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The Brightness Clustering Transformand Locally Contrasting Keypoints

  • J. Lomeli-REmail author
  • Mark S. Nixon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)

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.

Keywords

Integral Image Image Transformation Visual Odometry Interest Point Detector Maximally Stable Extremal Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Electronics and Computer SciencesUniversity of SouthamptonSouthamptonEngland

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