Examinando por Autor "Nieto, M."

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    Face reconstruction with structured light
    (INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION, 2011-01-01) Congote, J.; Barandiaran, I.; Barandiaran, J.; Nieto, M.; Ruiz, O.; 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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    A new evaluation framework and image dataset for keypoint extraction and feature descriptor matching
    (2013-01-01) Barandiaran, I.; Cortes, C.; Nieto, M.; Graña, M.; Ruiz, O.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.