Examinando por Autor "Nieto, Marcos"

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    Ítem
    Face Reconstruction with structured light
    (SciTePress, 2011-03) Congote, John; Barandiaran, Iñigo; Barandiaran, Javier; Nieto, Marcos; Ruíz, Óscar; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    This article presents a methodology for reconstruction of 3D faces which is based on stereoscopic images of the scene using active and passive surface reconstruction -- A sequence of gray patterns is generated, which are projected onto the scene and their projection recorded by a pair of stereo cameras -- The images are rectified to make coincident their epipolar planes and so to generate a stereo map of the scene -- An algorithm for stereo matching is applied, whose result is a bijective mapping between subsets of the pixels of the images -- A particular connected subset of the images (e.g. the face) is selected by a segmentation algorithm -- The stereo mapping is applied to such a subset and enables the triangulation of the two image readings therefore rendering the (x;y; z) points of the face, which in turn allow the reconstruction of the triangular mesh of the face -- Since the surface might have holes, bilateral filters are applied to have the holes filled -- The algorithms are tested in real conditions and we evaluate their performance with virtual datasets -- Our results show a good reconstruction of the faces and an improvement of the results of passive systems
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    Ítem
    A new evaluation framework and image dataset for keypoint extraction and feature descriptor matching
    (2013-02) Barandiaran, Iñigo; Cortes, Camilo; Nieto, Marcos; Graña, Manuel; Ruíz, Óscar E.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    Key point extraction and description mechanisms play a crucial role in image matching, where several image points must be accurately identified to robustly estimate a transformation or to recognize an object or a scene -- New procedures for keypoint extraction and for feature description are continuously emerging -- In order to assess them accurately, normalized data and evaluation protocols are required -- In response to these needs, we present a (1) new evaluation framework that allow assessing the performance of the state-of-the-art feature point extraction and description mechanisms, (2) a new image dataset acquired under controlled affine and photometric transformations and (3) a testing image generator -- Our evaluation framework allows generating detailed curves about the performance of different approaches, providing a valuable insight about their behavior -- Also, it can be easily integrated in many research and development environments -- The contributions mentioned above are available on-line for the use of the scientific community