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research
A fast neural-dynamical approach to scale-invariant object detection
Authors
D.G. Lowe
H. Bay
+6 more
K. Fukushima
K. Mikolajczyk
L. Itti
O. Lomp
P. Viola
T. Serre
Publication date
1 January 2014
Publisher
Springer Verlag
Doi
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Abstract
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
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Universidade do Algarve
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University of St. Andrews - Pure
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University of St. Andrews - Pure
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info:doi/10.1007%2F978-3-319-1...
Last time updated on 01/04/2019
Sapientia
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