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The Brightness Clustering Transformand Locally Contrasting Keypoints

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Computer Analysis of Images and Patterns (CAIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9256))

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Abstract

In recent years a new wave of feature descriptors has been presented to the computer vision community, ORB, BRISK and FREAK amongst others. These new descriptors allow reduced time and memory consumption on the processing and storage stages of tasks such as image matching or visual odometry, enabling real time applications. The problem is now the lack of fast interest point detectors with good repeatability to use with these new descriptors. We present a new blob-detector which can be implemented in real time and is faster than most of the currently used feature-detectors. The detection is achieved with an innovative non-deterministic low-level operator called the Brightness Clustering Transform (BCT). The BCT can be thought as a coarse-to-fine search through scale spaces for the true derivative of the image; it also mimics trans-saccadic perception of human vision. We call the new algorithm Locally Contrasting Keypoints detector or LOCKY. Showing good repeatability and robustness to image transformations included in the Oxford dataset, LOCKY is amongst the fastest affine-covariant feature detectors.

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Authors and Affiliations

  1. Electronics and Computer Sciences, University of Southampton, Southampton, England

    J. Lomeli-R & Mark S. Nixon

Authors
  1. J. Lomeli-R
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  2. Mark S. Nixon
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Correspondence to J. Lomeli-R .

Editor information

Editors and Affiliations

  1. University of Malta, Msida, Malta

    George Azzopardi

  2. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Lomeli-R, J., Nixon, M.S. (2015). The Brightness Clustering Transformand Locally Contrasting Keypoints. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_30

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  • DOI: https://doi.org/10.1007/978-3-319-23192-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23191-4

  • Online ISBN: 978-3-319-23192-1

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