On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Fuzzy Min-Max Neural Network Classifier with Compensatory Neuron Architecture
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A general fuzzy min max neural network with compensatory neuron architecture
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Translation, rotation, and scale-invariant object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
A granular reflex fuzzy min-max neural network for classification
IEEE Transactions on Neural Networks
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This paper proposes an object recognition system that is invariant to rotation, translation and scale and can be trained under partial supervision. The system is divided into two sections namely, feature extraction and recognition sections. Feature extraction section uses proposed rotation, translation and scale invariant features. Recognition section consists of a novel Reflex Fuzzy Min-Max Neural Network (RFMN) architecture with “Floating Neurons”. RFMN is capable to learn mixture of labeled and unlabeled data which enables training under partial supervision. Learning under partial supervision is of high importance for the practical implementation of pattern recognition systems, as it may not be always feasible to get a fully labeled dataset for training or cost to label all samples is not affordable. The proposed system is tested on shape data-base available online, Marathi and Bengali digits. Results are compared with “General Fuzzy Min-Max Neural Network” proposed by Gabrys and Bargiela.