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Nils Thune

dblp:45/2871 · DBLP profile ↗
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4ranked-venue papers
1as first author
0since 2021 · last 1997
—ORCID · none

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

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 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
Motion planning and robot control · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control architecture
hierarchical robot control
0.011987
CAOS: A hierarchical robot control system · ICRA 1987
Robotics › Motion planning and robot control
robot control
0.011987
CAOS: A hierarchical robot control system · ICRA 1987
Robotics › Motion planning and robot control › robot control
parallel robot control
0.011987
CAOS: A hierarchical robot control system · ICRA 1987

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

neuroschemas · 0.0action oriented schemas · 0.0
YearPublicationVenuePosition
1997 Edge detection in noisy data using finite mixture distribution analysis
Mari Thune, Bjørn Olstad, Nils Thune
Pattern Recognit.3
1994 Edge Detection in Noisy Data using Finite Mixture Distribution Analysis
abstract
An algorithm which identifies discontinuities in noisy data is presented. The signal is modelled as step edges with additive normally distributed noise present. Using finite mixture analysis a variable number of distributions are identified together with the location of the respective edges separating them. The problem is solved using a dynamic programming approach which ensures globally optimal edge positions according to the signal model of a finite mixture of normal distributions. The computational complexity is of order MN/sup 2/ where M is the number of discontinuities in the mixture and N is the number of data points in the signal. The algorithm is tested on a range of signals and yields as accurate edge positions as a corresponding square error method. Among applications for this algorithm is edge detection in medical images and examples from ultrasound imaging are included.>
Mari Thune, Bjørn Olstad, Nils Thune
ICIP (1)3
1991 Visualizing 4-D Medical Ultrasound Data
abstract
Different standard rendering methods applied to 4-D medical ultrasound data are discussed. In particular, maximum value projection, sum of values projection, transparent gray level gradient shading, and surface shading have been tested. Due to the fact that ultrasound data suffer from a low signal to noise ratio, image processing and image analysis are used to enhance and classify the volumetric data set.>
Nils Thune, Bjørn Olstad
IEEE Visualization1
1987 CAOS: A hierarchical robot control system
abstract
Control systems which enable robots to behave intelligently is a major issue in todays process of automating factories. This paper presents a hierarchical robot control system, termed CAOS for Control using Action Oriented Schemas, with ideas taken from the neurosciences. We are using action oriented schemas (called neuroschemas) as the basic building blocks in a hierarchical control structure which is being implemented on a BBN Butterfly Parallel Processor. Serial versions in C and LISP are presented with examples showing how CAOS achieves the goals of recognizing three-dimensional polyhedral objects. We also describe a simulation on how it manipulates objects. Moreover, the ongoing implementation of a parallel version of the system is discussed.
Bir Bhanu, Nils Thune, Mari Thune
ICRA2