Shenzhi Zhang

dblp:200/1980 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1989
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
camera calibration
0.011989
Camera calibration using geometric constraints · CVPR 1989
Computer vision › 3D vision
geometric constraints
0.011989
Camera calibration using geometric constraints · CVPR 1989
Computer vision › 3D vision › camera calibration
intrinsic and extrinsic parameter estimation
0.011989
Camera calibration using geometric constraints · CVPR 1989
Mathematical optimization › statistical estimation › point estimation
nonlinear estimation
0.011989
Camera calibration using geometric constraints · CVPR 1989

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

nonlinear estimation · 0.0linear equations · 0.0
YearPublicationVenuePosition
1989 Camera calibration using geometric constraints
abstract
A method to estimate the intrinsic and extrinsic patterns of a camera model is presented. The intrinsic parameters of camera center and focal length are estimated with a calibration device which is adjusted iteratively with four independent motions. Experimentation demonstrates that inexperienced users rapidly converge to satisfactory estimates. Extrinsic parameters define the position and orientation of the camera in a world coordinate frame. The straightforward formulation of this mapping leads to a complex system of nonlinear equations. Solutions based on nonlinear estimation techniques used unconstrained sets of known target points. These methods may converge to an incorrect solution and are characteristically ill-conditioned. By imposing a simple regularity on the arrangement of calibration points, the problem can be decomposed into a series of simple one- or two-parameter linear equations. The approach is described, and empirical tests of the accuracy of the method are given.>
Joe K. Kearney, Shenzhi Zhang
CVPR3