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Tensor voting in computer vision, vi...

University of Southern California.

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Tensor voting in computer vision, visualization, and higher dimensional inferences.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Tensor voting in computer vision, visualization, and higher dimensional inferences./
Author:	Tang, Chi-Keung.
Description:	247 p.
Notes:	Adviser: Gerard Medioni.
Contained By:	Dissertation Abstracts International62-06B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3018039
ISBN:	9780493286358

Tensor voting in computer vision, visualization, and higher dimensional inferences.
Tang, Chi-Keung.

Tensor voting in computer vision, visualization, and higher dimensional inferences. - 247 p.

Adviser: Gerard Medioni.

Thesis (Ph.D.)--University of Southern California, 2000.

The proposed theory and the implementation consolidate the existing tensor voting foundation. Promising further research and development in a wide variety of applications, and in any dimensions, are possible.

ISBN: 9780493286358Subjects--Topical Terms:

626642
Computer Science.

Tensor voting in computer vision, visualization, and higher dimensional inferences.
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020 $a 9780493286358
035 $a (UMI)AAI3018039
035 $a AAI3018039
040 $a UMI $c UMI
100 1 $a Tang, Chi-Keung. $3 1034234
245 1 0 $a Tensor voting in computer vision, visualization, and higher dimensional inferences.
300 $a 247 p.
500 $a Adviser: Gerard Medioni.
500 $a Source: Dissertation Abstracts International, Volume: 62-06, Section: B, page: 2800.
502 $a Thesis (Ph.D.)--University of Southern California, 2000.
520 $a The proposed theory and the implementation consolidate the existing tensor voting foundation. Promising further research and development in a wide variety of applications, and in any dimensions, are possible.
520 $a This dissertation is founded on the basic Tensor Voting Formalism. It provides a unified computational framework, making use of the continuity constraint to generate layered descriptions in terms of surfaces, regions, curves, and labeled junctions, from sparse, noisy, binary data in 2-D or 3-D. The method is non-iterative, does not depend on initialization, robust to spurious outlier noise, and the only free parameter is the size of the neighborhood, or the scale of analysis, which is indeed a property of visual perception.
520 $a In this thesis, feature extraction from tensor data is first studied. A number of new feature extraction algorithms is designed and implemented. Coherent features, such as a hole-free triangulation mesh, and a curve consisting of connected and oriented curve segments, are typical outputs of these algorithms.
520 $a While the basic formalism provides excellent results for smooth structures, it only detects discontinuities but does not localize them. A methodology for feature integration is proposed. This extended system is applied in a variety of visualization problems, and very encouraging results are obtained. The feature integration is further upgraded bar the use of second order, curvature information, which is absent from the basic formalism.
520 $a The tensor voting formalism is also generalized to higher dimensions, and the 8-D version is applied to solve the problem of epipolar geometry estimation. Given a set of noisy point correspondences in two images as obtained from two views of a static scene without correspondences, even in the presence of moving objects, our method extract all good matches while rejecting all outliers.
590 $a School code: 0208.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a University of Southern California. $3 700129
773 0 $t Dissertation Abstracts International $g 62-06B.
790 $a 0208
790 1 0 $a Medioni, Gerard, $e advisor
791 $a Ph.D.
792 $a 2000
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3018039