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André Lejeune

dblp:86/638 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1996
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

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%
Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d scene understanding
0.011993
Partitioning range images using curvature and scale · CVPR 1993
Computer vision › 3D vision › range sensing
range image segmentation
0.011993
Partitioning range images using curvature and scale · CVPR 1993
Geometric modeling and processing › shape decomposition
surface decomposition
0.011993
Partitioning range images using curvature and scale · CVPR 1993
Computer vision › 3D vision › 3d reconstruction
surface reconstruction
0.011993
Partitioning range images using curvature and scale · CVPR 1993

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

scale-space representation · 0.0curvature analysis · 0.0
YearPublicationVenuePosition
1996 Finding the Parts of Objects in Range Images
André Lejeune, Frank P. Ferrie
Comput. Vis. Image Underst.1
1993 Partitioning range images using curvature and scale
abstract
A method is presented for partitioning a set of surface estimates obtained with a laser range finding system into subsets corresponding to parts of an object. The authors' strategy uses two complementary representations for surfaces, i.e., one which describes local structures in terms of differential properties (e.g., edges, lines, contours) and another which represents the surface as a collection of smooth patches at different scales. By enforcing a consistent interpretation between these two representations, it is possible to derive a partitioning algorithm that is both efficient and robust.>
André Lejeune, Frank P. Ferrie
CVPR1