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On the Application of Massively Parallel SIMD Tree Machines to Certain Intermediate-Level Vision Tasks

Hussein Ibrahim; John R. Kender; David Elliot Shaw

Title:
On the Application of Massively Parallel SIMD Tree Machines to Certain Intermediate-Level Vision Tasks
Author(s):
Ibrahim, Hussein
Kender, John R.
Shaw, David Elliot
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-221-85
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
In this paper, we examine the implementation of two middle-level image understanding tasks on fine-grained tree-structured SIMD machines, which have highly efficient VLSI implementations. We first present one such massively parallel machine called NON-VON, and summarize the cost/performance trade-offs of such machines for vision taks. We follow with a more detailed description of the NON-VON architecture (a prototype of which has been operational since January 1985), and of the high-level parallel language in which our algorithms have been written and simulated. The heart of the paper consists of the description and analysis of algorithms for a representative Hough transform, and of an algorithm for the interpretation of moving light displays. Novel algorithmic techniques are motivated and described, and simulation timings are presented and discussed. We conclude that it is possible to exploit the available massive parallelism while avoiding many of the communication bottlenecks common at this level of image understanding, by carefully and inexpensively duplicating data and/or control information, and by delaying or avoiding the reporting of intermediate results.
Subject(s):
Computer science
Item views:
108
Metadata:
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