Infrared image enhancement based on hybrid-domain consideration and data fusion methods

Au cours des dernières décennies, l’effort sur les applications de capteurs infrarouges a largement progressé dans le monde. Mais, une certaine difficulté demeure, en ce qui concerne le fait que les objets ne sont pas assez clairs ou ne peuvent pas toujours être distingués facilement dans l’image obtenue pour la scène observée. L’amélioration de l’image infrarouge a joué un rôle important dans le développement de technologies de la vision infrarouge de l’ordinateur, le traitement de l’image et les essais non destructifs, etc. Cette thèse traite de la question des techniques d’amélioration de l’image infrarouge en deux aspects, y compris le traitement d’une seule image infrarouge dans le domaine hybride espacefréquence, et la fusion d’images infrarouges et visibles employant la technique du nonsubsampled Contourlet transformer (NSCT). La fusion d’images peut être considérée comme étant la poursuite de l’exploration du modèle d’amélioration de l’image unique infrarouge, alors qu’il combine les images infrarouges et visibles en une seule image pour représenter et améliorer toutes les informations utiles et les caractéristiques des images sources, car une seule image ne pouvait contenir tous les renseignements pertinents ou disponibles en raison de restrictions découlant de tout capteur unique de l’imagerie. Nous examinons et faisons une enquête concernant le développement de techniques d’amélioration d’images infrarouges, et ensuite nous nous consacrons à l’amélioration de l’image unique infrarouge, et nous proposons un schéma d’amélioration de domaine hybride avec une méthode d’évaluation floue de seuil amélioré, qui permet d’obtenir une qualité d’image supérieure et améliore la perception visuelle humaine. Les techniques de fusion d’images infrarouges et visibles sont établies à l’aide de la mise en oeuvre d’une mise en registre précise des images sources acquises par différents capteurs. L’algorithme SURF-RANSAC est appliqué pour la mise en registre tout au long des travaux de recherche, ce qui conduit à des images mises en registre de façon très précise et des bénéfices accrus pour le traitement de fusion. Pour les questions de fusion d’images infrarouges et visibles, une série d’approches avancées et efficaces sont proposés. Une méthode standard de fusion à base de NSCT multi-canal est présente comme référence pour les approches de fusion proposées suivantes. Une approche conjointe de fusion, impliquant l’Adaptive-Gaussian NSCT et la transformée en ondelettes (Wavelet Transform, WT) est propose, ce qui conduit à des résultats de fusion qui sont meilleurs que ceux obtenus avec les méthodes non-adaptatives générales. Une approche de fusion basée sur le NSCT employant la détection comprime (CS, compressed sensing) et de la variation totale (TV) à des coefficients d’échantillons clairsemés et effectuant la reconstruction de coefficients fusionnés de façon précise est proposée, qui obtient de bien meilleurs résultats de fusion par le biais d’une pré-amélioration de l’image infrarouge et en diminuant les informations redondantes des coefficients de fusion. Une procédure de fusion basée sur le NSCT utilisant une technique de détection rapide de rétrécissement itératif comprimé (fast iterative-shrinking compressed sensing, FISCS) est proposée pour compresser les coefficients décomposés et reconstruire les coefficients fusionnés dans le processus de fusion, qui conduit à de meilleurs résultats plus rapidement et d’une manière efficace.

Multiclass Adaboost Based on an Ensemble of Binary Adaboosts

This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts which is organized as Binary Decision Tree (BDT). It is proved that binary AdaBoost is extremely successful in producing accurate classification but it does not perform very well for multi-class problems. To avoid this performance degradation, the multi-class problem is divided into a number of binary problems and binary AdaBoost classifiers are invoked to solve these classification problems. This approach is tested with a dataset consisting of 6500 binary images of traffic signs. Haar-like features of these images are computed and the multi-class AdaBoost classifier is invoked to classify them. A classification rate of 96.7% and 95.7% is achieved for the traffic sign boarders and pictograms, respectively. The proposed approach is also evaluated using a number of standard datasets such as Iris, Wine, Yeast, etc. The performance of the proposed BDT classifier is quite high as compared with the state of the art and it converges very fast to a solution which indicates it as a reliable classifier.

Double Fourier analysis for Emotion Identification in Voiced Speech

We propose a novel analysis alternative, based on two Fourier Transforms for emotion recognition from speech -- Fourier analysis allows for display and synthesizes different signals, in terms of power spectral density distributions -- A spectrogram of the voice signal is obtained performing a short time Fourier Transform with Gaussian windows, this spectrogram portraits frequency related features, such as vocal tract resonances and quasi-periodic excitations during voiced sounds -- Emotions induce such characteristics in speech, which become apparent in spectrogram time-frequency distributions -- Later, the signal time-frequency representation from spectrogram is considered an image, and processed through a 2-dimensional Fourier Transform in order to perform the spatial Fourier analysis from it -- Finally features related with emotions in voiced speech are extracted and presented

Devaluation, competitiveness and new business formation in emerging countries

In September 2010, Brazil’s Finance Minister, Guido Mantega, used the term “currency war” with reference to monetary policies implemented by different countries to generate an artificial devaluation of their currency and achieve a cheaper, more competitive domestic economy that may be attractive to foreign investors. Similar cases have been documented since the 1930s Great Depression, when several countries abandoned the gold standard as backing for their currencies. More recently, a large-scale asset purchase by Japan’s Central Bank in 2013 was singled out as a strategy aimed at generating devaluation of the yen. This research uses statistics of new business formation density reported by Doing Business for 30 emerging countries in the period 2004-2011 to evaluate the impact of devaluation measured by the behavior of the real effective exchange rate (REER) on the rate of new business formation (NBF). It is determined how variables associated with competitiveness affect the relationship between devaluation and business formation. Results show that devaluation has a positive effect on NBF in the short term, which gets diluted in the long term. Countries with greater competitiveness have less dependence on devaluation to increase the number of businesses.

Export Behavior and Board Independence in Colombian Family Firms: The Reverse Causality Relationship

In the context of greater market liberalization in Latin America, one issue that merits greater attention for empirical investigation is the international expansion of family-owned business. Specifically, the relationship between export behavior, family control and board composition in the Latin American context is absent in the literature. Using a large and unique database from Colombian firms (33,249 firms in the period of 2008 to 2013), we provide insightful information on the determinants of export behavior of family firms in emerging markets. Our empirical test confirms an endogenous relation between boards’ composition (specifically the presence of independent members) and export behavior in family firms. Firms with a higher participation of independent board members are more likely to exhibit higher levels of exports. A "virtuous cycle" was also detected whereby the introduction of independent members on the board can be expected to boost export behavior, which in turn will encourage the increase of independent members on the board of private firms.

Implementación de un detector de coral utilizando filtros Gabor Wavelets y máquinas de aprendizaje

Este trabajo se enfoca en la implementación de un detector de arrecife de coral de desempeño rápido que se utiliza para un vehículo autónomo submarino (Autonomous Underwater Vehicle, AUV, por sus siglas en inglés). Una detección rápida de la presencia de coral asegura la estabilización del AUV frente al arrecife en el menor tiempo posible, evitando colisiones con el coral. La detección de coral se hace en una imagen que captura la escena que percibe la cámara del AUV. Se realiza una clasificación píxel por píxel entre dos clases: arrecife de coral y el plano de fondo que no es coral. A cada píxel de la imagen se le asigna un vector característico, el mismo que se genera mediante el uso de filtros Gabor Wavelets. Éstos son implementados en C++ y la librería OpenCV. Los vectores característicos son clasificados a través de nueve algoritmos de máquinas de aprendizaje. El desempeño de cada algoritmo se compara mediante la precisión y el tiempo de ejecución. El algoritmo de Árboles de Decisión resultó ser el más rápido y preciso de entre todos los algoritmos. Se creó una base de datos de 621 imágenes de corales de Belice (110 imágenes de entrenamiento y 511 imágenes de prueba).

Adquisición y análisis de señales cerebrales utilizando el dispositivo MindWave

La actividad cerebral puede ser monitoreada mediante la electroencefalografía y utilizada como un indicador bioeléctrico. Este articulo muestra como un dispositivo de bajo costo y fácil acceso puede utilizarse para el desarrollo de aplicaciones basadas en interfaces cerebro-computador (BCI). Los resultados obtenidos muestran que el dispositivo MindWave puede efectivamente utilizarse para la adquisición de señales relacionadas a la actividad cerebral en diversas actividades cerebrales bajo la influencia de diversos estímulos. Se propone además el uso de la transformada Wavelet para el acondicionamiento de las señales EEG con el objetivo de utilizar algoritmos de inteligencia artificial y técnicas de reconocimiento de patrones para distinguir respuestas cerebrales.

Electroencephalogram cepstral distances in alzheimer’s disease diagnosis

Alzheimer's disease (AD) represents one ofthe greatest public health challenges worldwide nowadays, because it affects millions of people ali o ver the world and it is expected that the disease will increase considerably in the near future. This study is the first application attempt of cepstral analysis on Electroencephalogram (EEG) signals to find new parameters in arder to achieve a better differentiation belween EEGs of AD patients and Control subjects. The results show that the methodology that uses a combined Wavelet (WT) Biorthogonal (Bior) 3.5 and cepstrum analysis was able to describe the EEG dynamics with a higher discriminative power than the other WTs/spectmm methodologies m previous studies. The most important significance figures were found in cepstral distances between cepstrums oftheta and alpha bands (p=0. 00006<0. 05).

Diagnóstico automático de patologías cardíacas de bloqueo

Los electrocardiogramas (ECG) registran la actividad eléctrica del corazón a través de doce señales principales denominadas derivaciones. Estas derivaciones son analizadas por expertos médicos observando aquellos segmentos de la señal eléctrica que determinan cada una de las patologías que pueden afectar al corazón. Este hecho en general, es un condicionante muy importante para el diseño de sistemas expertos de diagnóstico médico, ya que es preciso conocer, delimitar y extraer de la señal eléctrica aquellos segmentos que determinan la patología. Dar solución a estos problemas, sería fundamental para facilitar el diseño de sistemas expertos para el diagnóstico de enfermedades cardiacas. El objetivo de este trabajo es demostrar que es posible identificar patologías cardiacas analizando la señal completa de las diferentes derivaciones de los ECGs, y determinar puntos concretos que determinan la patología en lugar de segmentos de la señal. Para ello se ha utilizado una BBDD de electrocardiogramas y se ha determinado mediante un algoritmo, los puntos de la señal que determinan la patología. Se ha aplicado a la patología de bloqueos de rama y los puntos obtenidos con el algoritmo se han utilizado para el diseño de un clasificador automático basado en redes neuronales artificiales, obteniendo un coeficiente de sensibilidad del 100% y de especificad del 99.24%, demostrando su validez para el diseño de sistemas expertos de clasificación.

Respiratory syncytial virus seasonality in Brazil: implications for the immunisation policy for at-risk populations

Respiratory syncytial virus (RSV) infection is the leading cause of hospitalisation for respiratory diseases among children under 5 years old. The aim of this study was to analyse RSV seasonality in the five distinct regions of Brazil using time series analysis (wavelet and Fourier series) of the following indicators: monthly positivity of the immunofluorescence reaction for RSV identified by virologic surveillance system, and rate of hospitalisations per bronchiolitis and pneumonia due to RSV in children under 5 years old (codes CID-10 J12.1, J20.5, J21.0 and J21.9). A total of 12,501 samples with 11.6% positivity for RSV (95% confidence interval 11 - 12.2), varying between 7.1 and 21.4% in the five Brazilian regions, was analysed. A strong trend for annual cycles with a stable stationary pattern in the five regions was identified through wavelet analysis of the indicators. The timing of RSV activity by Fourier analysis was similar between the two indicators analysed and showed regional differences. This study reinforces the importance of adjusting the immunisation period for high risk population with the monoclonal antibody palivizumab taking into account regional differences in seasonality of RSV.

Texture-Detail Preservation Measurement in Camera Phones: An Updated Approach

Recent advances in mobile phone cameras have poised them to take over compact hand-held cameras as the consumer’s preferred camera option. Along with advances in the number of pixels, motion blur removal, face-tracking, and noise reduction algorithms have significant roles in the internal processing of the devices. An undesired effect of severe noise reduction is the loss of texture (i.e. low-contrast fine details) of the original scene. Current established methods for resolution measurement fail to accurately portray the texture loss incurred in a camera system. The development of an accurate objective method to identify the texture preservation or texture reproduction capability of a camera device is important in this regard. The ‘Dead Leaves’ target has been used extensively as a method to measure the modulation transfer function (MTF) of cameras that employ highly non-linear noise-reduction methods. This stochastic model consists of a series of overlapping circles with radii r distributed as r−3, and having uniformly distributed gray level, which gives an accurate model of occlusion in a natural setting and hence mimics a natural scene. This target can be used to model the texture transfer through a camera system when a natural scene is captured. In the first part of our study we identify various factors that affect the MTF measured using the ‘Dead Leaves’ chart. These include variations in illumination, distance, exposure time and ISO sensitivity among others. We discuss the main differences of this method with the existing resolution measurement techniques and identify the advantages. In the second part of this study, we propose an improvement to the current texture MTF measurement algorithm. High frequency residual noise in the processed image contains the same frequency content as fine texture detail, and is sometimes reported as such, thereby leading to inaccurate results. A wavelet thresholding based denoising technique is utilized for modeling the noise present in the final captured image. This updated noise model is then used for calculating an accurate texture MTF. We present comparative results for both algorithms under various image capture conditions.

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization

SVR-GARCH com misturas de kernels gaussianos

Dissertação (mestrado)—Universidade de Brasília, Departamento de Administração, Programa de Pós-graduação em Administração, 2016.

Um modelo matemático baseado em wavelets para análise do método térmico de recuperação de óleo pesado aplicando irradiação eletromagnética

This work proposes a model to investigate the use of a cylindrical antenna used in the thermal method of recovering through electromagnetic radiation of high-viscosity oil. The antenna has a simple geometry, adapted dipole type, and it can be modelled by using Maxwell s equation. The wavelet transforms are used as basis functions and applied in conjunction with the method of moments to obtain the current distribution in the antenna. The electric field, power and temperature distribution are carefully calculated for the analysis of the antenna as electromagnetic heating. The energy performance is analyzed based on thermo-fluid dynamic simulations at field scale, and through the adaptation in the Steam Thermal and Advanced Processes Reservoir Simulator (STARS) by Computer Modelling Group (CMG). The model proposed and the numerical results obtained are stable and presented good agreement with the results reported in the specialized literature

A mobile image enhancement technology for visually impaired

In this thesis, an image enhancement application is developed for low-vision patients when they use iPhones to see images/watch videos. The thesis has two contributions. The first contribution is the new image enhancement algorithm which combines human vision features. The new image enhancement algorithm is modified from a wavelet transform based image enhancement algorithm developed by Dr. Jinshan Tang. Different from the original algorithm, the new image enhancement algorithm combines human visual feature into the algorithm and thus can make the new algorithm more effective. Experimental simulation results show that the proposed algorithm has better visual results than the algorithm without combining visual features. The second contribution of this thesis is the development of a mobile image enhancement application. In this application, users with low-vision can see clearer images on an iPhone which is installed with the application I have developed.