Repositório do Conhecimento Institucional do Centro Universitário FEI
 

Ciência da Computação

URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/342

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20192

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Periódico: search.filters.ispartof.Proceedings - 15th Workshop of Computer Vision, WVC 2019Assunto: search.filters.subject.SIFT

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
  • N/D
    Artigo de evento 1 Citação(ões) na Scopus
    A Bio-Inspired Methodology for Digital Imaging Forensic Detection
    (2019-09-05) SANTOS, G. L.; OLIVEIRA, G. R.; PRADO, F. F.; SERIKAKU, R.; SANTOS, R. M.; LOPES, G.; RODRIGUES, P. S.
    © 2019 IEEE.The increasing number of digital media users, as well as the development of multimedia platforms, such as smartphones and tablets, has also increased the number of users who professionally and fraudulently manipulate all types of digital media. This paper proposes a methodology based on an image processing pipeline to detect mainly copy-move type frauds, which are intended to hide or enlarge visual information. Our proposal has been tested in a Copy-Move Forgery database and the results were equal or better in performance than the state-of-art methods.
  • N/D
    Artigo de evento 0 Citação(ões) na Scopus
    A Bio-Inspired Strategy for 3D Surface Reconstruction of Unstructured Scenes Applied to Medical Images
    (2019-09-05) BOUZON, M.; ALBERTINI, G.; VIANA, G.; MEDEIROS, G.; Paulo Rodrigues
    © 2019 IEEE.The use of 3D reconstruction, along with immersive technologies, is a technique used in several areas of research and development. Currently, the most common strategy for performing this type of reconstruction is using a stereoscopic camera model. The problem worsens when the challenge involves unstructured scenes, which are scenes that have an ill-defined cognitive architecture. The present work proposes a methodology for 3D reconstruction of unstructured surfaces using monocular cameras. Thus, modern AI techniques, Computer Vision and Computer Graphics techniques have been applied to solve this problem. The experiments performed in this work can be concluded that the proposed method can reconstruct structured scenes with a hit rate between 63% and 68%, depending on the number of thresholds used in the segmentation, thus being superior to the classical method, where the extraction of points is done over the original image without any pre-processing.