The Experts below are selected from a list of 132 Experts worldwide ranked by ideXlab platform
Lorenzo Bruzzone - One of the best experts on this subject based on the ideXlab platform.
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a Technique for the selection of kernel function parameters in rbf neural networks for classification of remote sensing images
IEEE Transactions on Geoscience and Remote Sensing, 1999Co-Authors: Lorenzo Bruzzone, D F PrietoAbstract:A Supervised Technique for training radial basis function (RBF) neural network classifiers is proposed. Such a Technique, unlike traditional ones, considers the class memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The result is twofold: a significant reduction in the overall classification error made by the classifier and a more stable behavior of the classification error versus variations in both the number of hidden units and the initial parameters of the training process.
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classification of remote sensing images using radial basis function neural networks a Supervised training Technique
Remote Sensing, 1998Co-Authors: Lorenzo Bruzzone, Diego FernandezprietoAbstract:A Supervised Technique for training Radial Basis Function (RBF) neural classifiers is proposed. Such a Technique, unlike traditional ones, considers the class-memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The proposed method has significant advantages over traditional ones in terms of classification accuracy and stability of the network. Experimental results, carried out on a multisensor remote-sensing data set, confirm the validity of the proposed Technique.
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Supervised training Technique for radial basis function neural networks
Electronics Letters, 1998Co-Authors: Lorenzo Bruzzone, Fernandez D PrietoAbstract:A novel Supervised Technique for training classifiers based on radial basis function (RBF) neural networks is presented. Unlike traditional Techniques, this considers the class-membership of training samples to select the centres and widths of the kernel functions associated with the hidden units of an RBF network. Experiments carried out to solve an industrial visual inspection problem confirmed the effectiveness of the proposed Technique.
Diego Fernandezprieto - One of the best experts on this subject based on the ideXlab platform.
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classification of remote sensing images using radial basis function neural networks a Supervised training Technique
Remote Sensing, 1998Co-Authors: Lorenzo Bruzzone, Diego FernandezprietoAbstract:A Supervised Technique for training Radial Basis Function (RBF) neural classifiers is proposed. Such a Technique, unlike traditional ones, considers the class-memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The proposed method has significant advantages over traditional ones in terms of classification accuracy and stability of the network. Experimental results, carried out on a multisensor remote-sensing data set, confirm the validity of the proposed Technique.
Marco Loog - One of the best experts on this subject based on the ideXlab platform.
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filter learning application to suppression of bony structures from chest radiographs
Medical Image Analysis, 2006Co-Authors: Marco Loog, B Van Ginneken, Arnold M R SchilhamAbstract:A novel framework for image filtering based on regression is presented. Regression is a Supervised Technique from pattern recognition theory in which a mapping from a number of input variables (features) to a continuous output variable is learned from a set of examples from which both input and output are known. We apply regression on a pixel level. A new, substantially different, image is estimated from an input image by computing a number of filtered input images (feature images) and mapping these to the desired output for every pixel in the image. The essential difference between conventional image filters and the proposed regression filter is that the latter filter is learned from training data. The total scheme consists of preprocessing, feature computation, feature extraction by a novel dimensionality reduction scheme designed specifically for regression, regression by k-nearest neighbor averaging, and (optionally) iterative application of the algorithm. The framework is applied to estimate the bone and soft-tissue components from standard frontal chest radiographs. As training material, radiographs with known soft-tissue and bone components, obtained by dual energy imaging, are used. The results show that good correlation with the true soft-tissue images can be obtained and that the scheme can be applied to images from a different source with good results. We show that bone structures are effectively enhanced and suppressed and that in most soft-tissue images local contrast of ribs decreases more than contrast between pulmonary nodules and their surrounding, making them relatively more pronounced.
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dimensionality reduction of image features using the canonical contextual correlation projection
Pattern Recognition, 2005Co-Authors: Marco Loog, Bram Van Ginneken, Robert P W DuinAbstract:A linear, discriminative, Supervised Technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classical linear discriminant analysis (LDA), extending this Technique to cases where there is dependency between the output variables, i.e., the class labels, and not only between the input variables. (The latter can readily be dealt with in standard LDA.) The novel method is useful, for example, in Supervised segmentation tasks in which high-dimensional feature vectors describe the local structure of the image. The principal idea is that where standard LDA merely takes into account a single class label for every feature vector, the new Technique incorporates class labels of its neighborhood in the analysis as well. In this way, the spatial class label configuration in the vicinity of every feature vector is accounted for, resulting in a Technique suitable for, e.g. image data. This extended LDA, that takes spatial label context into account, is derived from a formulation of standard LDA in terms of canonical correlation analysis. The novel Technique is called the canonical contextual correlation projection (CCCP). An additional drawback of LDA is that it cannot extract more features than the number of classes minus one. In the two-class case this means that only a reduction to one dimension is possible. Our contextual LDA approach can avoid such extreme deterioration of the classification space and retain more than one dimension. The Technique is exemplified on a pixel-based medical image segmentation problem in which it is shown that it may give significant improvement in segmentation accuracy.
D F Prieto - One of the best experts on this subject based on the ideXlab platform.
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a Technique for the selection of kernel function parameters in rbf neural networks for classification of remote sensing images
IEEE Transactions on Geoscience and Remote Sensing, 1999Co-Authors: Lorenzo Bruzzone, D F PrietoAbstract:A Supervised Technique for training radial basis function (RBF) neural network classifiers is proposed. Such a Technique, unlike traditional ones, considers the class memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The result is twofold: a significant reduction in the overall classification error made by the classifier and a more stable behavior of the classification error versus variations in both the number of hidden units and the initial parameters of the training process.
Robert P W Duin - One of the best experts on this subject based on the ideXlab platform.
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dimensionality reduction of image features using the canonical contextual correlation projection
Pattern Recognition, 2005Co-Authors: Marco Loog, Bram Van Ginneken, Robert P W DuinAbstract:A linear, discriminative, Supervised Technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classical linear discriminant analysis (LDA), extending this Technique to cases where there is dependency between the output variables, i.e., the class labels, and not only between the input variables. (The latter can readily be dealt with in standard LDA.) The novel method is useful, for example, in Supervised segmentation tasks in which high-dimensional feature vectors describe the local structure of the image. The principal idea is that where standard LDA merely takes into account a single class label for every feature vector, the new Technique incorporates class labels of its neighborhood in the analysis as well. In this way, the spatial class label configuration in the vicinity of every feature vector is accounted for, resulting in a Technique suitable for, e.g. image data. This extended LDA, that takes spatial label context into account, is derived from a formulation of standard LDA in terms of canonical correlation analysis. The novel Technique is called the canonical contextual correlation projection (CCCP). An additional drawback of LDA is that it cannot extract more features than the number of classes minus one. In the two-class case this means that only a reduction to one dimension is possible. Our contextual LDA approach can avoid such extreme deterioration of the classification space and retain more than one dimension. The Technique is exemplified on a pixel-based medical image segmentation problem in which it is shown that it may give significant improvement in segmentation accuracy.
