[en] In this paper, we present a FPGA based flexible self-adapting architecture for two features detectors, the Canny edge detector and the Harris corner detector, with reduced latency and memory requirements, and supporting variable resolution images. The new architecture uses neighbourhood extractors that can self-adapt its parameters on-the-fly and algorithm simplifications to reduce mathematical complexity, memory requirements and latency without losing reliability.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Da Cunha Possa, Paulo ; Université de Mons > Faculté Polytechnique > Electronique et Microélectronique
Mahmoudi, Sidi ; Université de Mons > Faculté Polytechnique > Informatique, Logiciel et Intelligence artificielle
Harb, Naim ; Université de Mons > Faculté Polytechnique > Electronique et Microélectronique
Valderrama, Carlos ; Université de Mons > Faculté Polytechnique > Electronique et Microélectronique
Language :
English
Title :
A New Self-Adapting Architecture for Feature Detection
Publication date :
29 August 2012
Journal title :
Lecture Notes in Computer Science
ISSN :
0302-9743
eISSN :
1611-3349
Publisher :
Springer, Heidelberg, Germany
Volume :
978-1-4673-2257-7
Issue :
2012(22) Oslo
Pages :
643 - 646
Peer reviewed :
Editorial reviewed
Research unit :
F109 - Electronique et Microélectronique
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
