Key-point Detection with Multi-layer Center-surround Inhibition

Foti Coleca; Sabrina Zîrnovean; Thomas Käster; Thomas Martinetz; Erhardt Barth

doi:10.5220/0004743103860393

Key-point Detection with Multi-layer Center-surround Inhibition

Foti Coleca, Sabrina Zîrnovean, Thomas Käster, Thomas Martinetz, Erhardt Barth

Institut für Neuro- und Bioinformatik

University POLITEHNICA of Bucureşti

Abstract

We present a biologically inspired algorithm for key-point detection based on multi-layer and nonlinear center-surround inhibition. A Bag-of-Visual-Words framework is used to evaluate the performance of the detector on the Oxford III-T Pet Dataset for pet recognition. The results demonstrate an increased performance of our algorithm compared to the SIFT key-point detector. We further improve the recognition rate by separately training codebooks for the ON- and OFF-type key points. The results show that our key-point detection algorithms outperform the SIFT detector by having a lower recognition-error rate over a whole range of different key-point densities. Randomly selected keypoints are also outperformed.

Originalsprache	Englisch
Titel	Proceedings of the 9th International Conference on Computer Vision Theory and Application
Redakteure/-innen	Sebastiano Battiato
Seitenumfang	8
Band	Vol. 1
Erscheinungsort	Lisbon, Portugal
Herausgeber (Verlag)	SciTePress
Erscheinungsdatum	01.01.2014
Auflage	1
Seiten	386-393
ISBN (Print)	9897580042, 978-9897580048
DOIs	https://doi.org/10.5220/0004743103860393
Publikationsstatus	Veröffentlicht - 01.01.2014
Veranstaltung	The International Conference on Computer Vision Theory and Applications - Lisbon / Lissabon, Portugal Dauer: 05.01.2014 → 08.01.2014 http://www.visapp.visigrapp.org/?y=2014

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Coleca, F., Zîrnovean, S., Käster, T., Martinetz, T., & Barth, E. (2014). Key-point Detection with Multi-layer Center-surround Inhibition. in S. Battiato (Hrsg.), Proceedings of the 9th International Conference on Computer Vision Theory and Application (1 Aufl., Band Vol. 1, S. 386-393). (INSTICC Conference Proceedings). SciTePress. https://doi.org/10.5220/0004743103860393

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title = "Key-point Detection with Multi-layer Center-surround Inhibition",

abstract = "We present a biologically inspired algorithm for key-point detection based on multi-layer and nonlinear center-surround inhibition. A Bag-of-Visual-Words framework is used to evaluate the performance of the detector on the Oxford III-T Pet Dataset for pet recognition. The results demonstrate an increased performance of our algorithm compared to the SIFT key-point detector. We further improve the recognition rate by separately training codebooks for the ON- and OFF-type key points. The results show that our key-point detection algorithms outperform the SIFT detector by having a lower recognition-error rate over a whole range of different key-point densities. Randomly selected keypoints are also outperformed.",

author = "Foti Coleca and Sabrina Z{\^i}rnovean and Thomas K{\"a}ster and Thomas Martinetz and Erhardt Barth",

year = "2014",

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doi = "10.5220/0004743103860393",

language = "English",

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pages = "386--393",

editor = "Sebastiano Battiato",

booktitle = "Proceedings of the 9th International Conference on Computer Vision Theory and Application",

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Coleca, F, Zîrnovean, S, Käster, T , Martinetz, T & Barth, E 2014, Key-point Detection with Multi-layer Center-surround Inhibition. in S Battiato (Hrsg.), Proceedings of the 9th International Conference on Computer Vision Theory and Application. 1 Aufl., Bd. Vol. 1, INSTICC Conference Proceedings, SciTePress, Lisbon, Portugal, S. 386-393, The International Conference on Computer Vision Theory and Applications , Lisbon / Lissabon, Portugal, 05.01.14. https://doi.org/10.5220/0004743103860393

Key-point Detection with Multi-layer Center-surround Inhibition. / Coleca, Foti; Zîrnovean, Sabrina; Käster, Thomas et al.
Proceedings of the 9th International Conference on Computer Vision Theory and Application. Hrsg. / Sebastiano Battiato. Band Vol. 1 1. Aufl. Lisbon, Portugal: SciTePress, 2014. S. 386-393 (INSTICC Conference Proceedings).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Key-point Detection with Multi-layer Center-surround Inhibition

AU - Coleca, Foti

AU - Zîrnovean, Sabrina

AU - Käster, Thomas

AU - Martinetz, Thomas

AU - Barth, Erhardt

PY - 2014/1/1

Y1 - 2014/1/1

N2 - We present a biologically inspired algorithm for key-point detection based on multi-layer and nonlinear center-surround inhibition. A Bag-of-Visual-Words framework is used to evaluate the performance of the detector on the Oxford III-T Pet Dataset for pet recognition. The results demonstrate an increased performance of our algorithm compared to the SIFT key-point detector. We further improve the recognition rate by separately training codebooks for the ON- and OFF-type key points. The results show that our key-point detection algorithms outperform the SIFT detector by having a lower recognition-error rate over a whole range of different key-point densities. Randomly selected keypoints are also outperformed.

AB - We present a biologically inspired algorithm for key-point detection based on multi-layer and nonlinear center-surround inhibition. A Bag-of-Visual-Words framework is used to evaluate the performance of the detector on the Oxford III-T Pet Dataset for pet recognition. The results demonstrate an increased performance of our algorithm compared to the SIFT key-point detector. We further improve the recognition rate by separately training codebooks for the ON- and OFF-type key points. The results show that our key-point detection algorithms outperform the SIFT detector by having a lower recognition-error rate over a whole range of different key-point densities. Randomly selected keypoints are also outperformed.

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M3 - Conference contribution

SN - 9897580042

SN - 978-9897580048

VL - Vol. 1

T3 - INSTICC Conference Proceedings

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BT - Proceedings of the 9th International Conference on Computer Vision Theory and Application

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CY - Lisbon, Portugal

T2 - The International Conference on Computer Vision Theory and Applications

Y2 - 5 January 2014 through 8 January 2014

ER -

Key-point Detection with Multi-layer Center-surround Inhibition

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