Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Competitive learning algorithms for vector quantization
Neural Networks
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Self-organizing maps
Mixtures of probabilistic principal component analyzers
Neural Computation
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Efficient Vector Quantization Using the WTA-Rule with Activity Equalization
Neural Processing Letters
Visual Checking of Grasping Positions of a Three-Fingered Robot Hand
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
A System for Various Visual Classification Tasks Based on Neural Networks
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Codeword distribution for frequency sensitive competitive learning with one-dimensional input data
IEEE Transactions on Neural Networks
Diffusion approximation of frequency sensitive competitive learning
IEEE Transactions on Neural Networks
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A computer vision system which can be trained to classification tasks from sample views is presented. It consists of several artificial neural networks which realize local PCA with subsequent expert nets as classifiers. The major benefit of the approach is that entirely different tasks can be solved with one and the same system without modifications or extensive parameter tuning. Therefore, the architecture is an example for the potential which lies in view based recognition: Making complicated tasks solvable with less and less expert knowledge.