BINK: Biological binary keypoint descriptor

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 so...

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Bibliographic Details
Published in:BioSystems 2017-12, Vol.162, p.147-156
Main Authors: Saleiro, Mário, Terzić, Kasim, Rodrigues, J.M.F., du Buf, J.M.H.
Format: Article
Language:English
Subjects:
Algorithms
Applications
Bio-inspired
Computational Biology - methods
Computer Simulation
Cortical cells
Descriptor
Humans
Keypoints
Pattern Recognition, Automated - methods
Reproducibility of Results
Software
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Summary: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.
ISSN:0303-2647
1872-8324
DOI:10.1016/j.biosystems.2017.10.007