Christoph von der Malsburg

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We present a system for recognizing human faces from single images out of a large database containing one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial(More)
—We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture exploits correlations in the fine-scale temporal structure of cellular signals in order to group neurons dynamically into higher-order entities. These entities represent a very rich(More)
Sensory integration or sensor fusion -- the integration of information from different modalities, cues, or sensors -- is among the most fundamental problems of perception in biological and artificial systems. We propose a new architecture for adaptively integrating different cues in a self-organized manner. In Democratic Integration different cues agree on(More)
A large attraction of neural systems lies in their promise of replacing programming by learning. A problem with many current neural models is that with realistically large input patterns learning time explodes. This is a problem inherent in a notion of learning that is based almost entirely on statistical estimation. We propose here a different learning(More)
The development of the concept of feature binding as fundamental to neural dynamics has made possible recent advances in the modeling of difficult problems of perception and brain function. Major weaknesses of past neural modeling (most prominently its inability to work with natural stimuli and its 'learning-time' barrier) have been traced back to improper(More)