14 resultados para Morán, Francisco

em Universidad Politécnica de Madrid


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Death of Dr Francisco Javier Ayala-Carcedo, Spanish lNHIGEO Member, at Burgos, Spain, 28 November 2004

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Hojas Kilométricas (Kilometric Sheets). Specifically, the study focuses on those sheets referring to the city centre and surrounding area of the Royal Site of Aranjuez, a town in the south of the Province of Madrid. The aim of this study is to restore the actual size and measurements of scanned images of the Hojas Kilométricas. This would allow us, among other things, to reestablish both the format and scale of the original plans. To achieve this goal it is necessary to rectify and then georeference these images, i.e. assign them a geographic reference system. This procedure is essential in the overlaying and comparison of the Hojas Kilométricas of the Royal Site with other historical cartography as well as other sources related to the same area from different time periods. Subsequent research would allow us, for example, to reconstruct the time-evolution of the urban area, to spot new construction and to pinpoint the locations of any altered or missing buildings or architectural features. In addition, this would allow us to develop and integrate databases for GIS models applicable to the management of our cultural heritage.

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Zernike polynomials are a well known set of functions that find many applications in image or pattern characterization because they allow to construct shape descriptors that are invariant against translations, rotations or scale changes. The concepts behind them can be extended to higher dimension spaces, making them also fit to describe volumetric data. They have been less used than their properties might suggest due to their high computational cost. We present a parallel implementation of 3D Zernike moments analysis, written in C with CUDA extensions, which makes it practical to employ Zernike descriptors in interactive applications, yielding a performance of several frames per second in voxel datasets about 2003 in size. In our contribution, we describe the challenges of implementing 3D Zernike analysis in a general-purpose GPU. These include how to deal with numerical inaccuracies, due to the high precision demands of the algorithm, or how to deal with the high volume of input data so that it does not become a bottleneck for the system.

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We introduce a simple and innovative method to compare any two texture maps, regardless of their sizes, aspect ratios, or even masks, as long as they are both meant to be mapped onto the same 3D mesh. Our system is based on a zero-distortion 3D mesh unwrapping technique which compares two new adapted texture atlases with the same mask but different texel colors, and whose every texel covers the same area in 3D. Once these adapted atlases are created, we measure their difference with ITEM-RMSE, a slightly modified version of the standard RMSE defined for images. ITEM-RMSE is more meaningful and reliable than RMSE because it only takes into account the texels inside the mask, since they are the only ones that will actually be used during rendering. Our method is not only very useful to compare the space efficiency of different texture atlas generation algorithms, but also to quantify texture loss in compression schemes for multi-resolution textured 3D meshes.

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We introduce an innovative, semi-automatic method to transform low resolution facial meshes into high definition ones, based on the tailoring of a generic, neutral human head model, designed by an artist, to fit the facial features of a specific person. To determine these facial features we need to select a set of "control points" (corners of eyes, lips, etc.) in at least two photographs of the subject's face. The neutral head mesh is then automatically reshaped according to the relation between the control points in the original subject's mesh through a set of transformation pyramids. The last step consists in merging both meshes and filling the gaps that appear in the previous process. This algorithm avoids the use of expensive and complicated technologies to obtain depth maps, which also need to be meshed later.

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We present an innovative system to encode and transmit textured multi-resolution 3D meshes in a progressive way, with no need to send several texture images, one for each mesh LOD (Level Of Detail). All texture LODs are created from the finest one (associated to the finest mesh), but can be re- constructed progressively from the coarsest thanks to refinement images calculated in the encoding process, and transmitted only if needed. This allows us to adjust the LOD/quality of both 3D mesh and texture according to the rendering power of the device that will display them, and to the network capacity. Additionally, we achieve big savings in data transmission by avoiding altogether texture coordinates, which are generated automatically thanks to an unwrapping system agreed upon by both encoder and decoder.

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El objetivo de este proyecto consiste en verificar si existen métodos de ajuste que nos permitan corregir observaciones que llevan introducidas un cierto error grosero. Este error puede ser cometido por el operador al realizar la toma de datos. Cuando se realiza el ajuste por el método clásico de una serie de observaciones que componen una red y éstas llevan incluidas un error grosero, el resultado del ajuste es totalmente inválido, ya que este método tiende a repartir los errores cometidos entre todas las observaciones dando como resultado residuos muy elevados. Normalmente, ante este tipo de casos, el usuario responde invalidando las observaciones con mayor residuo y procediendo a realizar el ajuste sin contar con las observaciones que se anularon. Para llevar a cabo este proceso, debe haber redundancia en la toma de datos, ya que si no se dispusiera de la misma, no podrían eliminarse observaciones. Nuestro objetivo real es llevar a cabo un estudio en el que demostrar que el método de ajuste por técnicas de estimación robusta tiene una capacidad infinitamente superior a los mínimos cuadrados en la detección de errores groseros en observaciones, permitiendo de esta forma al usuario corregir dicho error e incluir esa observación en el ajuste. Este método de ajuste (estimación robusta) comenzó a emplearse en el campo de la fotogrametría. Es por ello que nuestra investigación irá encaminada al empleo de los estimadores robustos en aplicaciones topográficas en las que todavía no han sido utilizados. Se decidió iniciar este estudio porque era un proyecto innovador que nunca había sido realizado y las conclusiones del mismo han sido muy favorables, como se puede comprobar más adelante.

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There is an increasing need of easy and affordable technologies to automatically generate virtual 3D models from their real counterparts. In particular, 3D human reconstruction has driven the creation of many clever techniques, most of them based on the visual hull (VH) concept. Such techniques do not require expensive hardware; however, they tend to yield 3D humanoids with realistic bodies but mediocre faces, since VH cannot handle concavities. On the other hand, structured light projectors allow to capture very accurate depth data, and thus to reconstruct realistic faces, but they are too expensive to use several of them. We have developed a technique to merge a VH-based 3D mesh of a reconstructed humanoid and the depth data of its face, captured by a single structured light projector. By combining the advantages of both systems in a simple setting, we are able to reconstruct realistic 3D human models with believable faces.

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A spatial-color-based non-parametric background-foreground modeling strategy in a GPGPU by using CUDA is proposed. This strategy is suitable for augmented-reality applications, providing real-time high-quality results in a great variety of scenarios.

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Markerless video-based human pose estimation algorithms face a high-dimensional problem that is frequently broken down into several lower-dimensional ones by estimating the pose of each limb separately. However, in order to do so they need to reliably locate the torso, for which they typically rely on time coherence and tracking algorithms. Their losing track usually results in catastrophic failure of the process, requiring human intervention and thus precluding their usage in real-time applications. We propose a very fast rough pose estimation scheme based on global shape descriptors built on 3D Zernike moments. Using an articulated model that we configure in many poses, a large database of descriptor/pose pairs can be computed off-line. Thus, the only steps that must be done on-line are the extraction of the descriptors for each input volume and a search against the database to get the most likely poses. While the result of such process is not a fine pose estimation, it can be useful to help more sophisticated algorithms to regain track or make more educated guesses when creating new particles in particle-filter-based tracking schemes. We have achieved a performance of about ten fps on a single computer using a database of about one million entries.

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The last generation of consumer electronic devices is endowed with Augmented Reality (AR) tools. These tools require moving object detection strategies, which should be fast and efficient, to carry out higher level object analysis tasks. We propose a lightweight spatio-temporal-based non-parametric background-foreground modeling strategy in a General Purpose Graphics Processing Unit (GPGPU), which provides real-time high-quality results in a great variety of scenarios and is suitable for AR applications.

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We propose a new method to automatically refine a facial disparity map obtained with standard cameras and under conventional illumination conditions by using a smart combination of traditional computer vision and 3D graphics techniques. Our system inputs two stereo images acquired with standard (calibrated) cameras and uses dense disparity estimation strategies to obtain a coarse initial disparity map, and SIFT to detect and match several feature points in the subjects face. We then use these points as anchors to modify the disparity in the facial area by building a Delaunay triangulation of their convex hull and interpolating their disparity values inside each triangle. We thus obtain a refined disparity map providing a much more accurate representation of the the subjects facial features. This refined facial disparity map may be easily transformed, through the camera calibration parameters, into a depth map to be used, also automatically, to improve the facial mesh of a 3D avatar to match the subjects real human features.

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El artículo analiza el proceso de rehabilitación de los barrios de Bilbao la Vieja, San Francisco y Zabala entre los años 1990 y 2014. El caso es destacable en el panorama de la rehabilitación urbana en España por ser una experiencia de largo recorrido con intervenciones de rehabilitación edificatoria, de renovación urbana y de regeneración urbana, entendidas como multidimensionales desde los primeros planes, y cuyo modelo de gestión, bajo el liderazgo y compromiso institucional, ha ido adaptándose a las circunstancias cambiantes y al desarrollo del propio proceso.