Components of Embodied Visual Object Recognition: Object Perception and Learning on a Robotic Platform
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
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
Signal Processing Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-93812ISBN: 978-91-7519-564-3 (print)OAI: oai:DiVA.org:liu-93812DiVA: diva2:634701
2013-08-16, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Björkman, Mårten, Dr.
Forssén, Per-Erik, Assistant Professor
ProjectsEmbodied Visual Object Recognition
FunderSwedish Research Council
List of papers