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Rama Chellapa

dblp:147/6519 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 1997
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorArtificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
3D vision · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
motion estimation
0.011988
3-D motion estimation using a sequence of noisy stereo images · CVPR 1988
Computer vision › 3D vision › motion estimation
motion parameter estimation
0.011988
3-D motion estimation using a sequence of noisy stereo images · CVPR 1988
Computer vision › 3D vision › motion estimation › 3d motion estimation
stereo motion estimation
0.011988
3-D motion estimation using a sequence of noisy stereo images · CVPR 1988
Computer vision › 3D vision
structure from motion
0.011988
3-D motion estimation using a sequence of noisy stereo images · CVPR 1988

Methods — techniques the papers use, named apart from their topics

quaternions · 0.0nonlinear filter · 0.0kinematic model · 0.0bilinear state-space model · 0.0
YearPublicationVenuePosition
1997 Expert Summaries
abstract
Provides an abstract for each of the exprt presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.
Rama Chellapa, Renato De Mori, Georgios B. Giannakis, Hans Georg Musmann, Hermann Ney, Mark J. T. Smith, John R. Treichler, Michael D. Zoltowski
ICASSP1
1988 3-D motion estimation using a sequence of noisy stereo images
abstract
The authors discuss a kinematic-model-based approach for the estimation of 3-D motion parameters from a sequence of noisy stereo images. The approach consists of representing the constant acceleration translational motion and constant angular velocity or constant precession rotational motion in the form of a bilinear state-space model using standard rectilinear states for translation and quaternions for rotation. Closed-form solutions of state transition equations are obtained to propagate the quaternions in both constant-angular-velocity and constant-precession models. The measurements are noisy perturbations of 3-D feature points represented in an inertial coordinate system. It is assumed that the structure is known and that 3-D feature points are extracted from the stereo images and matched over the frames. Owing to the nonlinearity in the state model, nonlinear filters are designed for the estimation of motion parameters.>
Gem-Sun J. Young, Rama Chellapa
CVPR2