6 resultados para Skew divergence. Segmentation. Clustering. Textural color image

em Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España


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[EN] In this work, we present a new model for a dense disparity estimation and the 3-D geometry reconstruction using a color image stereo pair. First, we present a brief introduction to the 3-D Geometry of a camera system. Next, we propose a new model for the disparity estimation based on an energy functional. We look for the local minima of the energy using the associate Euler-Langrage partial differential equations. This model is a generalization to color image of the model developed in, with some changes in the strategy to avoid the irrelevant local minima. We present some numerical experiences of 3-D reconstruction, using this method some real stereo pairs.

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[ES] En este trabajo proponemos un nuevo modelo para el cálculo de la disparidad y la reconstrucción 3-D a partir de un sistema estéreo compuesto por 2 imágenes en color. Proponemos un nuevo modelo para el cálculo de la disparidad basado en un criterio de energía. Para calcular los mínimos de este funcional de energía utilizamos la ecuación en derivadas parciales de Euler-Langrage asociada. Este modelo es una extensión a imágenes color del modelo desarrollado en "L. Alvarez, R. Deriche, J. Sánchez and J. Weickert, Dense disparity map estimation respecting image discontinuities : A PDE and Scale-Space Based Approach. INRIA Rapport de Recherche Nº 3874, 2000". Con algunos cambios en la estrategia parav evitar caer en mínimos locales de la energía. Por último presentamos algunas experiencias numéricas de la reconstrucción 3-D obtenida con este método en algunos pares estéreos de imágenes reales.

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We analyse the influence of colour information in optical flow methods. Typically, most of these techniques compute their solutions using grayscale intensities due to its simplicity and faster processing, ignoring the colour features. However, the current processing systems have minimized their computational cost and, on the other hand, it is reasonable to assume that a colour image offers more details from the scene which should facilitate finding better flow fields. The aim of this work is to determine if a multi-channel approach supposes a quite enough improvement to justify its use. In order to address this evaluation, we use a multi-channel implementation of a well-known TV-L1 method. Furthermore, we review the state-of-the-art in colour optical flow methods. In the experiments, we study various solutions using grayscale and RGB images from recent evaluation datasets to verify the colour benefits in motion estimation.

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Permitida la difusión del código bajo los términos de la licencia BSD de tres cláusulas.

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[EN]During the last decade, researchers have verified that clothing can provide information for gender recognition. However, before extracting features, it is necessary to segment the clothing region. We introduce a new clothes segmentation method based on the application of the GrabCut technique over a trixel mesh, obtaining very promising results for a close to real time system. Finally, the clothing features are combined with facial and head context information to outperform previous results in gender recognition with a public database.

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[EN]In this paper, a clothes segmentation method for fashion parsing is described. This method does not rely in a previous pose estimation but people segmentation. Therefore, novel and classic segmentation techniques have been considered and improved in order to achieve accurate people segmentation. Unlike other methods described in the literature, the output is the bounding box and the predominant color of the different clothes and not a pixel level segmentation. The proposal is based on dividing the person area into an initial fixed number of stripes, that are later fused according to similar color distribution. To assess the quality of the proposed method the experiments are carried out with the Fashionista dataset that is widely used in the fashion parsing community.