Hypothesis-based image segmentation - Denecke, Alexander
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This thesis addresses the figure-ground segmentation problem in the context of complex systems for automatic object recognition. Firstly the problem of image segmentation in general terms is introduced, followed by a discussion about its importance for online and interactive acquisition of visual representations. Secondly a machine learning approach using artificial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time figure-ground segmentation of complex shaped objects under…mehr

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Produktbeschreibung
This thesis addresses the figure-ground segmentation problem in the context of complex systems for automatic object recognition. Firstly the problem of image segmentation in general terms is introduced, followed by a discussion about its importance for online and interactive acquisition of visual representations. Secondly a machine learning approach using artificial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time figure-ground segmentation of complex shaped objects under continuously changing environment conditions. The ability to fulfill these requirements characterize the novelty of the approach compared to state-of-the-art methods. Finally the proposed technique is extended in several aspects, which yields a framework for object segmentation that is applicable to improve current systems for visual object learning and recognition.
Autorenporträt
He studied computer science at Bielefeld University and received his diploma in 2005. As member of the Research Institute for Cognition and Robotics (CoR-Lab) and guest scientist at Honda Research Institute Europe GmbH he finished his PhD in 2011. Since 2011 he develops advanced driver assistance systems at Elektronische Fahrwerksysteme GmbH.