Listar por autor "Mures, Omar A."

Mostrando ítems 1-5 de 5

    • An Application of Fish Detection Based on Eye Search with Artificial Vision and Artificial Neural Networks 

      Rico-Díaz, Ángel-José; Rabuñal, Juan R.; Gestal, M.; Mures, Omar A.; Puertas, Jerónimo (MDPI AG, 2020-10-27)
      [Abstract] A fish can be detected by means of artificial vision techniques, without human intervention or handling the fish. This work presents an application for detecting moving fish in water by artificial vision based ...

    • Assisted surface redesign by perturbing its point cloud representation 

      Pazos Pérez, Rafael Iván; Carballal, Adrián; Rabuñal, Juan R.; Mures, Omar A.; García-Vidaurrázaga, María D. (IET, 2018-04-09)
      [Abstract] This research study explores the use of point clouds for design geometrically complex surfaces based on genetic morphogenesis. To this end, a point-based genetic algorithm and the use of massive unstructured ...

    • In-Transit Molecular Dynamics Analysis with Apache Flink 

      Zamúz, Henrique C.; Raffin, Bruno; Mures, Omar A.; Padrón, Emilio J. (Association for Computing Machinery (ACM), 2018-11)
      [Abstract] In this paper, an on-line parallel analytics framework is proposed to process and store in transit all the data being generated by a Molecular Dynamics (MD) simulation run using staging nodes in the same cluster ...

    • Predicting vertical urban growth using genetic evolutionary algorithms in Tokyo’s minato ward 

      Pazos Pérez, Rafael Iván; Carballal, Adrián; Rabuñal, Juan R.; Mures, Omar A.; García-Vidaurrázaga, María D. (American Society of Civil Engineers, 2018-03)
      [Abstract] This article explores the use of evolutionary genetic algorithms to predict scenarios of urban vertical growth in large urban centers. Tokyo’s Minato Ward is used as a case study because it has been one of the ...

    • Understanding the Influence of Rendering Parameters in Synthetic Datasets for Neural Semantic Segmentation Tasks 

      Silva, Manuel; Mures, Omar A.; Seoane, Antonio; Iglesias-Guitian, Jose A. (Universidade da Coruña, Servizo de Publicacións, 2023)
      [Abstract] Deep neural networks are well known for demanding large amounts of training data, motivating the appearance of multiple synthetic datasets covering multiple domains. However, synthetic datasets have not yet ...