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

Institute of Neuro- and Bioinformatics

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.

Original language	English
Title of host publication	Proceedings of the 9th International Conference on Computer Vision Theory and Application
Editors	Sebastiano Battiato
Number of pages	8
Volume	Vol. 1
Place of Publication	Lisbon, Portugal
Publisher	SciTePress
Publication date	01.01.2014
Edition	1
Pages	386-393
ISBN (Print)	9897580042, 978-9897580048
DOIs	https://doi.org/10.5220/0004743103860393
Publication status	Published - 01.01.2014
Event	The International Conference on Computer Vision Theory and Applications - Lisbon / Lissabon, Portugal Duration: 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 (Ed.), Proceedings of the 9th International Conference on Computer Vision Theory and Application (1 ed., Vol. Vol. 1, pp. 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",

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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 (ed.), Proceedings of the 9th International Conference on Computer Vision Theory and Application. 1 edn, vol. Vol. 1, INSTICC Conference Proceedings, SciTePress, Lisbon, Portugal, pp. 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. ed. / Sebastiano Battiato. Vol. Vol. 1 1. ed. Lisbon, Portugal: SciTePress, 2014. p. 386-393 (INSTICC Conference Proceedings).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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

AU - Coleca, Foti

AU - Zîrnovean, Sabrina

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

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ER -

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

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