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Teaching Stereo Perception to YOUR Robot
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-5698-5983
2012 (English)Conference paper, Poster (Other academic)
Abstract [en]

This paper describes a method for generation of dense stereo ground-truth using a consumer depth sensor such as the Microsoft Kinect. Such ground-truth allows adaptation of stereo algorithms to a specific setting. The method uses a novel residual weighting based on error propagation from image plane measurements to 3D. We use this ground-truth in wide-angle stereo learning by automatically tuning a novel extension of the best-first-propagation (BFP) dense correspondence algorithm. We extend BFP by adding a coarse-to-fine scheme, and a structure measure that limits propagation along linear structures and flat areas. The tuned correspondence algorithm is evaluated in terms of accuracy, robustness, and ability to generalise. Both the tuning cost function, and the evaluation are designed to balance the accuracy-robustness trade-off inherent in patch-based methods such as BFP.

Place, publisher, year, edition, pages
University of Surrey, UK , 2012. 1-12 p.
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-81312DOI: 10.5244/C.26.29ISBN: 1-901725-46-4OAI: diva2:551483
British Machine Vision Conference (BMVC12), Surrey, UK, 3-7 September
Available from: 2012-09-11 Created: 2012-09-11 Last updated: 2015-12-10Bibliographically approved
In thesis
1. Components of Embodied Visual Object Recognition: Object Perception and Learning on a Robotic Platform
Open this publication in new window or tab >>Components of Embodied Visual Object Recognition: Object Perception and Learning on a Robotic Platform
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Object recognition is a skill we as humans often take for granted. Due to our formidable object learning, recognition and generalisation skills, it is sometimes hard to see the multitude of obstacles that need to be overcome in order to replicate this skill in an artificial system. Object recognition is also one of the classical areas of computer vision, and many ways of approaching the problem have been proposed. Recently, visually capable robots and autonomous vehicles have increased the focus on embodied recognition systems and active visual search. These applications demand that systems can learn and adapt to their surroundings, and arrive at decisions in a reasonable amount of time, while maintaining high object recognition performance. Active visual search also means that mechanisms for attention and gaze control are integral to the object recognition procedure. This thesis describes work done on the components necessary for creating an embodied recognition system, specifically in the areas of decision uncertainty estimation, object segmentation from multiple cues, adaptation of stereo vision to a specific platform and setting, and the implementation of the system itself. Contributions include the evaluation of methods and measures for predicting the potential uncertainty reduction that can be obtained from additional views of an object, allowing for adaptive target observations. Also, in order to separate a specific object from other parts of a scene, it is often necessary to combine multiple cues such as colour and depth in order to obtain satisfactory results. Therefore, a method for combining these using channel coding has been evaluated. Finally, in order to make use of three-dimensional spatial structure in recognition, a novel stereo vision algorithm extension along with a framework for automatic stereo tuning have also been investigated. All of these components have been tested and evaluated on a purpose-built embodied recognition platform known as Eddie the Embodied.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 64 p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1607
computer vision, object recognition, stereo vision, classification
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems)
urn:nbn:se:liu:diva-93812 (URN)978-91-7519-564-3 (print) (ISBN)
2013-08-16, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Embodied Visual Object Recognition
Swedish Research Council
Available from: 2013-07-09 Created: 2013-06-10 Last updated: 2015-12-10Bibliographically approved

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