Dans les réseaux énergétiques, la connaissance des équipements, leurs emplacements et leursfonctions sont les prérequis à l’exploitation de l’infrastucture. En effet, tout opérateur disposed’une carte appelée schéma synoptique indiquant les connexions entre les équipements. À partirde cette carte, sont prises des décisions pour un fonctionnement optimal du réseau.Ce schéma synoptique peut être érronné parce que des opérations de maintenance sur le réseaun’auraient pas été retranscrites ou mal saisies. Et cela peut entrainer des coûts supplémentairesd’exploitation du réseau énergetique.Nous considérons le réseau électrique d’un Datacenter. Ce réseau est composé d’une topologiephysique modélisée par un DAG sans circuit et de mesures électriques sur ces arcs. La particularitéde ce réseau est que les mesures contiennent des erreurs et cette topologie est inconnue c’est-à-direles arcs sont connus mais les extrémités des arcs sont inconnues. Dans le cas où ces mesuressont correctes alors la corrélation des arcs induit la matrice d’adjacence du line-graphe du graphenon-orienté sous-jacent de notre DAG. Un line-graphe est un graphe dans lequel chaque sommet etson voisinage peuvent être partitionnés par une ou deux cliques et que chaque arête est couvertepar une clique. Cependant, avec la présence des erreurs de mesures, nous avons un graphe avecdes arêtes en plus ou en moins qui n’est pas nécessairement un line-graphe. Si ce graphe est unline-graphe alors il n’est pas le line-graphe de notre DAG. Notre problème est de découvrir cettetopologie en se basant sur ces mesures électriques.Nous débutons par une étude bibliographique des corrélations de mesures possibles afin dedéterminer celle qui est pertinente pour notre problème. Ensuite nous proposons deux algorithmespour résoudre ce problème. Le premier algorithme est l’algorithme de couverture et il déterminel’ensemble des cliques qui couvre chaque sommet de notre graphe. Le second algorithme estl’algorithme de correction. Il ajoute ou supprime des arêtes au voisinage d’un sommet non couvertde telle sorte que son voisinage soit partitionné en une ou deux cliques. Enfin, nous évaluons lesperformances de nos algorithmes en vérifiant le nombre d’arêtes corrigées et la capacité à retournerle graphe le plus proche du line-graphe de notre DAG. / In energy network, the knowledge of equipments, their locations and their functions are theimportant information for the distributor service operator. In fact, each operator has a networkplan often named synoptic schema. That schema shows the interconnexion between equipments inthe network. From this schema, some management decisions have taken for ensuring an optimalperformance of a network.Sometimes, a synoptic schema has some mistakes because the maintenance operations, such aschanged the connexion between equipments or replaced equipments, have not been updated orhave been written with errors. And these mistakes increase exploitation cost in the energy network.We consider an electric network of a datacenter. This network consists of physical topologymodelised by a DAG without circuit and measurements are on the edges of a DAG. The mainpoint of the network is that measurements are some mistakes and the topology is unknown i.ewe know edges but the nodes of edges are unknown. When measurements are correct then thecorrelations between pairwise edges provide the adjacency matrix of the linegraph of undirectedgraph of the DAG. A linegraph is a graph in which each node and the neighbor are partitionnedby one or deux cliques. However, with the mistakes in measurements, the obtained graph is nota linegraph because it contains more or less edges. If the obtained graph is a linegraph then it isa linegraph of the other DAG. Our problem is to discovery the topology of the DAG with somemistakes in measurements.We start by the state of art in the measurement correlations in order to choose the good methodfor our problem. Then, we propose two algorithms to resolve our problem. The first algorithmis the cover algorithm and it returns the set of cliques in the graph. The second algorithm is acorrection algorithm which adds or deletes edges in the graph for getting a nearest linegraph ofthe DAG. In the last, we evaluate the performances of the algorithms by checking the number ofedges corrected and the ability to return a nearest linegraph of the DAG.

La détection de changements dans une scène est l’un des problèmes les plus complexes en télédétection. Il s’agit de détecter des modifications survenues dans une zone géographique donnée par comparaison d’images de cette zone acquises à différents instants. La comparaison est facilitée lorsque les images sont issues du même type de capteur c’est-à-dire correspondent à la même modalité (le plus souvent optique multi-bandes) et possèdent des résolutions spatiales et spectrales identiques. Les techniques de détection de changements non supervisées sont, pour la plupart, conçues spécifiquement pour ce scénario. Il est, dans ce cas, possible de comparer directement les images en calculant la différence de pixels homologues, c’est-à-dire correspondant au même emplacement au sol. Cependant, dans certains cas spécifiques tels que les situations d’urgence, les missions ponctuelles, la défense et la sécurité, il peut s’avérer nécessaire d’exploiter des images de modalités et de résolutions différentes. Cette hétérogénéité dans les images traitées introduit des problèmes supplémentaires pour la mise en œuvre de la détection de changements. Ces problèmes ne sont pas traités par la plupart des méthodes de l’état de l’art. Lorsque la modalité est identique mais les résolutions différentes, il est possible de se ramener au scénario favorable en appliquant des prétraitements tels que des opérations de rééchantillonnage destinées à atteindre les mêmes résolutions spatiales et spectrales. Néanmoins, ces prétraitements peuvent conduire à une perte d’informations pertinentes pour la détection de changements. En particulier, ils sont appliqués indépendamment sur les deux images et donc ne tiennent pas compte des relations fortes existant entre les deux images. L’objectif de cette thèse est de développer des méthodes de détection de changements qui exploitent au mieux l’information contenue dans une paire d’images observées, sans condition sur leur modalité et leurs résolutions spatiale et spectrale. Les restrictions classiquement imposées dans l’état de l’art sont levées grâce à une approche utilisant la fusion des deux images observées. La première stratégie proposée s’applique au cas d’images de modalités identiques mais de résolutions différentes. Elle se décompose en trois étapes. La première étape consiste à fusionner les deux images observées ce qui conduit à une image de la scène à haute résolution portant l’information des changements éventuels. La deuxième étape réalise la prédiction de deux images non observées possédant des résolutions identiques à celles des images observées par dégradation spatiale et spectrale de l’image fusionnée. Enfin, la troisième étape consiste en une détection de changements classique entre images observées et prédites de mêmes résolutions. Une deuxième stratégie modélise les images observées comme des versions dégradées de deux images non observées caractérisées par des résolutions spectrales et spatiales identiques et élevées. Elle met en œuvre une étape de fusion robuste qui exploite un a priori de parcimonie des changements observés. Enfin, le principe de la fusion est étendu à des images de modalités différentes. Dans ce cas où les pixels ne sont pas directement comparables, car correspondant à des grandeurs physiques différentes, la comparaison est réalisée dans un domaine transformé. Les deux images sont représentées par des combinaisons linéaires parcimonieuses des éléments de deux dictionnaires couplés, appris à partir des données. La détection de changements est réalisée à partir de l’estimation d’un code couplé sous condition de parcimonie spatiale de la différence des codes estimés pour chaque image. L’expérimentation de ces différentes méthodes, conduite sur des changements simulés de manière réaliste ou sur des changements réels, démontre les avantages des méthodes développées et plus généralement de l’apport de la fusion pour la détection de changements / Change detection is one of the most challenging issues when analyzing remotely sensed images. It consists in detecting alterations occurred in a given scene from between images acquired at different times. Archetypal scenarios for change detection generally compare two images acquired through the same kind of sensor that means with the same modality and the same spatial/spectral resolutions. In general, unsupervised change detection techniques are constrained to two multiband optical images with the same spatial and spectral resolution. This scenario is suitable for a straight comparison of homologous pixels such as pixel-wise differencing. However, in somespecific cases such as emergency situations, punctual missions, defense and security, the only available images may be of different modalities and of different resolutions. These dissimilarities introduce additional issues in the context of operational change detection that are not addressedby most classical methods. In the case of same modality but different resolutions, state-of-the artmethods come down to conventional change detection methods after preprocessing steps appliedindependently on the two images, e.g. resampling operations intended to reach the same spatialand spectral resolutions. Nevertheless, these preprocessing steps may waste relevant informationsince they do not take into account the strong interplay existing between the two images. The purpose of this thesis is to study how to more effectively use the available information to work with any pair of observed images, in terms of modality and resolution, developing practicalcontributions in a change detection context. The main hypothesis for developing change detectionmethods, overcoming the weakness of classical methods, is through the fusion of observed images. In this work we demonstrated that if one knows how to properly fuse two images, it is also known how to detect changes between them. This strategy is initially addressed through a change detection framework based on a 3-step procedure: fusion, prediction and detection. Then, the change detection task, benefiting from a joint forward model of two observed images as degradedversions of two (unobserved) latent images characterized by the same high spatial and highspectral resolutions, is envisioned through a robust fusion task which enforces the differencesbetween the estimated latent images to be spatially sparse. Finally, the fusion problem isextrapolated to multimodal images. As the fusion product may not be a real quantity, the process is carried out by modelling both images as sparse linear combinations of an overcomplete pair of estimated coupled dictionaries. Thus, the change detection task is envisioned through a dual code estimation which enforces spatial sparsity in the difference between the estimated codes corresponding to each image. Experiments conducted in simulated realistically and real changes illustrate the advantages of the developed method, both qualitatively and quantitatively, proving that the fusion hypothesis is indeed a real and effective way to deal with change detection

Um dos desafios da Inteligência Artificial aplicada em jogos é o aprendizado de comportamento, em que o objetivo é utilizar estatísticas obtidas da interação entre jogador e jogo de modo a reconhecer características particulares de um jogador ou monitorar a evolução de seu comportamento no decorrer do tempo. A maior parte dos trabalhos na área emprega modelos previamente aprendidos, por meio da utilização de algoritmos de Aprendizado de Máquina. Entretanto, são poucos os trabalhos que consideram que o comportamento de um jogador pode evoluir no tempo e que, portanto, reconhecer quando essas mudanças ocorrem é o primeiro passo para produzir jogos que se adaptam automaticamente às capacidades do jogador. Para detectar variações comportamentais em um jogador, são necessários algoritmos que processem dados de modo incremental. Esse pré-requisito motiva o estudo de algoritmos para detecção de mudanças da área de Mineração em Fluxos Contínuos de Dados. Entretanto, algumas das características dos algoritmos disponíveis na literatura inviabilizam sua aplicação direta ao problema de detecção de mudança em jogos. Visando contornar essas dificuldades, esta tese propõe duas novas abordagens para detecção de mudanças de comportamento. A primeira abordagem é baseada em um algoritmo incremental de agrupamento e detecção de novidades que é independente do número e formato dos grupos presentes nos dados e que utiliza um mecanismo de janela deslizante para detecção de mudanças de comportamento. A segunda abordagem, por outro lado, é baseada na comparação de janelas de tempo consecutivas utilizando espectrogramas gerados a partir dos dados contidos em cada janela. Os resultados experimentais utilizando simulações e dados de jogos comerciais indicam a aplicabilidade dos algoritmos propostos na tarefa de detecção de mudanças de comportamento de um jogador, assim como mostram sua vantagem em relação a outros algoritmos para detecção de mudança disponíveis na literatura / One of the challenges of Artificial Intelligence applied to games is behavior learning, where the objective is to use statistics derived from the interaction between the player and the game environment in order to recognize particular player characteristics or to monitor the evolution of a players behavior along time. The majority of work developed in this area applies models that were previously learned through the use of Machine Learning techniques. However, only a few pieces of work consider that the players behavior can evolve over time and, therefore, recognizing when behavior changes happen is the first step towards the production of games that adapt to the players needs. In order to detect changes in the behavior of a player, incremental algorithms are necessary, what motivates the study of change detection algorithms from the area of Data Stream Mining. However, some of the characteristics of the algorithms available in the literature make their application to the task of change detection in games unfeasible. To overcome these difficulties, this work proposes two new approaches for change detection. The first approach is based on an incremental clustering and novelty detection algorithm which is independent of the number and format of clusters and uses a mechanism for change detection based on sliding windows. The second approach, on the other hand, is based on the comparison of consecutive time windows using spectrograms created from the data inside each window. Experimental results using simulations and data from commercial games indicate the applicability of the proposed algorithms in the task of detecting a players changing behavior, as well as present their advantage when compared to other change detection algorithms available in the literature

Les systèmes de cartographie mobile sont de plus en plus utilisés pour la cartographie des scènes urbaines. La technologie de scan laser mobile (où le scanner est embarqué sur un véhicule) en particulier permet une cartographie précise de la voirie, la compréhension de la scène, la modélisation de façade, etc. Dans cette thèse, nous nous concentrons sur la détection de changement entre des nuages de points laser de cartographie mobile. Tout d'abord, nous étudions la détection des changements a partir de données RIEGL (scanner laser plan) pour la mise à jour de bases de données géographiques et l'identification d'objet temporaire. Nous présentons une méthode basée sur l'occupation de l'espace qui permet de surmonter les difficultés rencontrées par les méthodes classiques fondées sur la distance et qui ne sont pas robustes aux occultations et à l'échantillonnage anisotrope. Les zones occultées sont identifiées par la modélisation de l'état d'occupation de l'espace balayé par des faisceaux laser. Les écarts entre les points et les lignes de balayage sont interpolées en exploitant la géométrie du capteur dans laquelle la densité d'échantillonnage est isotrope. Malgré quelques limites dans le cas d'objets pénétrables comme des arbres ou des grilles, la méthode basée sur l'occupation est en mesure d'améliorer la méthode basée sur la distance point à triangle de façon significative. La méthode de détection de changement est ensuite appliquée à des données acquises par différents scanners laser et à différentes échelles temporelles afin de démontrer son large champs d'application. La géométrie d'acquisition est adaptée pour un scanner dynamique de type Velodyne. La méthode basée sur l'occupation permet alors la détection des objets en mouvement. Puisque la méthode détecte le changement en chaque point, les objets en mouvement sont détectés au niveau des points. Comme le scanner Velodyne scanne l'environnement de façon continue, les trajectoires des objets en mouvement peut être extraite. Un algorithme de détection et le suivi simultané est proposé afin de retrouver les trajectoires de piétons. Cela permet d'estimer avec précision la circulation des piétons des circulations douces dans les lieux publics. Les changements peuvent non seulement être détectés au niveau du point, mais aussi au niveau de l'objet. Ainsi nous avons pu étudier les changements entre des voitures stationnées dans les rues à différents moments de la journée afin d'en tirer des statistiques utiles aux gestionnaires du stationnement urbain. Dans ce cas, les voitures sont détectés en premier lieu, puis les voitures correspondantes sont comparées entre des passages à différents moments de la journée. Outre les changements de voitures, l'offre de stationnement et les types de voitures l'utilisant sont également des informations importantes pour la gestion du stationnement. Toutes ces informations sont extraites dans le cadre d'un apprentissage supervisé. En outre, une méthode de reconstruction de voiture sur la base d'un modèle déformable générique ajusté aux données est proposée afin de localiser précisément les voitures. Les paramètres du modèle sont également considérés comme caractéristiques de la voiture pour prendre de meilleures décisions. De plus, ces modèles géométriquement précis peuvent être utilisées à des fins de visualisation. Dans cette thèse, certains sujets liés à la détection des changements comme par exemple, suivi, la classification, et la modélisation sont étudiés et illustrés par des applications pratiques. Plus important encore, les méthodes de détection des changements sont appliquées à différentes géométries d'acquisition de données et à de multiples échelles temporelles et au travers de deux stratégies: “bottom-up” (en partant des points) et “top-down” (en partant des objets) / Mobile mapping systems are increasingly used for street environment mapping, especially mobile laser scanning technology enables precise street mapping, scene understanding, facade modelling, etc. In this research, the change detection from laser scanning point clouds is investigated. First of all, street environment change detection using RIEGL data is studied for the purpose of database updating and temporary object identification. An occupancy-based method is presented to overcome the challenges encountered by the conventional distance-based method, such as occlusion, anisotropic sampling. Occluded areas are identified by modelling the occupancy states within the laser scanning range. The gaps between points and scan lines are interpolated under the sensor reference framework, where the sampling density is isotropic. Even there are some conflicts on penetrable objects, e.g. trees, fences, the occupancy-based method is able to enhance the point-to-triangle distance-based method. The change detection method is also applied to data acquired by different laser scanners at different temporal-scales with the intention to have wider range of applications. The local sensor reference framework is adapted to Velodyne laser scanning geometry. The occupancy-based method is implemented to detection moving objects. Since the method detects the change of each point, moving objects are detect at point level. As the Velodyne scanner constantly scans the surroundings, the trajectories of moving objects can be detected. A simultaneous detection and tracking algorithm is proposed to recover the pedestrian trajectories in order to accurately estimate the traffic flow of pedestrian in public places. Changes can be detected not only at point level, but also at object level. The changes of cars parking on street sides at different times are detected to help regulate on-street car parking since the parking duration is limited. In this case, cars are detected in the first place, then they are compared with corresponding ones. Apart from car changes, parking positions and car types are also important information for parking management. All the processes are solved in a supervised learning framework. Furthermore, a model-based car reconstruction method is proposed to precisely locate cars. The model parameters are also treated as car features for better decision making. Moreover, the geometrically accurate models can be used for visualization purposes. Under the theme of change detection, related topics, e.g. tracking, classification, modelling, are also studied for the reason of practical applications. More importantly, the change detection methods are applied to different data acquisition geometries at multiple temporal-scales. Both bottom-up (point-based) and top-down (object-based) change detection strategies are investigated

Pour l’analyse de la dynamique territoriale il est fondamental une grande quantité de données et leur intégration avec des données spatiales et statistiques facilite ce processus. La thèse se propose d’analyser la dynamique de la superficie agricole des municipalités du Territoire de la Citoyenneté Região Central-RS (TCRCRS) qui fait partie d’une politique publique spatiale de territoires citoyens. Cette dynamique sera analysée à partir d’une technique de détection de changement connue par Rotation Contrôlée par Axe de Non Changement - RCEN. Ainsi, la thèse a comme objectif évaluer les altérations subies par la superficie agricole au long de la période 1985 / 2010 dans les municipalités du TCRCRS en utilisant l’algorithme RCEN. Les étapes méthodologiques suivantes ont été implémentées: utilisation d’images différentes pour l’obtention de pixeis échantillons de non changement; analyse qualitative de l’organisation de la superficie agricole pour les municipalités de Cacequi, Santiago et Tupanciretã, sélectionnés en raison de leur localisation parmi des différentes sous-unités de paysage dans l’État; définition des seuils pour la délimitation des classes thématiques de l’organisation spatiale de la superficie agricole et évaluation de la fiabilité des résultats de la technique RCEN, utilisée pour déterminer la précision de la classification supervisée des images TM Landsat 5 par une matrice de concaténation. La matrice est basée sur l’algèbre de cartes de façon à obtenir une image numérique finale qui exprime toutes les possibilités de l’espace échantillon. Les résultats ont montré que, avec 1% de signification, la technique RCEN peut être utilisée pour détecter la dynamique dans la superficie agricole en utilisant les seuils de vigueur végétatif de l’IDETEC comparés aux résultats des répertoires Normalized Difference Vegetation Index (NDVI), qui a été obtenu tout en considérant le total de pluies antérieures au passage du senseur, variable qui interfère sur les valeurs moyennes de NDVI. Des images de détections de changements (IDETEC) ont été engendrées pour analyser les cultures agricoles d’hiver et d’été, s’obtenant 99% de confiance en les images choisies pour la distribution spatiale des classes définies par l’adoption des seuils de ( - 0,5σ ; – 1,5σ ; + 0,5σ ; + 1,5σ), en prenant comme point central de la classe de non changement. Les images de détection de changements ont permis d’estimer et de comparer les classes de l’IDETEC avec les estimations du total d’aire plantée de cultures temporaires et des cultures agricoles de riz, avoine, maïs, soja et blé. Les aires obtenues par l’IDETEC à Tupanciretã ont surestimé l’aire agricole présentée par l’IBGE dans les images d’été avec des variations en pourcentages entre 1,11% dans l’IDETEC 1994/2009 et 8,13% dans l’IDETEC 2004/2010 ; pour les images d’hiver l’altération a été de 9,46% dans l’IDETEC 1989/2007 et de 3,44% dans l’IDETEC 1996/2005. À la municipalité de Cacequi, les variations en pourcentages de cultures temporaires ont été surestimées dans les images d’été en 7,71% dans l’IDETEC 1986/2006 et 20,47% dans l’IDETEC 1993/2005 et sous-estimées dans les images d’hiver en 9,42% dans l’IDETEC 1985/2003 et en 18,11% dans l’IDETEC 1996/2007. À Santiago elles ont été sous-estimées pour la période d’été en 24,76% dans l’IDETEC 1984/2009, pour la période d’hiver en 10,52% dans l’IDETEC 1996/2005 et surestimées en 8,23% dans l’IDETEC 2004/2010 et en 26,12% pour l’image d’hiver IDETEC 1989/2007. La technique RCEN a prouvé être capable d’évaluer des altérations dans la superficie agricole de cultures annuelles pour les municipalités de Cacequi, Santiago et Tupanciretã. / Para análise da dinâmica territorial, é fundamental uma grande quantidade de dados e a integração com dados espaciais e estatísticos, facilita esse processo. A tese propõe analisar, a dinâmica da superfície agrícola de municípios do Território da Cidadania Região Central-RS (TCRCRS), território este que, faz parte de uma política pública de territórios da cidadania. Essa dinâmica, será analisada a partir de uma técnica de detecção de mudança, conhecida por Rotação Controlada por Eixo de Não Mudança - RCEN. Assim, a tese objetiva avaliar as alterações na superfície agrícola, no período de 1985 a 2010, em municípios do TCRCRS, utilizando o algoritmo RCEN. As seguintes etapas metodológicas, foram implementadas: utilização de imagens diferentes, para obtenção de pixeis amostrais de não mudança; análise qualitativamente da organização da superfície agrícola, para os municípios de Cacequi, Santiago e Tupanciretã, selecionados por sua localização em diferentes subunidades de paisagem no Estado; definição dos limiares, para delimitação das classes temáticas da organização espacial da superfície agrícola e, avaliação da confiabilidade dos resultados da técnica RCEN, utilizada pra determinar, a precisão da classificação supervisionada das imagens TM Landsat 5, através de uma matriz de concatenação. A matriz, é baseada em álgebra de mapas de tal maneira, a obter uma imagem numérica final que, expresse todas as possibilidades do espaço amostral. Os resultados, mostraram que, com 1% de significância, a técnica RCEN pode ser utilizada, para detectar a dinâmica na superfície agrícola, utilizando limiares de vigor vegetativo da IDETEC, comparados aos resultados dos índices Normalized Difference Vegetation Index (NDVI), que foi obtido considerando, o total de chuvas antecedentes a passagem do sensor, que é uma variável que interfere, nos valores médios de NDVI. Foram geradas imagens de detecção de mudanças (IDETEC), para analisar culturas agrícolas de inverno e de verão, obtendo-se 99% de confiança nas imagens selecionadas, para a distribuição espacial das classes definidas pela adoção dos limiares de ( - 0,5σ; – 1,5σ ; + 0,5σ; + 1,5σ), utilizando a como ponto central da classe de não mudança. As imagens de detecção de mudanças, permitiram estimar e comparar as classes da IDETEC, com as estimativas do total de área plantada, de lavouras temporárias, das culturas agrícolas de arroz, aveia, milho, soja e trigo As áreas obtidas pela IDETEC em Tupanciretã, superestimaram a área agrícola, apresentada pelo IBGE, nas imagens de verão com variações percentuais entre 1,11% na IDETEC 1994/2009 e 8,13% na IDETEC 2004/2010, para as imagens de inverno, a alteração foi de 9,46% na IDETEC 1989/2007 e de 3,44% na IDETEC 1996/2005. No município de Cacequi, as variações percentuais de lavouras temporárias foram superestimadas nas imagens de verão em 7,71% na IDETEC 1986/2006 e 20,47% na IDETEC 1993/2005 e, subestimadas nas imagens de inverno em 9,42% na IDETEC 1985/2003 e em 18,11% na IDETEC 1996/2007. Em Santiago, foram subestimadas para o período de verão em 24,76% na IDETEC 1984/2009 e, para o período de inverno em 10,52%, na IDETEC 1996/2005 e superestimadas em 8,23% na IDETEC 2004/2010 e, em 26,12% para a imagem de inverno IDETEC 1989/2007. A técnica RCEN, demonstrou ser capaz de estimar alterações na superfície agrícola, de culturas anuais para os municípios de Cacequi, Santiago e Tupanciretã. / For the analysis of territorial dynamics, a great amount of data is fundamental, and the integration of spatial and statistical data facilitates this process. This thesis proposes to analyze the dynamic of the agricultural surface in the Citizenship Country in the Central Area of Rio Grande do Sul (CCCARS), a country that is part of a public policy of citizenship countries. This dynamics will be analyzed by a change detection technique, known as Rotation Controlled of Non-change Axis (RCNA).Thus, this thesis aims to evaluate the alterations in the agricultural surface, in the period from 1985 to 2010, in CCCARS cities, using the RCNA algorithm. The following methodological steps were implemented: the use of different images in order to obtain non-change sampling pixeis; qualitative analysis of the organization of the agricultural surface in the cities of Cacequi, Santiago and Tupanciretã, which were selected due to their location in different subunits of landscapes in the State; determination of thresholds for the delimitation of thematic clusters in the spatial organization of the agricultural surface; and the evaluation of the reliability of the results of RCNA technique, which was used to determine the accuracy of the supervised classification of Landsat TM 5 images through a concatenating matrix. The matrix is based on the map algebra in such manner that expresses all the possibilities of the sampling space. The results showed that with 1% of significance, the RCNA technique can be used to detect the dynamics of the agricultural surface using threshold of the vigor of the vegetative growth compared with the results of Normalized Difference Vegetation Index (NDVI), which were obtained considering the total amount of rain previous to the sensor scanning, which is a variable that interferes in the medium values of NDVI. It was created images of changes detection (IDETEC) in order to analyze summer and winter agricultural crops, obtaining 99% of reliability on the selected images, for the special distribution of the defined clusters by the adoption of the threshold values of de ( - 0,5σ; – 1,5σ ; + 0,5σ; + 1,5σ), using the as a central point of nonchange cluster. The change detection images enabled to estimate and compare IDETEC clusters with the estimate of the total planted area of temporary farm, agricultural crop of rice, oat, corn, soybean and wheat. The areas obtained by IDETEC in Tupanciretã overestimated the agricultural area presented by IBGE, with summer images with percentage variance among 1,11% on IDETEC 1994/2009 and 8,13% on IDETEC 2004/2010, for the winter images, the alteration was of 9,46% on IDETEC 1989/2007 and of 3,44% on IDETEC 1996/2007. In the city of Cacequi, the percentage variance of the temporary farms were overestimated on the summer images in 7,71% on IDETEC 1986/2006 and 20,47% on IDETEC 1993/2005 and , and overestimated on the winter images in 9,42% on IDETEC 1985/2003 and in 18,11% on IDETEC 1996/2007. In Santiago, they were underestimated for the summer period in 24,76% on IDETEC 1984/2009 and , for the winter period in 10,52%, on IDETEC 1996/2005 e and overestimated in 8,23% on IDETEC 2004/2010 and, in 26,12% for the winter image IDETEC 1989/2007. The RCNA technique showed itself to be capable of estimating the agricultural surface alteration in annual crops in the cities of Cacequi, Santiago e Tupanciretã.

為有效了解地表資訊，可視研究目的而利用各種遙測方法獲取地面影像，但不論目前常用之飛機、或是以衛星為載具之空中攝影測量等相關研究，在影像獲取方面，常面臨成本費用、機動性、比例尺需求、後續影像處理等問題。因此，本研究選定一小流域面積之河川作為實驗區，並設計以氣球為載具之數值航空攝影方式，裝載CCD攝影機以及數位攝影機，透過CCD攝影機，即時無線傳輸地面影像至監視螢幕，調整氣球至預定之位置後，遙控啟動數位攝影機之快門裝置。 　　研究設計之攝影方式曾實際應用於偵測河道之變遷，由成果影像中檢核點之精度結果，以及套疊正射糾正並鑲嵌後之河道影像與地形圖之成果，證明氣球載具之數值航空攝影方式，確能有效應用於大比例尺製圖之研究。 / Using a flexible and efficient way to obtain aerial images has been the primary purpose of this study. The balloon platform was used to take aerial images. A video camera and a digital camera were fixed together in a durable plastic box, and hung on the balloon. The video camera was used to monitor the ground view, and its image could be telemetered remotely and displayed on a LCD monitor arranged on the ground. Once monitoring the area of interest shown on the LCD, the shutter button of the digital camera was then pushed remotely and the interested image was taken. 　　The resultant images were ortho-rectified for analysis and comparison. The accuracy of aerial images was examined by check points. The results showed that the images achieved sub-pixel accuracy and were well-matched with the 1/1000 digital topographic maps. This expressed that it was really a useful and efficient method of taking large-scale images for a small research area. 　　At last, post-classification comparison method was introduced to detect change of the ortho-rectified images which were taken in three different periods. The classification maps and the from-to change class information clearly indicated the change of river way among various periods.

In the base station system of the GSM mobile network there are a large number of counters tracking the behaviour of the system. When the software of the system is updated, we wish to find out which of the counters that have changed their behaviour. This thesis work has shown that the counter data can be modelled as a stochastic time series with a daily profile and a noise term. The change detection can be done by estimating the daily profile and the variance of the noise term and perform statistical hypothesis tests of whether the mean value and/or the daily profile of the counter data before and after the software update can be considered equal. When the chosen counter data has been analysed, it seems to be reasonable in most cases to assume that the noise terms are approximately independent and normally distributed, which justies the hypothesis tests. When the change detection is tested on data where the software is unchanged and on data with known software updates, the results are as expected in most cases. Thus the method seems to be applicable under the conditions studied.

Synthetic aperture radar (SAR) is one of the most versatile tools ever invented for imaging. Due to its better Rayleigh resolution, SAR imaging provides the highest quality radar imagery. These images are used for many applications including but not limited to terrestrial mapping, disaster reconnaissance, medical imaging and military applications. Imaging techniques or geometries which can improve the resolution of the reconstructed imagery is always desired in the SAR imaging. In this dissertation both the linear and nonlinear frequency modulated chirp signals are discussed. The most widely used frequency modulated chirp signal, linear frequency modulated chirp signal, and some of its properties such as spectrum, point spread function and matched filter are summarized. A new nonlinear frequency modulated chirp signal which can be used to improve the image resolution is introduced. In order to validate the offered chirp signal, spotlight SAR imaging geometry together with 2D polar and Stolt format reconstruction algorithms are considered. The synthetic examples are generated using both chirps both with polar and Stolt format processing. Additionally a new change detection method which depends on the idea of generating two different final change maps of the initial and final images in a sequence is offered. The specific algorithms utilized for testing this method are the widely used correlation coefficient change statistic and the intensity ratio change statistic algorithms. This method together with the algorithms mentioned is first applied to synthetic data generated by Stolt format processing. It is shown that the method works on synthetic data. The method together with the algorithms mentioned is also applied to two case studies dfreal disasters, one is 2010 Gulf of Mexico oil spill and the second is 2008 China Sichuan earthquake. It is shown that two final change map method can reduce the false identifications of the changes. Also it is shown that intensity ratio change statistics is a better tool for identifying the changes due to oil contamination. The data used in this study is acquired by Japanese Aerospace Agency's Advanced Land Observing Satellite (ALOS) through Alaska SAR Facility (ASF), at the University of Alaska, Fairbanks.

Change detection is an ability that allows intelligent agents to react to unexpected situations. This mechanism is fundamental in providing more autonomy to robots. It has been used in many different fields including quality control and network intrusion. In the visual domain, however, most research has been confined to stationary cameras and only recently have researchers started to shift to mobile cameras. \ We propose a general framework for building internal spatial models of the visual experiences. These models are used to retrieve expectations about visual inputs which can be compared to the actual observation in order to identify the presence of changes. Our framework leverages the tolerance to small view changes of optic flow and color histogram representations and a self-organizing map to build a compact memory of camera observations. The effectiveness of the approach is demonstrated in a walking simulation, where spatial information and color histograms are combined to detect changes in a room. The location signal allows the algorithm to query the self-organizing map for the expected color histogram and compare it to the current input. Any deviations can be considered changes and are then localized on the input image. Furthermore, we show how detecting a vehicle entering or leaving the camera's lane can be reduced to a change detection problem. This simplifies the problem by removing the need to track or even know about other vehicles. Matching Pursuit is used to learn a compact dictionary to describe the observed experiences. Using this approach, changes are detected when the learned dictionary is unable to reconstruct the current input. The human experiments presented in this dissertation support the idea that humans build statistical models that evolve with experience. We provide evidence that not only does this experience improve people's behavior in 3D environments, but also enables them to detect chromatic changes. Mobile cameras are now part of our everyday lives, ranging from built-in laptop cameras to cell phone cameras. The vision of this research is to enable these devices with change detection mechanisms to solve a large class of problems. Beyond presenting a foundation that effectively detects changes in environments, we also show that the algorithms employed are computationally inexpensive. The practicality of this approach is demonstrated by a partial implementation of the algorithm on commodity hardware such as Android mobile devices. / text

Τα τελευταία χρόνια, η δημοτικότητα του διαδικτύου αυξάνεται ολοένα και περισσότερο και σε συνδυασμό με την κινηματογραφική βιομηχανία που ανθίζει με γρήγορους ρυθμούς , έχει σαν αποτέλεσμα έναν τεράστιο αριθμό βίντεο κοινής χρήσης στο διαδίκτυο και μια πληθώρα κινηματογραφικών ταινιών, στα οποία έχει άμεση πρόσβαση μεγάλη μερίδα του πληθυσμού, συμπεριλαμβανομένων και διάφορων ευαίσθητων κοινωνικών ομάδων, παραδείγματος χάρη παιδιά και εφήβους. Η προστασία τέτοιων ατόμων αλλά και η επιθυμία γνώσης του περιεχομένου ενός βίντεο δημιούργησε την αναγκαιότητα ανάπτυξης αποτελεσματικών, αυτόματων ανιχνευτών βίας.Στην παρούσα διπλωματική παρουσιάζονται οι μέθοδοι που έχουν προταθεί στο συγκεκριμένο πεδίο. Στην συνέχεια, υιοθετείται μια εκ των μεθόδων και αναπτύσσεται αλγόριθμος, με σκοπό τη μελέτη της απόδοσης του. / In recent years, the popularity of the internet growing more and more.This results a huge number of video sharing on the internet and a plethora of films. A large portion of population has direct access in such videos,including sensitive and different social groups , for example children and adolescents . The protection of such persons and the desire knowing the content of a video, created the necessity to develop efficient , automated violence detectors.In this dissertation we present methods that have been proposed in this field . Then , we have adopted one of the methods and we have developed an algorithm in order to study its accuracy.