Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream
Neural Processing Letters
Development of micro mirror solar concentrator
EE'07 Proceedings of the 2nd IASME/WSEAS international conference on Energy and environment
Support frame for micro facet solar concentrator
EE'07 Proceedings of the 2nd IASME/WSEAS international conference on Energy and environment
FPGA realization of the LIRA neural classifier
Optical Memory and Neural Networks
Similarity-Based Retrieval With Structure-Sensitive Sparse Binary Distributed Representations
Computational Intelligence
Feature representation selection based on Classifier Projection Space and Oracle analysis
Expert Systems with Applications: An International Journal
Hebbian ensemble neural network for robot movement control
Optical Memory and Neural Networks
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A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%