aus Bremen

Contour integration is believed to be a fundamental process in object recognition and image segmentation. However, its neuronal mechanisms are still not well understood. Psychophysical experiments showed that humans are remarkably efficient in integrating contours even if these are jittered or partially occluded. Therefore the brain requires a reliable algorithm for extracting contours from stimuli. Several recent publications demonstrated that the brain often uses op-timal strategies to integrate sensory information. Hence in this thesis I want to tackle the question which contour integration model describes human contour integration best. Mathematically, contour ensembles can be characterized by a conditional link probability density between oriented edge elements, termed an association field. This association field can be used to generate contours or vice versa to extract a contour from a stimulus. While in most neuronal network models all inputs to a neuron are summed up, in such a probabilistically motivated neural network fo