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Abstract

In this thesis a computational framework for visual object recognition is developed, which is based on results from perceptual research. The motivation for this approach is given by the fact that despite several decades of research in the field of computer vision, there still exists no recognition system which is able to match the visual
performance of humans (or other primates). The apparent ease with which visual tasks such as recognition and categorization are solved by humans is testimony of a highly optimized visual system which not only exhibits excellent robustness and generalization capabilities
but is in addition highly flexible in learning and organizing new data. In developing the framework, the underlying philosophy was to model object recognition on an abstract cognitive level rather than supplying a complete neurophysiologically plausible implementation. The proposed framework is able to model results from psychophysics
and, in addition, delivers excellent recognition performance in computational recognition experiments. Furthermore, the framework also interfaces well with advanced classification schemes from machine learning thus
further broadening the scope of application.