Browsing by Author "Brito, Darlan Nunes de"

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    Análise comparativa de detectores e descritores de características locais em imagens no âmbito do problema de autocalibração de câmeras.
    (2016) Brito, Darlan Nunes de; Pádua, Flávio Luis Cardeal; Lopes, Aldo Peres Campos e; Dalip, Daniel Hasan
    Este trabalho apresenta uma análise comparativa de diferentes métodos do estado da arte para detecção e descrição de características locais em imagens, com o objetivo de solucionar de forma robusta e eficiente o problema de autocalibração de câmeras. Para atingir esse objetivo, é essencial a utilização de métodos detectores e descritores eficazes, uma vez que a correspondência robusta de características em um conjunto de imagens sucessivas sujeitas a uma ampla variedade de distorções afins e mudanças no ponto de vista 3D da cena, é crucial para a exatidão dos cálculos dos parâmetros da câmera. Muito embora diversos detectores e descritores têm sido propostos na literatura, seus impactos no processo de autocalibração de câmeras não foram ainda devidamente estudados. Nesse trabalho de análise comparativa, utilizam-se como critérios de qualidade da autocalibração os erros: epipolar, de reprojeção e reconstrução, bem como os tempos de execução dos métodos. Os resultados experimentais demonstram que detectores e descritores binários de características (ORB, BRISK e FREAK) e de ponto flutuante (SIFT e SURF) apresentam erros de reprojeção e reconstrução equivalentes. Considerando-se, porém, o menor custo computacional dos métodos binários, recomenda-se, fortemente, o uso destes em soluções de problemas de autocalibração de câmeras.
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    Evaluation of interest point matching methods for projective reconstruction of 3d scenes.
    (2016) Brito, Darlan Nunes de; Nunes, Cristiano Fraga Guimarães; Pádua, Flávio Luis Cardeal; Lacerda, Anisio Mendes
    This work evaluates the application of different state-of-the-art methods for interest point matching, aiming the robust and efficient projective reconstruction of three-dimensional scenes. Projective reconstruction refers to the computation of the structure of a scene from images taken with uncalibrated cameras. To achieve this goal, it is essential the usage of an effective point matching algorithm. Even though several point matching methods have been proposed in the literature, their impacts in the projective reconstruction task have not yet been carefully studied. Our evaluation uses as criterion the estimated epipolar, reprojection and reconstruction errors, as well as the running times of the algorithms. Specifically, we compare five different techniques: SIFT, SURF, ORB, BRISK and FREAK. Our experiments show that binary algorithms such as, ORB and BRISK, are so accurate as float point algorithms like SIFT and SURF, nevertheless, with smaller computational cost.
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    Temporal synchronization in mobile sensor networks using image sequence analysis.
    (2014) Brito, Darlan Nunes de; Pádua, Flávio Luis Cardeal; Pereira, Guilherme A. S.
    This paper addresses the problem of estimating the temporal synchronization inmobile sensors’ networks, by using image sequence analysis of their corresponding scene dynamics. Unlike existing methods, which are frequently based on adaptations of techniques originally designed for wired networks with static topologies, or even based on solutions specially designed for ad hoc wireless sensor networks, but that have a high energy consumption and a low scalability regarding the number of sensors, this work proposes a novel approach that reduces the problem of synchronizing a general number N of sensors to the robust estimation of a single line in RN+1. This line captures all temporal relations between the sensors and can be computed without any prior knowledge of these relations. It is assumed that (1) the network’s mobile sensors cross the field of view of a stationary calibrated camera that operates with constant frame rate and (2) the sensors trajectories are estimated with a limited error at a constant sampling rate, both in the world coordinate system and in the camera’s image plane. Experimental results with real-world and synthetic scenarios demonstrate that our method can be successfully used to determine the temporal alignment in mobile sensor networks.
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    Using geometric interval algebra modeling for improved three-dimensional camera calibration.
    (2019) Brito, Darlan Nunes de; Pádua, Flávio Luis Cardeal; Lopes, Aldo Peres Campos e
    This paper addresses the problem of estimating camera calibration parameters by using a novel method based on interval algebra. Unlike existing solutions, which usually apply real algebra, our method is capable of obtaining highly accurate parameters even in scenarios where the input data for camera calibration are severely corrupted by noise or no artificial calibration target can be introduced on the scene. We introduce some key concepts regarding the usage of interval algebra on projective space, which might be used by other computer vision methods. To demonstrate the robustness and effectiveness of our method, we present results for camera calibration with varying levels of noise on the input data of a world coordinate frame (standard deviation of up to 0.5 m) and their corresponding projections onto an image plane (standard deviation of up to 10 pixels), which are significantly larger than noise levels considered by state-of-the-art methods.