A fast neural-dynamical approach to scale-invariant object detection

Terzic, Kasim; Lobato, D.; Saleiro, Mário; du Buf, J. M. H.

http://hdl.handle.net/10400.1/6998

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Abstract(s)

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.