Christoph von der Malsburg

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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 higherorder entities. These entities represent a very rich(More)
A summary of brain theory is given so far as it is contained within the framework of Localization Theory. Di culties of this \conventional theory" are traced back to a speci c de ciency: there is no way to express relations between active cells (as for instance their representing parts of the same object). A new theory is proposed to cure this de ciency. It(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)
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)
The extension of 2D image-based face recognition methods with respect to 3D shape information and the fusion of both modalities is one of the main topics in the recent development of facial recognition. In this paper we discuss different strategies and their expected benefit for the fusion of 2D and 3D face recognition. The face recognition grand challenge(More)
This paper summarizes the Bochum/USC face recognition system, our preparations for the FERET Phase III test, and test results as far as they have been made known to us. Our technology is based on Gabor wavelets and elastic bunch graph matching. We brie y discuss our technology in relation to biological and PCA based systems and indicate current activities(More)
When recognizing patterns or objects, our visual system can easily separate what kind of pattern is seen and where (location and orientation) it is seen. Neural networks as pattern recognizers can deal well with noisy input patterns, but have difficulties when confronted with the large variety o.f possible geometric transformations of an object. We propose(More)