Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/6998
Título: A fast neural-dynamical approach to scale-invariant object detection
Autor: Tersic, K.
Lobato, D.
Saleiro, Mário
du Buf, J. M. H.
Data: 2014
Editora: Springer Verlag
Resumo: We present a biologically-inspired method for object detection which is capable of online and one-shot learning of object appearance. We use a computationally efficient model of V1 keypoints to select object parts with the highest information content and model their surroundings by a simple binary descriptor based on responses of cortical cells. We feed these features into a dynamical neural network which binds compatible features together by employing a Bayesian criterion and a set of previously observed object views. We demonstrate the feasibility of our algorithm for cognitive robotic scenarios by evaluating detection performance on a dataset of common household items. © Springer International Publishing Switzerland 2014.
Peer review: yes
URI: http://hdl.handle.net/10400.1/6998
ISSN: 0302-9743
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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