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Carl Smets

dblp:135/1934 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 1989
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, 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.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Artificial intelligence
1 paper
3D vision · 100%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › stereo vision
stereo matching
0.011989
Recognition of the coronary blood vessels on angiograms using hierarchical model-based iconic search · CVPR 1989
Medical and health informatics › medical imaging › vascular imaging
coronary angiography
0.011989
Recognition of the coronary blood vessels on angiograms using hierarchical model-based iconic search · CVPR 1989
Medical and health informatics › medical imaging
medical image analysis
0.011989
Recognition of the coronary blood vessels on angiograms using hierarchical model-based iconic search · CVPR 1989
Multimedia analysis and retrieval › object recognition
model-based recognition
0.011989
Recognition of the coronary blood vessels on angiograms using hierarchical model-based iconic search · CVPR 1989

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

stereoscopic matching · 0.0iconic search · 0.0hierarchical model · 0.0
YearPublicationVenuePosition
1989 Recognition of the coronary blood vessels on angiograms using hierarchical model-based iconic search
abstract
The recognition of the coronary blood vessels on single angiograms is addressed. Recognizing blood vessels on digital subtraction angiograms is a prerequisite to automatic diagnosis and quantification of blood-vessel pathologies. It is shown how stereoscopic angiograms can improve the interpretation process. The computational strategy recommended to solve the problem has the following important characteristics. First, the model is hierarchically instantiated, starting from a partial model and working up to the final complete model. Second, the contact with the image is never broken during the interpretation process. Missing model attributes are instantiated by returning to the image. Third, delineation (segmentation and image partitioning), interpretation, and stereoscopic matching are not sequential but integrated processes. Fourth, the knowledge representation is partially iconic. The iconic memory is used to guide the search for missing or incomplete attributes in the image.>
Paul Suetens, Carl Smets, Frans Van de Werf, André Oosterlinck
CVPR2
1988 A knowledge-based system for the delineation of blood vessels on subtraction angiograms
Carl Smets, Geert Verbeeck, Paul Suetens, André Oosterlinck
Pattern Recognit. Lett.1