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Tom Chang

dblp:37/7213 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1992
—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%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
camera calibration
0.011992
Stereo from uncalibrated cameras · CVPR 1992
Computer vision › 3D vision
structure from motion
0.011992
Stereo from uncalibrated cameras · CVPR 1992
Computer vision › 3D vision
multi-view geometry
0.011992
Stereo from uncalibrated cameras · CVPR 1992
Computer vision › 3D vision › multi-view geometry › geometric invariants
projective invariants
0.011992
Stereo from uncalibrated cameras · CVPR 1992

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

linear non-iterative method · 0.0ground control points · 0.0
YearPublicationVenuePosition
1992 Stereo from uncalibrated cameras
abstract
The problem of computing placement of points in 3-D space, given two uncalibrated perspective views, is considered. The main theorem shows that the placement of the points is determined only up to an arbitrary projective transformation of 3-space. Given additional ground control points, however, the location of the points and the camera parameters may be determined. The method is linear and noniterative, whereas previously known methods for solving the camera calibration and placement problem to take proper account of both ground-control points and image correspondences are unsatisfactory in requiring either iterative methods or model restrictions. As a result of the main theorem, it is possible to determine projective invariants of 3-D geometric configurations from two perspective views.>
Richard I. Hartley, Rajiv Gupta 0002, Tom Chang
CVPR3