This thesis deals with the introduction into the topic of time-lapse video creation. It focuses on cases where tripod is not used and therefore it is  necessary to eliminate incurred shortcomings. The main shortcomings are different position of individual frames, different brightness and color adjustment. The next topic describes which principles should be followed during the creation process. Thesis describes and implements methods for elimination of main shortcomings during process long time-lapse videos, which are recorded by hand. Thesis also precisely describes image registration, correction of brightness and colors. Thesis is also considers histograms comparison. Result of this work is application, which eliminates problems described above.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-04-18T15:01:39Z No. of bitstreams: 1 MARCELL MANFRIN BARBACENA - DISSERTAÇÃO PPGCC 2014..pdf: 1468565 bytes, checksum: b94d20ffdace21ece654986ffd8fbb63 (MD5) / Made available in DSpace on 2018-04-18T15:01:39Z (GMT). No. of bitstreams: 1 MARCELL MANFRIN BARBACENA - DISSERTAÇÃO PPGCC 2014..pdf: 1468565 bytes, checksum: b94d20ffdace21ece654986ffd8fbb63 (MD5) Previous issue date: 2014 / Impulsionadas pela crescente demanda por sistemas de segurança para proteção do indivíduo e da propriedade nos dias atuais, várias pesquisas têm sido desenvolvidas com foco na implantação de sistemas de vigilância por vídeo com ampla cobertura. Um dos problemas de pesquisa em aberto nas áreas de visão computacional e redes de computadores envolvem a escalabilidade desses sistemas, principalmente devido ao aumento do número de câmeras transmitindo vídeos em tempo real para monitoramento e processamento. Neste contexto, o objetivo geral deste trabalho é avaliar o impacto que a redução da taxa de transmissão dos fluxos de vídeos impõe na eficácia dos algoritmos de detecção de pessoas utilizados em sistemas inteligentes de videovigilância. Foram realizados experimentos utilizando vídeos em alta resolução no contexto de vigilância com tomadas externas e com um algoritmo de detecção de pessoas baseado em histogramas de gradientes orientados, nos quais se coletou, como medida de eficácia do algoritmo, a métrica de área sob a curva de precisão e revocação para, em sequência, serem aplicados os testes estatísticos de Friedman e de comparações múltiplas com um controle na aferição das hipóteses levantadas. Os resultados obtidos indicaram que é possível uma redução da taxa de transmissão em mais de 70% sem que haja redução da eficácia do algoritmo de detecção de pessoas. / Motivated by the growing demand for security systems to protect persons and properties in the nowadays, several researches have been developed focusing on the deployment of widearea video coverage surveillance systems. One open research problem in the areas of computer vision and computer networks involves the scalability of these systems, mainly due to the increasing number of cameras transmitting real-time video for monitoring and processing. In this context, the aim of this study was to evaluate the impact that transmission data-rate reduction of video streams imposes on the effectiveness of people detection algorithms used in intelligent video surveillance systems. With a proposed experimental design, experiments were performed using high-resolution wide-area external coverage video surveillance and using an algorithm for people detection based on histograms of oriented gradients. As a measure of effectiveness of the people detection algorithm, the metric of area under the precision-recall curve was collected and statistical tests of Friedman and multiple comparisons with a control were applied to evaluate the hypotheses. The results indicated that it is possible to reduce transmission rate by more than 70% without decrease in the effectiveness of the people detection algorithm.

This master thesis describes the design and development of the system for detection and recognition of whole coat of arms as well as each heraldic parts. In the thesis are presented methods of computer vision for segmentation and detection of an object and selected methods that are the most suitable. Most of the heraldic parts are segmented using a convolution neural networks and the rest using active contours. The Histogram of the gradient method was selected for coats of arms detection in an image. For training and functionality verification is used my own data set. The resulting system can serve as an auxiliary tool used in auxiliary sciences of history.

This master’s Thesis focuses on image processing on Android platform and development of an application, that is able to do face detection and recognition in real scene. Thesis gives highlight of modern algorithms of face detection. It first examines and compares the standard features of Android platform (FaceDetector a FaceDetectionListener) and JJIL, OpenIMAJ, OpenCV libraries experiment, and presents the results. For purposes of face recognition was selected OpenCV library. Three different algorithms of identification were tested: FisherFaces, EigenFaces a Local Binary Patterns Histograms. Based on performance comparison best methods were implemented in developed application.

Cette thèse a pour but l'enrichissement des méthodes supervisées d'analyse de données symboliques et l'extension de ce domaine aux données volumineuses, dites "Big Data". Nous proposons à cette fin une méthode supervisée nommée HistSyr. HistSyr convertit automatiquement les variables continues en histogrammes les plus discriminants pour les classes d'individus. Nous proposons également une nouvelle méthode d'arbres de décision symbolique, dite SyrTree. SyrTree accepte tous plusieurs types de variables explicatives et à expliquer pour construire l'arbre de décision symbolique. Enfin, nous étendons HistSyr aux Big Data, en définissant une méthode distribuée nommée CloudHistSyr. CloudHistSyr utilise Map/Reduce pour créer les histogrammes les plus discriminants pour des données trop volumineuses pour HistSyr. Nous avons testé CloudHistSyr sur Amazon Web Services (AWS). Nous démontrons la scalabilité et l’efficacité de notre méthode sur des données simulées et sur les données expérimentales. Nous concluons sur l’utilité de CloudHistSyr qui , grâce à ses résultats, permet l'étude de données massives en utilisant les méthodes d'analyse symboliques existantes. / This Thesis proposes new supervised methods for Symbolic Data Analysis (SDA) and extends this domain to Big Data. We start by creating a supervised method called HistSyr that converts automatically continuous variables to the most discriminant histograms for classes of individuals. We also propose a new method of symbolic decision trees that we call SyrTree. SyrTree accepts many types of inputs and target variables and can use all symbolic variables describing the target to construct the decision tree. Finally, we extend HistSyr to Big Data, by creating a distributed method called CloudHistSyr. Using the Map/Reduce framework, CloudHistSyr creates of the most discriminant histograms for data too big for HistSyr. We tested CloudHistSyr on Amazon Web Services. We show the efficiency of our method on simulated data and on actual car traffic data in Nantes. We conclude on overall utility of CloudHistSyr which, through its results, allows the study of massive data using existing symbolic analysis methods.

In the automotive sector, engine testing generates vast data volumes that are mainly beneficial to requesting engineers. However, these tests are often not revisited for further analysis due to inconsistent data quality and a lack of structured assessment methods. Moreover, the absence of a tailored knowledge discovery process hinders effective preprocessing, transformation, analytics, and visualization of data, restricting the potential for historical data insights. Another challenge arises from the heterogeneous nature of test structures, resulting in varying measurements, data types, and contextual requirements across different engine test datasets. This thesis aims to overcome these obstacles by introducing a specialized knowledge discovery approach for the distinctive Multiple Inconsistent Heterogeneous Data (MIHData) format characteristic of engine testing. The proposed methods include adapting data quality assessment and reporting, classifying engine types through compositional features, employing modified dendrogram similarity measures for classification, performing customized feature extraction, transformation, and structuring, generating and manipulating synthetic images to enhance data visualization, and applying adapted list-based indexing for multivariate engine test summary data searches. The thesis demonstrates how these techniques enable exploratory analysis, visualization, and classification, presenting a practical framework to extract meaningful insights from historical data within the engineering domain. The ultimate objective is to facilitate the reuse of past data resources, contributing to informed decision-making processes and enhancing comprehension within the automotive industry. Through its focus on data quality, heterogeneity, and knowledge discovery, this research establishes a foundation for optimized utilization of historical Engine Test Data (ETD) for improved insights. / Soroptimist International Bradford

Les fonctionnelles de Minkowski définissent des mesures topologiques et géométriques d'ensembles, insuffisantes pour la caractérisation, des ensembles différents pouvant avoir les mêmes fonctionnelles. D'autres fonctionnelles de forme, géométriques et morphométriques, sont donc utilisées. Un diagramme de forme, défini grâce à deux fonctionnelles morphométriques, donne une représentation permettant d'étudier les formes d'ensembles. En analyse d'image, ces fonctionnelles et diagrammes sont souvent limités aux images binaires et déterminés de manière globale et mono-échelle. Les Voisinages Adaptatifs Généraux (VAG) simultanément adaptatifs avec les échelles d'analyse, structures spatiales et intensités des images, permettent de pallier ces limites. Une analyse locale, adaptative et multi-échelle des images à tons de gris est proposée sous forme de cartographies des fonctionnelles de forme à VAG.Les VAG, définis en tout point du support spatial d'une image à tons de gris, sont homogènes par rapport à un critère d'analyse représenté dans un modèle vectoriel, suivant une tolérance d'homogénéité. Les fonctionnelles de forme calculées pour chaque VAG de l'image définissent les cartographies des fonctionnelles de forme à VAG. Les histogrammes et diagrammes de ces cartographies donnent des distributions statistiques des formes des structures locales de l'image contrairement aux histogrammes classiques qui donnent une distribution globale des intensités de l'image. L'impact de la variation des critères axiomatiques des VAG est analysé à travers ces cartographies, histogrammes et diagrammes. Des cartographies multi-échelles sont construites, définissant des fonctions de forme à VAG. / Minkowski functionals define set topological and geometrical measurements, insufficient for the characterization, because different sets may have the same functionals. Thus, other shape functionals, geometrical and morphometrical are used. A shape diagram, defined thanks to two morphometrical functionals, provides a representation allowing the study of set shapes. In quantitative image analysis, these functionals and diagrams are often limited to binary images and achieved in a global and monoscale way. The General Adaptive Neighborhoods (GANs) simultaneously adaptive with the analyzing scales, the spatial structures and the image intensities, enable to overcome these limitations. The GAN-based Minkowski functionals are introduced, which allow a gray-tone image analysis to be realized in a local, adaptive and multiscale way.The GANs, defined around each point of the spatial support of a gray-tone image, are homogeneous with respect to an analyzing criterion function represented in an algebraic model, according to an homogeneity tolerance. The shape functionals computed on the GAN of each point of the spatial support of the image, define the so-called GAN-based shape maps. The map histograms and diagrams provide statistical distributions of the shape of the gray-tone image local structures, contrary to the classical histogram that provides a global distribution of image intensities. The impact of axiomatic criteria variations is analyzed through these maps, histograms and diagrams. Thus, multiscale maps are built, defining GAN-based shape functions.

Le but de cette thèse est de développer des méthodes pratiques plus performantes pour la détection d'instances de classes d'objets de la vie quotidienne dans les images. Nous présentons une famille de détecteurs qui incorporent trois types d'indices visuelles performantes - histogrammes de gradients orientés (Histograms of Oriented Gradients, HOG), motifs locaux binaires (Local Binary Patterns, LBP) et motifs locaux ternaires (Local Ternary Patterns, LTP) - dans des méthodes de discrimination efficaces de type machine à vecteur de support latent (Latent SVM), sous deux régimes de réduction de dimension - moindres carrées partielles (Partial Least Squares, PLS) et sélection de variables par élagage de poids SVM (SVM Weight Truncation). Sur plusieurs jeux de données importantes, notamment ceux du PASCAL VOC2006 et VOC2007, INRIA Person et ETH Zurich, nous démontrons que nos méthodes améliorent l'état de l'art du domaine. Nos contributions principales sont : Nous étudions l'indice visuelle LTP pour la détection d'objets. Nous démontrons que sa performance est globalement mieux que celle des indices bien établies HOG et LBP parce qu'elle permet d'encoder à la fois la texture locale de l'objet et sa forme globale, tout en étant résistante aux variations d'éclairage. Grâce à ces atouts, LTP fonctionne aussi bien pour les classes qui sont caractérisées principalement par leurs structures que pour celles qui sont caractérisées par leurs textures. En plus, nous démontrons que les indices HOG, LBP et LTP sont bien complémentaires, de sorte qu'un jeux d'indices étendu qui intègre tous les trois améliore encore la performance. Les jeux d'indices visuelles performantes étant de dimension assez élevée, nous proposons deux méthodes de réduction de dimension afin d'améliorer leur vitesse et réduire leur utilisation de mémoire. La première, basée sur la projection moindres carrés partielles, diminue significativement le temps de formation des détecteurs linéaires, sans réduction de précision ni perte de vitesse d'exécution. La seconde, fondée sur la sélection de variables par l'élagage des poids du SVM, nous permet de réduire le nombre d'indices actives par un ordre de grandeur avec une réduction minime, voire même une petite augmentation, de la précision du détecteur. Malgré sa simplicité, cette méthode de sélection de variables surpasse toutes les autres approches que nous avons mis à l'essai.

The aim of this work is informed first about photovoltaics universally, works to inform the photovoltaic panels and complete plants. The work also includes instructions on how to implement PVP in accordance with law. Another part is the rough draft of the photovoltaic power 30 kWp, which can be placed on the house, computation and calculation of investment and them profitable investments to time. Design is made in two separate forms of the Fronius Solar and Sunny Design, their outputs are compared. The practical part of this work cooperates with the company SOLARTEC Ltd. for experimental measurements of the photovoltaic system and develop a methodology for setting the properties of real solar systems in operation from the measured data then stored in a database. These data further evaluate and compare the similar operating conditions. This data will show as the course of production of electricity during the typical day in percentage terms, depending on the incident irradiance, cell temperature, angle of incident radiation, etc. We can compare what it looks like an ideal day in terms of production of photovoltaic power, with the other days. Further are in work mentioned histograms achievement panel behind classical day and behind all - time investigation.

This master thesis deals with design and implementation of betting agent. It covers issues such as theoretical background of an online betting, probability and statistics. In its first part it is focused on data mining and explains the principle of knowledge mining form data warehouses and certain methods suitable for different types of tasks. Second, it is concerned with neural networks and algorithm of back-propagation. All the findings are demonstrated on and supported by graphs and histograms of data analysis, made via SAS Enterprise Miner program. In conclusion, the thesis summarizes all the results and offers specific methods of extension of the agent.