First edition 2005, 190 pages, € 64,00. ISBN 3-86628-001-7

This book presents a new method for visual object categorization, i.e. for recognizing previously unseen objects, localizing them in cluttered images, and assigning the correct category label. It considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in this thesis, the tight coupling between those two processes allows them to profit from each other and improve the combined performance.
The core part of this work is a highly flexible implicit formulation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then used to again improve recognition by allowing the system to focus its effort on object pixels and discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is used in an MDL based verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion.
An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.

Bastian Leibe obtained a M.Sc. degree in Computer Science (Dipl.-Inform.) from the University of Stuttgart, Germany, in 2001, and a M.Sc. degree in Computer Science from Georgia Institute of Technology, Atlanta, GA, in 1999. From 2001 to 2004, he was research assistant and Ph.D. student at the Perceptual Computing and Computer Vision Group of the Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, with Prof. Bernt Schiele. He received his Ph.D. in Computer Science (Dr.sc.techn.) from ETH Zurich for his work on visual object categorization in combination with top-down segmentation.

Keywords: object recognition, object categorization, segmentation, computer vision, machine learning, Objekterkennung, Objektkategorisierung, Segmentierung, Bildverstehen, maschinelles Lernen