Proceedings Article10.1109/SPDP.1992.242757
Surface reconstruction in parallel
S. Talele,Theodore Johnson,Panos E. Livadas +2 more
- 01 Dec 1992
- pp 102-106
2
TL;DR: By exploiting the structure of a directed toroidal graph, the authors have developed a parallel solution to find the shortest path and have obtained a significant speedup to a computationally intensive problem.
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Abstract: By exploiting the structure of a directed toroidal graph, the authors have developed a parallel solution to find the shortest path. A parallel dynamic programming solution to finding the minimum cost path is presented. First the authors map the toroidal graph to a planar graph, whose structure is exploited to form a parallel algorithm suitable for a message-passing parallel architecture. The problem has applications in surface reconstruction, where contours of a surface are represented as graphs. Finding the shortest-path in these graphs corresponds to finding a best-fit surface over the contours. By parallelizing the solution, the authors have obtained a significant speedup to a computationally intensive problem. Since generic message passing is used for interprocessor communication, the proposed algorithm can be implemented in any distributed or parallel environment. In a heterogeneous environment, relative processor speed and memory would have to be considered for load balancing. >
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Citations
An Efficient Parallel Algorithm for Computing the Gaussian Convolution of Multi-dimensional Image Data
TL;DR: A parallel convolution algorithm for estimating the partial derivatives of 2D and 3D images on distributed-memory MIMD architectures by exploiting the separable characteristics of the Gaussian filter.
A parallel algorithm for surface triangulation
TL;DR: A parallel algorithm to find the minimum cost acceptable path in an m by n toroidal graph is presented and an implementation of the parallel algorithm on a parallel architecture is shown, using a message passing approach.
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Geometrically deformed models: a method for extracting closed geometric models form volume data
James V. Miller,David E. Breen,William Edward Lorensen,Robert M. O'Bara,Michael J. Wozny +4 more
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TL;DR: This work proposes a new approach to the problem of generating a simple topologically-closed geometric model from a point-sampled volume data set, called Geometrically Deformed Model or GDM, which is created by placing a 'seed' model in thevolume data set.
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TL;DR: This work proposes a new approach to the problem of generating a simple topologically-closed geometric model from a point-sampled volume data set and calls such a model a Geometrically Deformed Model or GDM.
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