Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

P. Kempenaers

dblp:33/5609 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 1995
—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
Image recognition and object detection · 100%
Theoretical computer science
2 papers
Computational geometry · 100%
Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection
shape recognition
0.011995
Recognition of planar shapes under affine distortion · Int. J. Comput. Vis. 1995
Geometric modeling and processing › shape analysis
shape recognition
0.011991
Recognition and semi-differential invariants · CVPR 1991
Computational geometry › geometric transformation
affine transformation
0.011995
Recognition of planar shapes under affine distortion · Int. J. Comput. Vis. 1995

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

lie group theory · 0.0
YearPublicationVenuePosition
1995 Recognition of planar shapes under affine distortion
Eric J. Pauwels, Theo Moons, Luc Van Gool, P. Kempenaers, André Oosterlinck
Int. J. Comput. Vis.4
1991 Recognition and semi-differential invariants
abstract
Semidifferential invariants, combining coordinates in different points together with their derivatives, are used for the description of planar contours. Their use can be seen as a tradeoff between two extreme strategies currently used in shape recognition: (invariant) feature extraction methods, involving high-order derivatives, and invariant coordinate descriptions, leading to the correspondence problem of reference points. The method for the derivation of such invariants, based on Lie group theory and applicable to a wide spectrum of transformation groups, is described. As an example, invariant curve parameterizations are developed for affine and projective transformations. The usefulness of the approach is illustrated with two examples: (1) recognition of a test set of 12 planar objects viewed under conditions allowing affine approximations, and (2) the detection of symmetry in perspective projections of curves.>
Luc Van Gool, P. Kempenaers, André Oosterlinck
CVPR2