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.

De nos jours les cartes d’utilisation/occupation du sol (USOS) à une échelle régionale sont habituellement générées à partir d’images satellitales de résolution modérée (entre 10 m et 30 m). Le National Land Cover Database aux États-Unis et le programme CORINE (Coordination of information on the environment) Land Cover en Europe, tous deux fondés sur les images LANDSAT, en sont des exemples représentatifs. Cependant ces cartes deviennent rapidement obsolètes, spécialement en environnement dynamique comme les megacités et les territoires métropolitains. Pour nombre d’applications, une mise à jour de ces cartes sur une base annuelle est requise. Depuis 2007, le USGS donne accès gratuitement à des images LANDSAT ortho-rectifiées. Des images archivées (depuis 1984) et des images acquises récemment sont disponibles. Sans aucun doute, une telle disponibilité d’images stimulera la recherche sur des méthodes et techniques rapides et efficaces pour un monitoring continue des changements des USOS à partir d’images à résolution moyenne. Cette recherche visait à évaluer le potentiel de telles images satellitales de résolution moyenne pour obtenir de l’information sur les changements des USOS à une échelle régionale dans le cas de la Communauté Métropolitaine de Montréal (CMM), une métropole nord-américaine typique. Les études précédentes ont démontré que les résultats de détection automatique des changements dépendent de plusieurs facteurs tels : 1) les caractéristiques des images (résolution spatiale, bandes spectrales, etc.); 2) la méthode même utilisée pour la détection automatique des changements; et 3) la complexité du milieu étudié. Dans le cas du milieu étudié, à l’exception du centre-ville et des artères commerciales, les utilisations du sol (industriel, commercial, résidentiel, etc.) sont bien délimitées. Ainsi cette étude s’est concentrée aux autres facteurs pouvant affecter les résultats, nommément, les caractéristiques des images et les méthodes de détection des changements. Nous avons utilisé des images TM/ETM+ de LANDSAT à 30 m de résolution spatiale et avec six bandes spectrales ainsi que des images VNIR-ASTER à 15 m de résolution spatiale et avec trois bandes spectrales afin d’évaluer l’impact des caractéristiques des images sur les résultats de détection des changements. En ce qui a trait à la méthode de détection des changements, nous avons décidé de comparer deux types de techniques automatiques : (1) techniques fournissant des informations principalement sur la localisation des changements et (2)techniques fournissant des informations à la fois sur la localisation des changements et sur les types de changement (classes « de-à »). Les principales conclusions de cette recherche sont les suivantes : Les techniques de détection de changement telles les différences d’image ou l’analyse des vecteurs de changements appliqués aux images multi-temporelles LANDSAT fournissent une image exacte des lieux où un changement est survenu d’une façon rapide et efficace. Elles peuvent donc être intégrées dans un système de monitoring continu à des fins d’évaluation rapide du volume des changements. Les cartes des changements peuvent aussi servir de guide pour l’acquisition d’images de haute résolution spatiale si l’identification détaillée du type de changement est nécessaire. Les techniques de détection de changement telles l’analyse en composantes principales et la comparaison post-classification appliquées aux images multi-temporelles LANDSAT fournissent une image relativement exacte de classes “de-à” mais à un niveau thématique très général (par exemple, bâti à espace vert et vice-versa, boisés à sol nu et vice-versa, etc.). Les images ASTER-VNIR avec une meilleure résolution spatiale mais avec moins de bandes spectrales que LANDSAT n’offrent pas un niveau thématique plus détaillé (par exemple, boisés à espace commercial ou industriel). Les résultats indiquent que la recherche future sur la détection des changements en milieu urbain devrait se concentrer aux changements du couvert végétal puisque les images à résolution moyenne sont très sensibles aux changements de ce type de couvert. Les cartes indiquant la localisation et le type des changements du couvert végétal sont en soi très utiles pour des applications comme le monitoring environnemental ou l’hydrologie urbaine. Elles peuvent aussi servir comme des indicateurs des changements de l’utilisation du sol. De techniques telles l’analyse des vecteurs de changement ou les indices de végétation son employées à cette fin. / Nowadays land use/land cover maps at regional scale are commonly generated with satellite data of medium spatial resolution (between 10 m and 30m). The National Land Cover Database (NLCD) in the United States and the Coordination of Information on the Environment (CORINE) Land Cover program in Europe, both based on LANDSAT images, are two typical examples. However, these maps become rapidly obsolete, especially in highly dynamic areas such as mega cities and metropolitan areas. In many applications, such as to monitor the water quality affected by the Land use/Land cover (LULC) change, the spread of invasive species, policy making for city managers, annual updating of LULC maps is required. Since 2007, the USGS offers access to ortho-rectified LANDSAT imagery free of charge. Both archived (since 1984) and recently acquired images are available. Without doubt, such data availability will stimulate the research on fast and cost effective methods and techniques for “continuous” regional land cover/use map updating using medium resolution satellite imagery. The objective of this research was to evaluate the potential of such medium resolution satellite imagery for providing information on changes useful for the continuous updating of LULC maps at a regional scale in the case of the Montreal Metropolitan Community (MMC) area, a typical North American metropolis. Previous studies have demonstrated that many factors could affect the results of automatic change detection such as: (1) the characteristics of the images (spatial resolution, spectral bands, etc.); (2) the method itself used to automatically detect changes; and (3) the complexity of the landscape. In the study site except for the Central Business District (CBD) and some commercial streets, land uses (industrial, commercial, residential, etc.) are well delimited. Thus this study was focused on the other factors affecting change detection results, namely, the characteristics of the images and the method of change detection. We used 6 spectral bands of LANDSAT TM/ETM+ with 30 m spatial resolution and 3 spectral bands of ASTER-VNIR with 15 m spatial resolution to evaluate the impact of image characteristics on change detection. Concerning the change detection method, we decided to compare two types of automatic techniques: (1) techniques providing information principally on the location of changed areas,and (2) techniques providing information on both the location of changed areas and the type of changes ("from-to" classes). The main conclusions of this research are as follows: Change detection techniques such as image differencing or change vector analysis applied to LANDSAT multi-temporal imagery provide an accurate picture of changed areas in a fast and efficient manner. They can thus be integrated in a continuous monitoring system for a rapid evaluation of the volume of changes. The produced maps could be helpful to guide the acquisition of high spatial resolution imagery if a detailed identification of the type of changes is required. Change detection techniques such as principal component analysis and post-classification comparison applied to LANDSAT multi-temporal imagery could provide a relatively accurate picture of “from-to” classes but at a very general thematic level (for example, built-up to green space and vice-versa, forest lands to bare soil and vice-versa, etc.). ASTER images with better spatial resolution but with less spectral bands than LANDSAT images do not provide more detailed thematic information (for example forest land to commercial or industrial areas). The results indicate that future research should be focused on the detection of changes in the vegetation cover as medium resolution imagery is highly sensitive to this type of surface cover. Maps indicating the location and the type of changes in vegetation cover are in itself very useful for various applications, such as environmental monitoring or urban hydrology, and can be used as indicators on land use changes. Techniques such as change vector analysis or vegetation indices could be used to this end.

Many mobile robot applications require robots to act safely and intelligently in complex unfamiliarenvironments with little structure and limited or unavailable human supervision. As arobot is forced to operate in an environment that it was not engineered or trained for, various aspectsof its performance will inevitably degrade. Roboticists equip robots with powerful sensorsand data sources to deal with uncertainty, only to discover that the robots are able to make onlyminimal use of this data and still find themselves in trouble. Similarly, roboticists develop andtrain their robots in representative areas, only to discover that they encounter new situations thatare not in their experience base. Small problems resulting in mildly sub-optimal performance areoften tolerable, but major failures resulting in vehicle loss or compromised human safety are not.This thesis presents a series of online algorithms to enable a mobile robot to better deal withuncertainty in unfamiliar domains in order to improve its navigational abilities, better utilizeavailable data and resources and reduce risk to the vehicle. We validate these algorithms throughextensive testing onboard large mobile robot systems and argue how such approaches can increasethe reliability and robustness of mobile robots, bringing them closer to the capabilitiesrequired for many real-world applications.