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There are two main subjects in this thesis: the first one deals with the numerical simulation of shallow water equations, the other one is the resolution of some problems in image processingThe first part of this dissertation is devoted to the shallow waters. We propose a combined scheme which uses the Marquina's double flux decomposition (extended to the non homogeneous case) when adjacent states are not close and a single decomposition otherwise. This combined scheme satisfies the exact C property. Furthermore, we propose a special treatment of the numerical scheme at dry zones.The second subject is the digital simulation of the Day for Night (or American Night in Europe). The proposed algorithm simulates a night image coming from a day image and considers some aspects of night perception. In order to simulate the loss of visual acuity we introduce a partial differential equation that simulates the spatial summation principle of the photoreceptors in the retina.The gap restoration (inpainting) on surfaces is the object of the third part. For that, we propose some geometrical approaches based on the mean curvature. Then, we also use two interpolation methods: the resolution of the Laplace equation, and an Absolutely Minimizing Lipschitz Extension (AMLE). Finally, we solve the restoration problem of satellite images. The variational problem that we propose manages to do irregular to regular sampling, denoising, deconvolution and zoom at the same time., Los temas tratados en esta tesis son, por un lado, la simulación numérica de las ecuaciones de aguas someras ("shallow waters") y por otro, la resolución de algunos problemas de procesamiento de imágenes. En la primera parte de la tesis, dedicada a las aguas someras, proponemos un esquema combinado que usa la técnica de doble descomposición de flujos de Marquina (extendida al caso no homogéneo) cuando los dos estados adyacentes no están próximos y una única descomposición en caso contrario. El esquema combinado verifica la propiedad C exacta. Por otro lado, proponemos un tratamiento especial en las zonas secas.El segundo tema tratado es la simulación digital de la Noche Americana ("Day for Night"). El algoritmo propuesto simula una imagen nocturna a partir de una imagen diurna considerando varios aspectos de la percepción visual nocturna. Para simular la pérdida de agudeza visual se propone una ecuación en derivadas parciales que simula el principio de sumación espacial de los fotoreceptores situados en la retina.La restauración de agujeros ("inpainting") en superficies es objeto de la tercera parte. Para ello se proponen varios enfoques geométricos basados en la curvatura media. También se utilizan dos métodos de interpolación: la resolución de la ecuación de Laplace y el método AMLE (Absolutely Minimization Lipschitz Extension).Por último, tratamos la restauración de imágenes satelitales. El método propuesto consigue obtener una colección de muestras regulares a partir de un muestreo irregular, eliminando a la vez el ruido, deconvolucinando la imagen y haciendo un zoom.

The Project Framework is the detection and tracking of foreground objects in static and moving video sequences. The objective of a foreground segmentation and Tracking is to segment the scene in foreground objects and background and establish the temporal correspondence of the foreground objects. In this project we will focus on techniques that are based on a classification using a statistical model of the background and the foreground. For this reason, we will assume that the segmentation of the first frame is provided. Our objective will be to improve the models and define an appropriate updating of these models to reach a correct foreground-background segmentation minimizing False Negatives and False Positives. The tracking process makes the correspondence of the segmented objects with the objects being tracked from previous frames. Depending on the technique, the tracking can be clearly separated from the segmentation (when previous foreground information is not used for the segmentation) or can be implicit in the foreground segmentation (when we are using a priori information of the object).