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

Research output: Contribution to journalArticlepeer-review

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A fast neural-dynamical approach to scale-invariant object detection. / Terzić, Kasim; Lobato, David; Saleiro, Màrio; Du Buf, J. M.H.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8834, 01.01.2014, p. 511-518.

Research output: Contribution to journalArticlepeer-review

Harvard

Terzić, K, Lobato, D, Saleiro, M & Du Buf, JMH 2014, 'A fast neural-dynamical approach to scale-invariant object detection', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8834, pp. 511-518.

APA

Terzić, K., Lobato, D., Saleiro, M., & Du Buf, J. M. H. (2014). A fast neural-dynamical approach to scale-invariant object detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8834, 511-518.

Vancouver

Terzić K, Lobato D, Saleiro M, Du Buf JMH. A fast neural-dynamical approach to scale-invariant object detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014 Jan 1;8834:511-518.

Author

Terzić, Kasim ; Lobato, David ; Saleiro, Màrio ; Du Buf, J. M.H. / A fast neural-dynamical approach to scale-invariant object detection. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014 ; Vol. 8834. pp. 511-518.

Bibtex - Download

@article{3254c6c6be4a4793a329314c9b5cbd6f,
title = "A fast neural-dynamical approach to scale-invariant object detection",
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.",
author = "Kasim Terzi{\'c} and David Lobato and M{\`a}rio Saleiro and {Du Buf}, {J. M.H.}",
year = "2014",
month = jan,
day = "1",
language = "English",
volume = "8834",
pages = "511--518",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer-Verlag",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - A fast neural-dynamical approach to scale-invariant object detection

AU - Terzić, Kasim

AU - Lobato, David

AU - Saleiro, Màrio

AU - Du Buf, J. M.H.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84921633140&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84921633140

VL - 8834

SP - 511

EP - 518

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -

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