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BINK: Biological Binary Keypoint Descriptor

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BINK : Biological Binary Keypoint Descriptor. / Saleiro, Mário; Terzić, Kasim; Rodrigues, J. M. F.; du Buf, J. M. H.

In: BioSystems, Vol. 162, 12.2017, p. 147-156.

Research output: Contribution to journalArticlepeer-review

Harvard

Saleiro, M, Terzić, K, Rodrigues, JMF & du Buf, JMH 2017, 'BINK: Biological Binary Keypoint Descriptor', BioSystems, vol. 162, pp. 147-156. https://doi.org/10.1016/j.biosystems.2017.10.007

APA

Saleiro, M., Terzić, K., Rodrigues, J. M. F., & du Buf, J. M. H. (2017). BINK: Biological Binary Keypoint Descriptor. BioSystems, 162, 147-156. https://doi.org/10.1016/j.biosystems.2017.10.007

Vancouver

Saleiro M, Terzić K, Rodrigues JMF, du Buf JMH. BINK: Biological Binary Keypoint Descriptor. BioSystems. 2017 Dec;162:147-156. https://doi.org/10.1016/j.biosystems.2017.10.007

Author

Saleiro, Mário ; Terzić, Kasim ; Rodrigues, J. M. F. ; du Buf, J. M. H. / BINK : Biological Binary Keypoint Descriptor. In: BioSystems. 2017 ; Vol. 162. pp. 147-156.

Bibtex - Download

@article{62a1ecc6dcdd44eeb64d358920ca9c62,
title = "BINK: Biological Binary Keypoint Descriptor",
abstract = "Learning robust keypoint descriptors has become an active research area in the past decade. Matching local features is not only important for computational applications, but may also play an important role in early biological vision for disparity and motion processing. Although there were already some floating-point descriptors like SIFT and SURF that can yield high matching rates, the need for better and faster descriptors for real-time applications and embedded devices with low computational power led to the development of binary descriptors, which are usually much faster to compute and to match. Most of these descriptors are based on purely computational methods. The few descriptors that take some inspiration from biological systems are still lagging behind in terms of performance. In this paper, we propose a new biologically inspired binary keypoint descriptor: BINK. Built on responses of cortical V1 cells, it significantly outperforms the other biologically inspired descriptors. The new descriptor can be easily integrated with a V1-based keypoint detector that we previously developed for real-time applications.",
keywords = "Descriptor, Cortical cells, Keypoints, Applications, Bio-inspired",
author = "M{\'a}rio Saleiro and Kasim Terzi{\'c} and Rodrigues, {J. M. F.} and {du Buf}, {J. M. H.}",
note = "This work was supported by the EU under the FP-7 Grant ICT-2009.2.1-270247 NeuralDynamics, the Portuguese Foundation for Science and Technology (FCT), LARSyS [UID/EEA/50009/2013] and by FCT PhD grant to the 1st author SFRH/BD/71831/2010.",
year = "2017",
month = dec,
doi = "10.1016/j.biosystems.2017.10.007",
language = "English",
volume = "162",
pages = "147--156",
journal = "BioSystems",
issn = "0303-2647",
publisher = "Elsevier",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - BINK

T2 - Biological Binary Keypoint Descriptor

AU - Saleiro, Mário

AU - Terzić, Kasim

AU - Rodrigues, J. M. F.

AU - du Buf, J. M. H.

N1 - This work was supported by the EU under the FP-7 Grant ICT-2009.2.1-270247 NeuralDynamics, the Portuguese Foundation for Science and Technology (FCT), LARSyS [UID/EEA/50009/2013] and by FCT PhD grant to the 1st author SFRH/BD/71831/2010.

PY - 2017/12

Y1 - 2017/12

N2 - Learning robust keypoint descriptors has become an active research area in the past decade. Matching local features is not only important for computational applications, but may also play an important role in early biological vision for disparity and motion processing. Although there were already some floating-point descriptors like SIFT and SURF that can yield high matching rates, the need for better and faster descriptors for real-time applications and embedded devices with low computational power led to the development of binary descriptors, which are usually much faster to compute and to match. Most of these descriptors are based on purely computational methods. The few descriptors that take some inspiration from biological systems are still lagging behind in terms of performance. In this paper, we propose a new biologically inspired binary keypoint descriptor: BINK. Built on responses of cortical V1 cells, it significantly outperforms the other biologically inspired descriptors. The new descriptor can be easily integrated with a V1-based keypoint detector that we previously developed for real-time applications.

AB - Learning robust keypoint descriptors has become an active research area in the past decade. Matching local features is not only important for computational applications, but may also play an important role in early biological vision for disparity and motion processing. Although there were already some floating-point descriptors like SIFT and SURF that can yield high matching rates, the need for better and faster descriptors for real-time applications and embedded devices with low computational power led to the development of binary descriptors, which are usually much faster to compute and to match. Most of these descriptors are based on purely computational methods. The few descriptors that take some inspiration from biological systems are still lagging behind in terms of performance. In this paper, we propose a new biologically inspired binary keypoint descriptor: BINK. Built on responses of cortical V1 cells, it significantly outperforms the other biologically inspired descriptors. The new descriptor can be easily integrated with a V1-based keypoint detector that we previously developed for real-time applications.

KW - Descriptor

KW - Cortical cells

KW - Keypoints

KW - Applications

KW - Bio-inspired

U2 - 10.1016/j.biosystems.2017.10.007

DO - 10.1016/j.biosystems.2017.10.007

M3 - Article

VL - 162

SP - 147

EP - 156

JO - BioSystems

JF - BioSystems

SN - 0303-2647

ER -

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