The brightness clustering transform and locally contrasting keypoints

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 blobdetector 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-tofine 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.