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Inference of multiple curves and sur...

University of Southern California.

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Inference of multiple curves and surfaces from sparse data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inference of multiple curves and surfaces from sparse data./
Author:	Guy, Gideon.
Description:	170 p.
Notes:	Adviser: Gerard Medioni.
Contained By:	Dissertation Abstracts International57-05B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9630757

Inference of multiple curves and surfaces from sparse data.
Guy, Gideon.

Inference of multiple curves and surfaces from sparse data. - 170 p.

Adviser: Gerard Medioni.

Thesis (Ph.D.)--University of Southern California, 1995.

The interpretation of the voting phase generates a dense saliency map which lends itself to easy extraction of high-level primitives. These include junctions and edges in 2-D, and junctions, space curves, and surfaces in 3-D.Subjects--Topical Terms:

626642
Computer Science.

Inference of multiple curves and surfaces from sparse data.
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035 $a (UMI)AAI9630757
035 $a AAI9630757
040 $a UMI $c UMI
100 1 $a Guy, Gideon. $3 1034225
245 1 0 $a Inference of multiple curves and surfaces from sparse data.
300 $a 170 p.
500 $a Adviser: Gerard Medioni.
500 $a Source: Dissertation Abstracts International, Volume: 57-05, Section: B, page: 3335.
502 $a Thesis (Ph.D.)--University of Southern California, 1995.
520 $a The interpretation of the voting phase generates a dense saliency map which lends itself to easy extraction of high-level primitives. These include junctions and edges in 2-D, and junctions, space curves, and surfaces in 3-D.
520 $a The result of the voting phase is represented in a compact way, by keeping a covariance matrix at each site (2 x 2 in 2-D, and 3 x 3 in 3-D).
520 $a We present results on synthetic and real data.
520 $a We address the problem of inferring high-level descriptions from sparse and noisy input data in 2-D and in 3-D. We claim that a local process cannot always capture meaningful structure among input data points, and more global constraints need to be imposed. Our system employs a global voting scheme that makes use of perceptual grouping constraints. The constraints relate to properties such as smoothness, cocurvilinearity, proximity, and curvature. These are captured into a single vector field applied at each input site. Thus, the voting process becomes a superposition of these fields over the entire input space.
520 $a The result is in the form of dense saliency maps for curves and junctions (in 2-D), and surfaces, intersections between surfaces, and 3-D junctions (in 3-D). These saliency maps are then used to guide a 'marching' process to generate a high-level description. In the 2-D case, the description is in terms of connected curves and junctions, while in the 3-D case it consists of polygonal meshes, polygonal space curves, and 3-D junctions.
520 $a The scheme is non-iterative, parameter-free, can handle multiple objects, each with any size genus, and does not require an initial guess. Moreover, it can handle large amounts of noise, both in the form of erroneous input primitives, and in the localization accuracy of valid input samples.
590 $a School code: 0208.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0544
690 $a 0984
710 2 $a University of Southern California. $3 700129
773 0 $t Dissertation Abstracts International $g 57-05B.
790 $a 0208
790 1 0 $a Medioni, Gerard, $e advisor
791 $a Ph.D.
792 $a 1995
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9630757