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Anders H. Torp

dblp:97/59 · DBLP profile ↗
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2ranked-venue papers
1as 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 · 1Graphics, 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.

Computer graphics and multimedia
1 paper
Image and video processing · 100%

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

TopicWeightPapersLastEvidence papers
Image and video processing › image segmentation
active contour
0.011996
Encoding of a priori Information in Active Contour Models · IEEE Trans. Pattern Anal. Mach. Intell. 1996
Image and video processing › image segmentation
contour completion
0.011996
Encoding of a priori Information in Active Contour Models · IEEE Trans. Pattern Anal. Mach. Intell. 1996
Image and video processing
image segmentation
0.011996
Encoding of a priori Information in Active Contour Models · IEEE Trans. Pattern Anal. Mach. Intell. 1996

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

knuth-morris-pratt string matching · 0.0grammatical encoding · 0.0dynamic programming · 0.0
YearPublicationVenuePosition
1996 Encoding of a priori Information in Active Contour Models
abstract
The theory of active contours models the problem of contour recovery as an energy minimization process. The computational solutions based on dynamic programming require that the energy associated with a contour candidate can be decomposed into an integral of local energy contributions. In this paper we propose a grammatical framework that can model different local energy models and a set of allowable transitions between these models. The grammatical encodings are utilized to represent a priori knowledge about the shape of the object and the associated signatures in the underlying images. The variability encountered in numerical experiments is addressed with the energy minimization procedure which is embedded in the grammatical framework. We propose an algorithmic solution that combines a nondeterministic version of the Knuth-Morris-Pratt algorithm for string matching with a time-delayed discrete dynamic programming algorithm for energy minimization. The numerical experiments address practical problems encountered in contour recovery such as noise robustness and occlusion.
Bjørn Olstad, Anders H. Torp
IEEE Trans. Pattern Anal. Mach. Intell.2
1994 Multispectral Analysis of Object Surfaces Extracted from Volumetric Data Sets
abstract
This paper explores the potential of multispectral analysis of density slices extracted by automatic object recognition algorithms in volumetric data sets. The object recognition algorithm is utilized to define the object surface. Each point x on the object surface is hence associated with the set of measurements in the underlying volumetric data set that is within the region of interest and projected onto x. This variable size attribute vector is converted to a fixed size attribute vector by transforming the measurements into the associated cumulative distribution vector. The fixed size attribute vectors represent a multispectral image over the manifold defined by the object surface. This image is further processed with multispectral techniques such as the Karhunen-Loeve transform and visualized as a coloring of the 3-dimensional object surface. Our numerical experiments include 3-dimensional ultrasonic catheter studies of plaque formation in arteries, MRI studies of the brain, and evaluation of customized femoral hip prostheses based on CT imaging.>
Anders H. Torp, Bjørn Olstad
ICIP (2)1