105 research outputs found
Automatic visual recognition using parallel machines
Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity.
In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods.
Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration.
A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture
Scalable Data Parallel Algorithms for Texture Synthesis and Compression using Gibbs Random Fields
This paper introduces scalable data parallel algorithms for image
processing. Focusing on Gibbs and Markov Random Field model
representation for textures, we present parallel algorithms for
texture synthesis, compression, and maximum likelihood parameter
estimation, currently implemented on Thinking Machines CM-2 and CM-5.
Use of fine-grained, data parallel processing techniques yields
real-time algorithms for texture synthesis and compression that are
substantially faster than the previously known sequential
implementations. Although current implementations are on Connection
Machines, the methodology presented here enables machine independent
scalable algorithms for a number of problems in image processing and
analysis.
(Also cross-referenced as UMIACS-TR-93-80.
Mapping Signal Processing Algorithms on Parallel Arcidtectures
Electrical Engineerin
Centre for Information Science Research Annual Report, 1987-1991
Annual reports from various departments of the AN
Research in progress and other activities of the Institute for Computer Applications in Science and Engineering
This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics and computer science during the period April 1, 1993 through September 30, 1993. The major categories of the current ICASE research program are: (1) applied and numerical mathematics, including numerical analysis and algorithm development; (2) theoretical and computational research in fluid mechanics in selected areas of interest to LaRC, including acoustic and combustion; (3) experimental research in transition and turbulence and aerodynamics involving LaRC facilities and scientists; and (4) computer science
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