Research Interests

A primary goal of computer vision is to develop algorithms that can learn representations of objects from training sets and subsequently label digital images with instances of these objects. The main focus of my research is the formulation of statistical models for objects. Although not extensively used in computer vision these emerge as a powerful tool in developing recognition algorithms which allow for proper modeling of object and data variability. The simplicity and transparency of the statistical models enables training with small samples, and give rise to efficient computational methods. Models for individual objects can be composed to create models for entire scenes. The models have been implemented in concrete applications such as reading license plates on photos of cars, reading handwritten zipcodes, detecting faces, cars or other objects in images. I am interested in the relation between computer algorithms for vision and biological processing in the cortex. Can the computer algorithms be implemented in a biologically plausible neural network and can they contribute to generating hypotheses on how the visual cortex processes input. Finally I am involved in importing ideas developed in computer vision into the domain of speech recognition. Although the speech recognition is a more mature field of research than vision, there are some interesting insights from vision that may contribute to increase robustness and stability of speech recognition algorithms.

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