ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Neocognitron-Type Network for Recognizing Rotated and Shifted Patterns with Reduction of Resources
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Learning Hough Transform: A Neural Network Model
Neural Computation
Theoretical quantification of shape distortion in fuzzy Hough transform
Fuzzy Sets and Systems
| Hi-index | 0.00 |
A connectionist system has been designed for learning and simultaneous recognition of flat industrial objects (based an the concepts of conventional and structured connectionist computing) by integrating the psychological hypotheses with the generalized Hough transform technique. The psychological facts include the evidence of separation of two regions for identification (“what it is”) and pose estimation (“where it is”). The system uses the mechanism of selective attention for initial hypotheses generation. A special two-stage training paradigm has been developed for learning the structural relationships between the features and objects and the importance values of the features with respect to the objects. The performance of the system has been demonstrated on real-life data both for single and mixed (overlapped) instances of object categories. The robustness of the system with respect to noise and false alarming has been theoretically investigated