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Erik Roeland van Dop

dblp:23/4764 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2001
—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 · 50% Image recognition and object detection · 50%
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 › range sensing
range image analysis		 0.011998
Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification · CVPR 1998
Computer vision › Image recognition and object detection
shape recognition		 0.011998
Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification · CVPR 1998
Geometric modeling and processing › surface fitting
superquadric fitting		 0.011998
Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification · CVPR 1998
Geometric modeling and processing › shape representation › volumetric representation
volumetric shape representation		 0.011998
Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification · CVPR 1998

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

model fitting · 0.0
YearPublicationVenuePosition
2001 Multi-sensor recognition of electronic components
Erik Roeland van Dop, Paul P. L. Regtien
 Mach. Vis. Appl.1
1998 Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification
abstract
Undeformed superquadrics are volumetric modeling primitives with an extensive shape vocabulary that are described by only 5 parameters. Fitting these models viewpoint invariantly to range data enables classification based on the superquadric parameters. However, current recovery routines show several limitations, especially when the algorithms are applied to range images instead of true 3D images. In this paper problems with the common superquadric recovery procedure are identified and solutions are presented.
Erik Roeland van Dop, Paul P. L. Regtien
 CVPR1