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Marc Herniou

dblp:44/4410 · DBLP profile ↗
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2ranked-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 · 2Graphics, 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 · 50% Machine translation · 50%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
camera calibration
0.011992
Camera Calibration with Distortion Models and Accuracy Evaluation · IEEE Trans. Pattern Anal. Mach. Intell. 1992
Natural language and speech › Machine translation › statistical machine translation
distortion modeling
0.011992
Camera Calibration with Distortion Models and Accuracy Evaluation · IEEE Trans. Pattern Anal. Mach. Intell. 1992
Mathematical optimization › continuous optimization
nonlinear optimization
0.011992
Camera Calibration with Distortion Models and Accuracy Evaluation · IEEE Trans. Pattern Anal. Mach. Intell. 1992

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

nonlinear refinement · 0.0minimum variance estimation · 0.0closed-form solution · 0.0
YearPublicationVenuePosition
1992 Camera Calibration with Distortion Models and Accuracy Evaluation
abstract
A camera model that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortions is presented. The proposed calibration procedure consists of two steps: (1) the calibration parameters are estimated using a closed-form solution based on a distribution-free camera model; and (2) the parameters estimated in the first step are improved iteratively through a nonlinear optimization, taking into account camera distortions. According to minimum variance estimation, the objective function to be minimized is the mean-square discrepancy between the observed image points and their inferred image projections computed with the estimated calibration parameters. The authors introduce a type of measure that can be used to directly evaluate the performance of calibration and compare calibrations among different systems. The validity and performance of the calibration procedure are tested with both synthetic data and real images taken by tele- and wide-angle lenses.>
Juyang Weng, Paul R. Cohen, Marc Herniou
IEEE Trans. Pattern Anal. Mach. Intell.3
1990 Calibration of stereo cameras using a non-linear distortion model (CCD sensory)
abstract
A camera model is presented which accounts for major sources of camera distortion: radial, decentering, and thin-prism distortions. The proposed calibration procedure consists of two steps. In the first step, calibration parameters are estimated using a closed-form solution based on a distortion-free camera model. In the second step, the parameters estimated in the first step are improved iteratively through nonlinear optimization, taking into account camera distortions. According to minimum-variance estimation, the objective function to be minimized is the mean-square discrepancy between the observed image points and their inferred image projections computed with the estimated calibration parameters. A type of measure is introduced which can be used to directly evaluate the performance of the calibration and compare calibrations among different systems. The validity and the performance of the calibration procedure are tested on real images taken by wide-angle lenses. Results consistently show significant improvements over less complete camera models.>
Juyang Weng, Paul R. Cohen, Marc Herniou
ICPR (1)3