Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Computer Vision
A Hybrid Object Recognition Architecture
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A Fast Hybrid Color Segmentation Method
Mustererkennung 1993, Mustererkennung im Dienste der Gesundheit, 15. DAGM-Symposium
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Integration of Regions and Contours for Object Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Structure and Process: Learning of Visual Models and Construction Plans for Complex Objects
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
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Four decades of intensive research in computer vision have lead to numerous computational paradigms. This fact is comprehensible since problems like object recognition or scene descriptions are of high complexity, have different aspects and can be attacked by processing various features. In this paper we propose an architecture that combines the advantages of different paradigms in pattern recognition. Voting and Bayesian networks provide a computational framework to integrate approaches to knowledge based and probabilistic reasoning as well as neural computations.