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Abstract:
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. 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. Using an integrative approach to the problem of object recognition we have developed a framework that combines cognitive psychophysics, computer vision as well as machine learning. This 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.
