Intelligent hybrid system for pattern recognition and classification
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Comparison between analog and digital neural network implementations for range-finding applications
IEEE Transactions on Neural Networks
Intelligent visual recognition and classification of cork tiles with neural networks
IEEE Transactions on Neural Networks
A receptive field based approach for face detection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Fabric defect classification using radial basis function network
Pattern Recognition Letters
Multilevel image segmentation with adaptive image context based thresholding
Applied Soft Computing
Resistive-type CVNS distributed neural networks with improved noise-to-signal ratio
IEEE Transactions on Circuits and Systems II: Express Briefs
Journal of Computational Neuroscience
Multilayer neural networks with receptive fields as a model for the neuron reconstruction problem
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
An improved NN training scheme using two-stage LDA features for face recognition
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Analog implementation of a novel resistive-type sigmoidal neuron
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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In this paper, we propose a new neural architecture for classification of visual patterns that is motivated by the two concepts of image pyramids and local receptive fields. The new architecture, called pyramidal neural network (PyraNet), has a hierarchical structure with two types of processing layers: Pyramidal layers and one-dimensional (1-D) layers. In the new network, nonlinear two-dimensional (2-D) neurons are trained to perform both image feature extraction and dimensionality reduction. We present and analyze five training methods for PyraNet [gradient descent (GD), gradient descent with momentum, resilient backpropagation (RPROP), Polak-Ribiere conjugate gradient (CG), and Levenberg-Marquadrt (LM)] and two choices of error functions [mean-square-error (mse) and cross-entropy (CE)]. In this paper, we apply PyraNet to determine gender from a facial image, and compare its performance on the standard facial recognition technology (FERET) database with three classifiers: The convolutional neural network (NN), the k-nearest neighbor (k-NN), and the support vector machine (SVM)