Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Morphological bidirectional associative memories
Neural Networks
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perception-Based Self-Localization Using Fuzzy Locations
RUR '95 Proceedings of the International Workshop on Reasoning with Uncertainty in Robotics
Steps towards one-shot vision-based self-localization
Biologically inspired robot behavior engineering
Generalizing Operations of Binary Autoassociative Morphological Memories Using Fuzzy Set Theory
Journal of Mathematical Imaging and Vision
Reconstruction of Patterns from Noisy Inputs Using Morphological Associative Memories
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Morphological associative memories
IEEE Transactions on Neural Networks
Lattice algebra approach to single-neuron computation
IEEE Transactions on Neural Networks
Morphological neural networks and vision based simultaneous localization and mapping
Integrated Computer-Aided Engineering - Artificial Neural Networks
Lattice Independence and Vision Based Mobile Robot Navigation
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Morphological independence for landmark detection in vision based SLAM
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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Morphological Associative Memories (MAM) have been proposed for image denoising and pattern recognition. We have shown that they can be applied to other domains, like image retrieval and hyperspectral image unsupervised segmentation. In both cases the key idea is that Morphological Autoassociative Memories (MAAM) selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. The convex coordinates obtained by linear unmixing based on the sets of morphological independent patterns define a feature extraction process. These features may be useful either for pattern classification. We present some results on the task of visual landmark recognition for a mobile robot self-localization task.