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
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 neural networks and vision based mobile robot navigation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Morphological associative memories
IEEE Transactions on Neural Networks
Lattice algebra approach to single-neuron computation
IEEE Transactions on Neural Networks
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Strong Lattice Independence implies Affine Independence. Affine Independent sets of vectors define a convex polytope and if this polytope is a good approximation to the convex hull of a set data points, we can use them to represent the data points through their convex coordinates. This representation can be used as a feature extraction or dimensionality reduction method. Morphological Associative Memories (MAM) have been proposed for image denoising and pattern recognition. Recent works show that, by construction, Autoassociative Morphological Memories (AMM) are composed of lattice independent vectors. After a transformation these vectors can be shown to be a good approximation to the data convex hull, and therefore as a candidate set of points for convex coordinate representation of the data. In this paper we present some results on the task of visual landmark recognition for a mobile robot self-localization task improving previous results using AMM.