Examinando por Autor "Graña, Manuel"

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    Ítem
    Evaluation of interest point detectors for image information extraction
    (2012) Barandiaran, Iñigo; Goenetxea, Jon; Congote, John; Graña, Manuel; Ruíz, Oscar; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    Interest points extraction and matching is a common task in many computer vision based application, which are used in different domains, such as 3D reconstruction, object recognition, or tracking -- We present an evaluation of current state of the art about interest point extraction algorithms to measure several parameters, such as detection quality, invariance to rotation and scale transformation, and computational efficiency
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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
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    Ítem
    Tuning of Adaptive Weight Depth Map Generation Algorithms Exploratory Data Analysis and Design of Computer Experiments (DOCE)
    (Springer Verlag, 2013-09) Acosta, Diego; Congote, John; Barandiaran, Iñigo; Ruíz, Óscar; Hoyos, Alejandro; Graña, Manuel; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    In depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on un planned experiments -- Algorithms' performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury's standards -- This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative inf uence of the parameters and to fine-tune them based on the number of bad pixels -- The implemented methodology improves the performance of adaptive weight based dense depth map algorithms -- As a result, the algorithm improves from 16.78% to 14.48% bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78% to 13%