the hypercube are considered. These are breadth-first mapping, depth-first mapping and depth-first mapping with load sharing. The algorithm has been experimentally verified on synthetic tactile data from two-dimensional scenes. It has shown that the requirement for uniform load sharing leads to increased inter-processor communication. >
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Citations
A novel reconfigurable multiprocessor for robot vision
Suchendra M. Bhandarkar,Hamid R. Arabnia +1 more
- 08 May 1994
TL;DR: A novel reconfigurable architecture based on a multi-ring multiprocessor network that is well suited for a number of problems in robot vision and to contain a single 1-factor of the Boolean hypercube in any given configuration is described.
3
References
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The combinatorics of object recognition in cluttered environments using constrained search
TL;DR: Formal bounds are established on the efficacy of using the Hough transform to preselect likely subspaces of the search space, showing that the problem remains exponential, but that in practical terms the size of the problem is significantly decreased.
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W.E.L. Grimson
- 01 Feb 1988
TL;DR: In this paper, the authors established formal bounds on the combinatorics of this approach and showed that the expected complexity of recognizing isolated objects is quadratic in the number of model and sensory fragments, but exponential in the size of the correct interpretation.
The Combinatorics of Local Constraints in Model-Based Recognition and Localization,
W.E.L. Grimson
- 01 Apr 1984
TL;DR: In this article, a set of constraints for sparse sensory data that are applicable to a wide variety of sensors and examine their completeness and exhaustiveness are derived. But these constraints are not applicable to many types of local constraints, other than those used here.
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The combinatorics of local constraints in model-based recognition and localization from sparse data
TL;DR: A set of constraints for sparse sensory data that are applicable to a wide variety of sensors are derived, and their characteristics are examined and it is shown that these bounds are consistent with empirical results reported previously.
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