A Stochastic Modeling Approach to Region-and Edge-Based Image Segmentation

The purpose of image segmentation is to isolate objects in a scene from the background. This is a very important step in any computer vision system since various tasks, such as shape analysis and object recognition, require accurate image segmentation. Image segmentation can also produce tremendous data reduction. Edge-based and region-based segmentation have been examined and two new algorithms based on recent results in random field theory have been developed. The edge-based segmentation algorithm uses the pixel gray level intensity information to allocate object boundaries in two stages: edge enhancement, followed by edge linking. Edge enhancement is accomplished by maximum energy filters used in one-dimensional bandlimited signal analysis. The issue of optimum filter spatial support is analyzed for ideal edge models. Edge linking is performed by quantitative sequential search using the Stack algorithm. Two probabilistic search metrics are introduced and their optimality is proven and demonstrated on test as well as real scenes. Compared to other methods, this algorithm is shown to produce more accurate allocation of object boundaries. Region-based segmentation was modeled as a MAP estimation problem in which the actual (unknown) objects were estimated from the observed (known) image by a recursive classification algorithms. The observed image was modeled by an Autoregressive (AR) model whose parameters were estimated locally, and a Gibbs-Markov random field (GMRF) model was used to model the unknown scene. A computational study was conducted on images having various types of texture images. The issues of parameter estimation, neighborhood selection, and model orders were examined. It is concluded that the MAP approach for region segmentation generally works well on images having a large content of microtextures which can be properly modeled by both AR and GMRF models. On these texture images, second order AR and GMRF models were shown to be adequate

Spatial regression in large datasets: problem set solution

In this dissertation we investigate a possible attempt to combine the Data Mining methods and traditional Spatial Autoregressive models, in the context of large spatial datasets. We start to considere the numerical difficulties to handle massive datasets by the usual approach based on Maximum Likelihood estimation for spatial models and Spatial Two-Stage Least Squares. So, we conduct an experiment by Monte Carlo simulations to compare the accuracy and computational complexity for decomposition and approximation techniques to solve the problem of computing the Jacobian in spatial models, for various regular lattice structures. In particular, we consider one of the most common spatial econometric models: spatial lag (or SAR, spatial autoregressive model). Also, we provide new evidences in the literature, by examining the double effect on computational complexity of these methods: the influence of "size effect" and "sparsity effect". To overcome this computational problem, we propose a data mining methodology as CART (Classification and Regression Tree) that explicitly considers the phenomenon of spatial autocorrelation on pseudo-residuals, in order to remove this effect and to improve the accuracy, with significant saving in computational complexity in wide range of spatial datasets: realand simulated data

Étude de la propagation acoustique en milieu complexe par des réseaux de neurones profonds

Abstract : Predicting the propagation of aerocoustic noise is a challenging task in the presence of complex mean flows and geometry installation effects. The design of future quiet propul- sion systems requires tools that are able to perform many accurate evaluations with a low computational cost. Analytical models or hybrid numerical approaches have tradition- ally been employed for that purpose. However, such methods are typically constrained by simplifying hypotheses that are not easily relaxed. Thus, the main objective of this thesis is to develop and validate novel methods for the fast and accurate prediction of aeroacoustic propagation in complex mean flows and geometries. For that, data-driven deep convolutional neural networks acting as auto-regressive spatio-temporal predictors are considered. These surrogates are trained on high-fidelity data, generated by direct aeroacoustic numerical solvers. Such datasets are able to model complex flow phenomena, along with complex geometrical parameters. The neural network is designed to substitute the high-fidelity solver at a much lower computational cost once the training is finished, while predicting the time-domain acoustic propagation with sufficient accuracy. Three test cases of growing complexity are employed to test the approach, where the learned surrogate is compared to analytical and numerical solutions. The first one corresponds to the two-dimensional propagation of Gaussian pulses in closed domains, which allows understanding the fundamental behavior of the employed convolution neural networks. Second, the approach is extended in order to consider a variety of boundary conditions, from non-reflecting to curved reflecting obstacles, including the reflection and scattering of waves at obstacles. This allows the prediction of acoustic propagation in configurations closer to industrial problems. Finally, the effects of complex mean flows is investigated through a dataset of acoustic waves propagating inside sheared flows. These applications highlight the flexibility of the employed data-driven methods using convolutional neural networks. They allow a significant acceleration of the acoustic predictions, while keeping an adequate accuracy and being also able to correctly predict the acoustic propagation outside the range of the training data. For that, prior knowledge about the wave propa- gation physics is included during and after the neural network training phase, allowing an increased control over the error performed by the surrogate. Among this prior knowledge, the conservation of physics quantities and the correct treatment of boundary conditions are identified as key parameters that improve the surrogate predictions.Prédire la propagation du bruit aéroacoustique est une tâche difficile en présence d’écoulements moyens complexes et d’effets géométriques d’installation. La conception des futurs systèmes de propulsion silencieux appelle au développement d’outils capables d’effectuer de nombreuses évaluations avec une faible erreur et un faible coût de calcul. Traditionnellement, des modèles analytiques ou des approches numériques hybrides ont été utilisés à cette fin. Cependant, ces méthodes sont généralement contraintes par des hypothèses simplificatrices qui ne sont pas facilement assouplies. Ainsi, l’objectif principal de cette thèse est de développer et de valider de nouvelles méthodes pour la prédiction rapide et précise de la propagation aéroacoustique dans des écoulements moyens et des géométries complexes. Pour cela, des réseaux de neurones profonds à convolution, entraînés sur des données, et agissant comme prédicteurs spatio-temporels sont considérés. Ces modèles par substitution sont entraînés sur des données de haute fidélité, générées par des solveurs numériques aérocoustiques directs. De telles bases de données sont capables de modéliser des phénomènes d’écoulement, ainsi que des paramètres géométriques complexes. Le réseau de neurones est conçu pour remplacer le solveur haute fidélité à un coût de calcul beaucoup plus faible une fois la phase d’entraînement terminée, tout en prédisant la propagation acoustique dans le domaine temporel avec une précision suffisante. Trois cas de test, de complexité croissante, sont utilisés pour tester l’approche, où le substitut appris est comparé à des solutions analytiques et numériques. Le premier cas correspond à la propagation acoustique bidimensionnelle dans des domaines fermés, où des sources impulsionnelles Gaussiennes sont considérées. Ceci permet de comprendre le comportement fondamental des réseaux de neurones à convolution étudiés. Deuxièmement, l’approche est étendue afin de prendre en compte une variété de conditions aux limites, notamment des conditions aux limites non réfléchissantes et des obstacles réfléchissants de géométrie arbitraire, modélisant la réflexion et la diffusion des ondes acoustiques sur ces obstacles. Cela permet de prédire la propagation acoustique dans des configurations plus proches des problématiques industrielles. Enfin, les effets des écoulements moyens complexes sont étudiés à travers une base de données d’ondes acoustiques qui se propagent à l’intérieur d’écoulements cisaillés. Ces applications mettent en évidence la flexibilité des méthodes basées sur les données, utilisant des réseaux de neurones à convolution. Ils permettent une accélération significative des prédictions acoustiques, tout en gardant une précision adéquate et en étant également capables de prédire correctement la propagation acoustique en dehors de la gamme de paramètres des données d’apprentissage. Pour cela, des connaissances préalables sur la physique de propagation des ondes sont incluses pendant et après la phase d’apprentissage du réseau de neurones, permettant un contrôle accru sur l’erreur effectuée par le substitut. Parmi ces connaissances préalables, la conservation des grandeurs physiques et le traitement correct des conditions aux limites sont identifiés comme des paramètres clés qui améliorent les prédictions du modèle proposé

Journal of Telecommunications and Information Technology, 2002, nr 1

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Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Detecting language activations with functional magnetic resonance imaging

This thesis investigates a number of factors that affect sensitivity to language activations in functional Magnetic Resonance Imaging (fMRI). In the first part, I investigate the impact of experimental design parameters on the ability to detect language activations. These parameters include stimulus rate, stimulus duration, stimulus amplitude, epoch length and stimulus ordering. Crucially, they may affect sensitivity in multiple ways that include neurophysiological, efficiency-mediated and BOLD saturation effects. I illustrate and discuss these effects by presenting biophysical simulations and fMRI studies of single word and pseudoword reading. In addition, I focus on the differential effects of the above parameters in Positron Emission Tomography and fMRI studies. In the second part, I investigate the impact of the analysis used to estimate effects of interest from the data. I compare event-related and epoch analyses and show that, even in the context of blocked design fMRI, an event-related model may provide greater sensitivity than an epoch model. I then address the notion that experimentally-induced effects may be detected not only as task-dependent changes in regional responses but also as changes in connectivity amongst functionally connected regions. These two complementary approaches are motivated by two fundamental principles of brain organisation: functional specialisation and functional integration. I present two fMRI studies investigating the neural correlates of reading words and pseudowords in terms of functional specialisation and functional integration. Furthermore, in both studies I address the issue of inter-subject variability, which may be a critical determinant of sensitivity. Men

Aeronautical engineering: A continuing bibliography with indexes (supplement 251)

This bibliography lists 526 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Aeronautical engineering: A cumulative index to a continuing bibliography (supplement 274)

This publication is a cumulative index to the abstracts contained in supplements 262 through 273 of Aeronautical Engineering: A Continuing Bibliography. The bibliographic series is compiled through the cooperative efforts of the American Institute of Aeronautics and Astronautics (AIAA) and the National Aeronautics and Space Administration (NASA). Seven indexes are included: subject, personal author, corporate source, foreign technology, contract number, report number, and accession number

Technology for large space systems: A bibliography with indexes (supplement 14)

This bibliography lists 645 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1, 1985 and December 31, 1985. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems