%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.11.15.00
%2 sid.inpe.br/sibgrapi/2021/09.11.15.00.11
%T Targets Detection Using Multiple Foveas
%D 2021
%A Medeiros, Petrucio Ricardo Tavares,
%A Gomes, Rafael Beserra,
%A Gonçalves, Luiz Marcos Garcia,
%@affiliation Department of Computer Engineering, Federal University of Rio Grande do Norte
%@affiliation Department of Computer Science and Applied Mathematics, Federal University of Rio Grande do Norte
%@affiliation Department of Computer Engineering, Federal University of Rio Grande do Norte
%E Paiva, Afonso,
%E Menotti, David,
%E Baranoski, Gladimir V. G.,
%E Proença, Hugo Pedro,
%E Junior, Antonio Lopes Apolinario,
%E Papa, João Paulo,
%E Pagliosa, Paulo,
%E dos Santos, Thiago Oliveira,
%E e Sá, Asla Medeiros,
%E da Silveira, Thiago Lopes Trugillo,
%E Brazil, Emilio Vital,
%E Ponti, Moacir A.,
%E Fernandes, Leandro A. F.,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K Multifoveation, target detection, gradient descent, maximum likelihood, trilateration and barycentric coordinates.
%X Target detection enables running a robotic task. However, their limited resources make large amount of data processing harder. Image foveation is an approach that can reduce processing demand by reducing the amount of data to be processed. However, as an important visual stimulli can be attenuated by this reduction, some strategy should be applied in order to keep/recover awareness of it. This work compares gradient descent (potential field), maximum likelihood, multilateration, trilateration, and barycentric coordinates to solve this problem in a multiple mobile foveas context. Our results demonstrate that the proposed methodology detects the target converging with an average euclidian distance of 51 pixels from the target's center position.
%@language en
%3 paper.pdf