This paper presents a new statistical approach for learning automatic color image correction. The goal is to parameterize color independently of illumination and to correct color for changes of illumination. This is useful in many image processing applications, such as color image segmentation or background subtraction. The motivation for using a learning approach is to deal with changes of lig...

I. Abstract It is well-established that a multi-layer perceptron (MLP) with a single hidden layer of N neurons and an activation function bounded by zero at negative infinity and one at infinity can learn N distinct training sets with zero error. Previous work has shown that the input weights and biases for such a MLP can be chosen in an effectively arbitrary manner; however, this work makes th...

In this study, we propose a Multi-Layer Perceptron (MLP) with pulse glial network having dynamic period of inactivity. We connect glias to neurons in a hidden-layer. The glia is excited by the connecting neuron output. Then, the glia generates a pulse. The pulse is propagated to the connecting neuron and the neighboring glia. In the previous method, we fix a period of inactivity. The period of ...

We have proposed the glial network which was inspired from the feature of brain. In the glial network, glias generate independent oscillations and these oscillations propagated neurons and other glias. We confirmed that the glial network improved the learning performance of the Multi-Layer Perceptron (MLP) In this article, we investigate the MLP with the impulse glial network. The glias have on...

Different normalization methods are applied in recent Large Vocabulary Continuous Speech Recognition Systems (LVCSR) to reduce the influence of speaker variability on the acoustic models. In this paper we investigate the use of Vocal Tract Length Normalization (VTLN) and Speaker Adaptive Training (SAT) in Multi Layer Perceptron (MLP) feature extraction on an English task. We achieve significant...

Gaussian Mixture Model (GMM) and Multi Layer Perceptron (MLP) based acoustic models are compared on a French large vocabulary continuous speech recognition (LVCSR) task. In addition to optimizing the output layer size of the MLP, the effect of the deep neural network structure is also investigated. Moreover, using different linear transformations (time derivatives, LDA, CMLLR) on conventional M...

In the tandem feature extraction scheme a Multi-Layer Perceptron (MLP) with softmax output layer is discriminatively trained to estimate context independent phoneme posterior probabilities on a labeled database. The outputs of the MLP after nonlinear transformation and Principal ComponentAnalysis (PCA) are used as features in a Gaussian Mixture Model (GMM) based recognizer. The baseline tandem ...

In this paper, we propose to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We chose to combine two transfer functions for the nodes of the hidden layer, and to use a temporal indicator (time index as input) in order to take into account the seasonal aspect of the studied time series. The results of the prediction concern two years of me...

A pruning schema is applied to Multi-Layer Perceptron (MLP) gender classi£er. MLP uses eigenvector coef£cients of the face space created by Principal Component Analysis (PCA). We show that pruning improves the initial MLP performance by preserving the most effective input while eliminating most of the units and connections. Pruning is also used as a tool to monitor which eigenvectors contribute...

In tandem systems, the outputs of multi-layer perceptron (MLP) classifiers have been successfully used as features for HMM-based automatic speech recognition. In this paper, we propose a data-driven clustered hierarchical tandem system that yields improved performance on a large-vocabulary broadcast news transcription task. The complicated global learning for a large monolithic MLP classifier i...