Les Séries Temporelles d'Images Satellitaires (STIS), visant la même scène en évolution, sont très intéressantes parce qu'elles acquièrent conjointement des informations temporelles et spatiales. L'extraction de ces informations pour aider les experts dans l'interprétation des données satellitaires devient une nécessité impérieuse. Dans ce mémoire, nous exposons comment on peut adapter l'extraction de motifs séquentiels fréquents à ce contexte spatio-temporel dans le but d'identifier des ensembles de pixels connexes qui partagent la même évolution temporelle. La démarche originale est basée sur la conjonction de la contrainte de support avec différentes contraintes de connexité qui peuvent filtrer ou élaguer l'espace de recherche pour obtenir efficacement des motifs séquentiels fréquents groupés (MSFG) avec signification pour l'utilisateur. La méthode d'extraction proposée est non supervisée et basée sur le niveau pixel. Pour vérifier la généricité du concept de MSFG et la capacité de la méthode proposée d'offrir des résultats intéressants à partir des SITS, sont réalisées des expérimentations sur des données réelles optiques et radar. / The Satellite Image Time Series (SITS), aiming the same scene in evolution, are of high interest as they capture both spatial and temporal information. The extraction of this infor- mation to help the experts interpreting the satellite data becomes a stringent necessity. In this work, we expound how to adapt frequent sequential patterns extraction to this spatiotemporal context in order to identify sets of connected pixels sharing a same temporal evolution. The original approach is based on the conjunction of support constraint with different constraints based on pixel connectivity that can filter or prune the search space in order to efficiently ob- tain Grouped Frequent Sequential (GFS) patterns that are meaningful to the end user. The proposed extraction method is unsupervised and pixel level based. To verify the generality of GFS-pattern concept and the proposed method capability to offer interesting results from SITS, real data experiments on optical and radar data are presented.

Segmentação de objetos em imagens é um dos problemas mais fundamentais e desafiadores em processamento de imagem e visão computacional. O conhecimento de alto nível e específico do usuário é frequentemente requerido no processo de segmentação, devido à presença de fundos heterogêneos, objetos com bordas fracamente definidas, inomogeneidade de campo, ruído, artefatos, efeitos de volume parcial e seus efeitos conjuntos. Propriedades globais do objeto de interesse, tais como conexidade, restrições de forma e polaridade de borda, são conhecimentos prévios de alto nível úteis para a sua segmentação, permitindo a customização da segmentação para um objeto alvo. Nesse trabalho, apresentamos um novo método chamado Transformada Imagem-Floresta Orientada Conexa (COIFT, Connected Oriented Image Foresting Transform), que fornece soluções ótimas globais de acordo com uma medida de corte em grafo, incorporando a restrição de conexidade na Transformada Imagem-Floresta Orientada (OIFT, Oriented Image Foresting Transform), com o fim de garantir a geração de objetos conexos, bem como permitir o controle simultâneo da polaridade de borda. Enquanto o emprego de restrições de conexidade em outros arcabouços, tais como no algoritmo de corte-mínimo/fluxo-máximo (min-cut/max-flow), leva a um problema NP-difícil, a COIFT conserva o baixo custo computacional da OIFT. Experimentos mostram que a COIFT pode melhorar consideravelmente a segmentação de objetos com partes finas e alongadas, para o mesmo número de sementes em segmentação baseada em marcadores. / Object segmentation is one of the most fundamental and challenging problems in image processing and computer vision. The high-level and specific knowledge of the user is often required in the segmentation process, due to the presence of heterogeneous backgrounds, objects with poorly defined boundaries, field inhomogeneity, noise, artifacts, partial volume effects and their joint effects. Global properties of the object of interest, such as connectivity, shape constraints and boundary polarity, are useful high-level priors for its segmentation, allowing the customization of the segmentation for a given target object. In this work, we introduce a new method called Connected Oriented Image Foresting Transform (COIFT), which provides global optimal solutions according to a graph-cut measure in graphs, subject to the connectivity constraint in the Oriented Image Foresting Transform (OIFT), in order to ensure the generation of connected objects, as well as allowing the simultaneous control of the boundary polarity. While the use of connectivity constraints in other frameworks, such as in the min-cut/max-flow algorithm, leads to a NP-Hard problem, COIFT retains the low computational cost of OIFT. Experiments show that COIFT can considerably improve the segmentation of objects with thin and elongated parts, for the same number of seeds in segmentation based on markers.

The main body of this thesis consists of six appended papers. In the  first two, different  cooperative surveillance problems are considered. The second two consider different aspects of the trajectory planning problem, while the last two deal with observer design for mobile robotic and Euler-Lagrange systems respectively.In Papers A and B,  a combinatorial optimization based framework to cooperative surveillance missions using multiple Unmanned Ground Vehicles (UGVs) is proposed. In particular, Paper A  considers the the Minimum Time UGV Surveillance Problem (MTUSP) while Paper B treats the Connectivity Constrained UGV Surveillance Problem (CUSP). The minimum time formulation is the following. Given a set of surveillance UGVs and a polyhedral area, find waypoint-paths for all UGVs such that every point of the area is visible from  a point on a waypoint-path and such that the time for executing the search in parallel is minimized.  The connectivity constrained formulation  extends the MTUSP by additionally requiring the induced information graph to be  kept recurrently connected  at the time instants when the UGVs  perform the surveillance mission.  In these two papers, the NP-hardness of  both these problems are shown and decomposition techniques are proposed that allow us to find an approximative solution efficiently in an algorithmic manner.Paper C addresses the problem of designing a real time, high performance trajectory planner for an aerial vehicle that uses information about terrain and enemy threats, to fly low and avoid radar exposure on the way to a given target. The high-level framework augments Receding Horizon Control (RHC) with a graph based terminal cost that captures the global characteristics of the environment.  An important issue with RHC is to make sure that the greedy, short term optimization does not lead to long term problems, which in our case boils down to two things: not getting into situations where a collision is unavoidable, and making sure that the destination is actually reached. Hence, the main contribution of this paper is to present a trajectory planner with provable safety and task completion properties. Direct methods for trajectory optimization are traditionally based on a priori temporal discretization and collocation methods. In Paper D, the problem of adaptive node distribution is formulated as a constrained optimization problem, which is to be included in the underlying nonlinear mathematical programming problem. The benefits of utilizing the suggested method for  online  trajectory optimization are illustrated by a missile guidance example.In Paper E, the problem of active observer design for an important class of non-uniformly observable systems, namely mobile robotic systems, is considered. The set of feasible configurations and the set of output flow equivalent states are defined. It is shown that the inter-relation between these two sets may serve as the basis for design of active observers. The proposed observer design methodology is illustrated by considering a  unicycle robot model, equipped with a set of range-measuring sensors. Finally, in Paper F, a geometrically intrinsic observer for Euler-Lagrange systems is defined and analyzed. This observer is a generalization of the observer proposed by Aghannan and Rouchon. Their contractivity result is reproduced and complemented  by  a proof  that the region of contraction is infinitely thin. Moreover, assuming a priori bounds on the velocities, convergence of the observer is shown by means of Lyapunov's direct method in the case of configuration manifolds with constant curvature. / QC 20100622 / TAIS, AURES

Segmentação de objetos em imagens é um dos problemas mais fundamentais e desafiadores em processamento de imagem e visão computacional. O conhecimento de alto nível e específico do usuário é frequentemente requerido no processo de segmentação, devido à presença de fundos heterogêneos, objetos com bordas fracamente definidas, inomogeneidade de campo, ruído, artefatos, efeitos de volume parcial e seus efeitos conjuntos. Propriedades globais do objeto de interesse, tais como conexidade, restrições de forma e polaridade de borda, são conhecimentos prévios de alto nível úteis para a sua segmentação, permitindo a customização da segmentação para um objeto alvo. Nesse trabalho, apresentamos um novo método chamado Transformada Imagem-Floresta Orientada Conexa (COIFT, Connected Oriented Image Foresting Transform), que fornece soluções ótimas globais de acordo com uma medida de corte em grafo, incorporando a restrição de conexidade na Transformada Imagem-Floresta Orientada (OIFT, Oriented Image Foresting Transform), com o fim de garantir a geração de objetos conexos, bem como permitir o controle simultâneo da polaridade de borda. Enquanto o emprego de restrições de conexidade em outros arcabouços, tais como no algoritmo de corte-mínimo/fluxo-máximo (min-cut/max-flow), leva a um problema NP-difícil, a COIFT conserva o baixo custo computacional da OIFT. Experimentos mostram que a COIFT pode melhorar consideravelmente a segmentação de objetos com partes finas e alongadas, para o mesmo número de sementes em segmentação baseada em marcadores. / Object segmentation is one of the most fundamental and challenging problems in image processing and computer vision. The high-level and specific knowledge of the user is often required in the segmentation process, due to the presence of heterogeneous backgrounds, objects with poorly defined boundaries, field inhomogeneity, noise, artifacts, partial volume effects and their joint effects. Global properties of the object of interest, such as connectivity, shape constraints and boundary polarity, are useful high-level priors for its segmentation, allowing the customization of the segmentation for a given target object. In this work, we introduce a new method called Connected Oriented Image Foresting Transform (COIFT), which provides global optimal solutions according to a graph-cut measure in graphs, subject to the connectivity constraint in the Oriented Image Foresting Transform (OIFT), in order to ensure the generation of connected objects, as well as allowing the simultaneous control of the boundary polarity. While the use of connectivity constraints in other frameworks, such as in the min-cut/max-flow algorithm, leads to a NP-Hard problem, COIFT retains the low computational cost of OIFT. Experiments show that COIFT can considerably improve the segmentation of objects with thin and elongated parts, for the same number of seeds in segmentation based on markers.