Ponencia
Spiking Hough for Shape Recognition
Autor/es | Negri, Pablo
Serrano Gotarredona, María Teresa Linares Barranco, Bernabé |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2017 |
Fecha de depósito | 2020-10-30 |
Publicado en |
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ISBN/ISSN | 978-3-319-75192-4 0302-9743 |
Resumen | The paper implements a spiking neural model methodology
inspired on the Hough Transform. On-line event-driven spikes from
Dynamic Vision Sensors are evaluated to characterize and recognize the
shape of Poker signs. The ... The paper implements a spiking neural model methodology inspired on the Hough Transform. On-line event-driven spikes from Dynamic Vision Sensors are evaluated to characterize and recognize the shape of Poker signs. The multi-class system, referred as Spiking Hough, shows the good performance on the public POKER-DVS dataset. |
Agencias financiadoras | European Union (UE) European Union (UE) Ministerio de Economía y Competitividad (MINECO). España |
Identificador del proyecto | H2020 grant 644096
H2020 grant 687299 TEC2015-63884-C2-1-P |
Cita | Negri, P., Serrano Gotarredona, M.T. y Linares Barranco, B. (2017). Spiking Hough for Shape Recognition. En CIARP 2017: 22nd Iberoamerican Congress on Pattern Recognition (425-432), Valparaíso, Chile: Springer. |
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