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Titolo:
3-DIMENSIONAL LOCATION ESTIMATION OF TRAJECTORIES OF POINT TARGETS USING A PROJECTION-BASED TRANSFORMATION METHOD
Autore:
CHOI JH; RAJALA SA;
Indirizzi:
CHONBUK NATL UNIV,DEPT COMP ENGN CHONJU 560756 SOUTH KOREA N CAROLINA STATE UNIV,DEPT ELECT & COMP ENGN RALEIGH NC 27695
Titolo Testata:
Optical engineering
fascicolo: 3, volume: 34, anno: 1995,
pagine: 933 - 939
SICI:
0091-3286(1995)34:3<933:3LEOTO>2.0.ZU;2-3
Fonte:
ISI
Lingua:
ENG
Soggetto:
SIGNAL-DETECTION-THEORY; HOUGH TRANSFORM;
Keywords:
TARGET MOTION DETECTION ESTIMATION; RADON TRANSFORM; HOUGH TRANSFORM; COMPUTER VISION; REMOTE SENSING;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
19
Recensione:
Indirizzi per estratti:
Citazione:
J.H. Choi e S.A. Rajala, "3-DIMENSIONAL LOCATION ESTIMATION OF TRAJECTORIES OF POINT TARGETS USING A PROJECTION-BASED TRANSFORMATION METHOD", Optical engineering, 34(3), 1995, pp. 933-939

Abstract

A new computational approach for determining the parameters that characterize the locations of trajectories of point targets in a 3-D spaceis described. The targets of concern are dim, unresolved point targets moving along straight paths across the same field of view. Since thetarget's signal-to-noise ratio is low and the spatial extent of the target is less than a pixel, one must rely on integration over a targettrack that spans many image frames. The proposed method estimates these parameters by transforming the entire set of time-sequential imagesof a constant field of view into the projection space by using a modified Radon transform. Since the 3-D (spatiotemporal) data can be decomposed into 2-D multiple-view representations along arbitrary orientations, the Radon transform enables us to analyze the 3-D problem in terms of its 2-D projections. When this generalization of the Hough transform-based algorithm using the Radon transform is applied to a set of real infrared images, it produces promising estimation results even under noisy conditions. The noise in the images is assumed to be additivewhite Gaussian.

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Documento generato il 11/07/20 alle ore 21:12:37