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Point-of-Interest Detection for Range Data
Linköping University, Department of Electrical Engineering, Image Coding. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
2008 (English)In: International Conference on Pattern Recognition (ICPR), IEEE , 2008, 1-4 p.Conference paper, Published paper (Refereed)
Abstract [en]

Point-of-interest detection is a way of reducing the amount of data that needs to be processed in a certain application and is widely used in 2D image analysis. In 2D image analysis, point-of-interest detection is usually related to extraction of local descriptors for object recognition, classification, registration or pose estimation. In analysis of range data however, some local descriptors have been published in the last decade or so, but most of them do not mention any kind of point-of-interest detection. We here show how to use an extended Harris detector on range data and discuss variants of the Harris measure. All described variants of the Harris detector for 3D should also be usable in medical image analysis, but we focus on the range data case. We do present a performance evaluation of the described variants of the Harris detector on range data.

Place, publisher, year, edition, pages
IEEE , 2008. 1-4 p.
Series
Pattern Recognition, ISSN 1051-4651
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-44928DOI: 10.1109/ICPR.2008.4761179Local ID: 78311ISBN: 978-1-4244-2175-6 (print)ISBN: 978-1-4244-2174-9 (print)OAI: oai:DiVA.org:liu-44928DiVA: diva2:265790
Conference
International Conference on Pattern Recognition (ICPR), December 8-11, Tampa, Florida, USA
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2014-06-03
In thesis
1. Local Features for Range and Vision-Based Robotic Automation
Open this publication in new window or tab >>Local Features for Range and Vision-Based Robotic Automation
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Robotic automation has been a part of state-of-the-art manufacturing for many decades. Robotic manipulators are used for such tasks as welding, painting, pick and place tasks etc. Robotic manipulators are quite flexible and adaptable to new tasks, but a typical robot-based production cell requires extensive specification of the robot motion and construction of tools and fixtures for material handling. This incurs a large effort both in time and monetary expenses. The task of a vision system in this setting is to simplify the control and guidance of the robot and to reduce the need for supporting material handling machinery.

This dissertation examines performance and properties of the current state-of-the-art local features within the setting of object pose estimation. This is done through an extensive set of experiments replicating various potential problems to which a vision system in a robotic cell could be subjected. The dissertation presents new local features which are shown to increase the performance of object pose estimation. A new local descriptor details how to use log-polar sampled image patches for truly rotational invariant matching. This representation is also extended to use a scale-space interest point detector which in turn makes it very competitive in our experiments. A number of variations of already available descriptors are constructed resulting in new and competitive features, among them a scale-space based Patch-duplet.

In this dissertation a successful vision-based object pose estimation system is extended for multi-cue integration, yielding increased robustness and accuracy. Robustness is increased through algorithmic multi-cue integration, combining the individual strengths of multiple local features. Increased accuracy is achieved by utilizing manipulator movement and applying temporal multi-cue integration. This is implemented using a real flexible robotic manipulator arm.

Besides work done on local features for ordinary image data a number of local features for range data has also been developed. This dissertation describes the theory behind and the application of the scene tensor to the problem of object pose estimation. The scene tensor is a fourth order tensor representation using projective geometry. It is shown how to use the scene tensor as a detector as well as how to apply it to the task of object pose estimation. The object pose estimation system is extended to work with 3D data.

A novel way of handling sampling of range data when constructing a detector is discussed. A volume rasterization method is presented and the classic Harris detector is adapted to it. Finally, a novel region detector, called Maximally Robust Range Regions, is presented. All developed detectors are compared in a detector repeatability test.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 81 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1325
Keyword
Local features, object pose estimation, range data
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-57333 (URN)978-91-7393-362-9 (ISBN)
Public defence
2010-09-09, Glashuset, House B, entrance 25,, Campus Valla, Linköpings universitet, Linköping, 13:00 (English)
Opponent
Supervisors
Available from: 2010-06-17 Created: 2010-06-17 Last updated: 2010-06-23Bibliographically approved

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Viksten, FredrikNordberg, Klas

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