An extension to the brightness clustering transform and locally contrasting keypoints

Abstract

The need for faster feature matching has left as a result a new set of feature descriptors 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, mitigating the implementation of more complex tasks. The problem is now the lack of fast interest point detectors with good repeatability to use with these new descriptors. A blob-detection algorithm was recently presented that uses an innovative non-deterministic low-level operator called the Brightness Clustering Transform (BCT) (Lomeli-R. and Nixon in The brightness clustering transform and locally contrasting keypoints. In CAIP. Springer, Berlin, pp [362][363][364][365][366][367][368][369][370][371][372][373] 2015). This algorithm is easy to implement and is faster than most of the currently used feature detectors. The BCT can be thought as a coarse-to-fine search through scale spaces for the true derivative of the image. The new algorithm is called Locally Contrasting Keypoints detector (LOCKY). Showing good robustness to image transformations included in the Oxford affine-covariant regions dataset, LOCKY is amongst the fastest affine-covariant feature detectors. In this paper, we present an extension of the BCT that detects larger structures maintaining timing and repeatability; this extension is called the BCT-S.

A Global Extraction Method of High Repeatability on Discretized Scale-Space Representations

“…Figure 5 in [11] and Figure 7 in [12] show results in the tests of LOCKY, where the sub-figures, (a)-(h) correspond to Figures 1a, 2a, 3a, 4a, 5a, 6a, 7 and 8a, respectively, in our work. Since LOCKY mainly aims to achieve faster computation than most of the currently used feature detectors, except the cases that viewpoints are greater than 40 in the Graffiti sequence, GPE shows apparently higher repeatability score than LOCKY.…”

“…Because of the techniques of integral images and box filters, Hessian feature detector in SURF is revised into Fast-Hessian detector, which can be computed more quickly than the former. Recently, Lomeli-R and Nixon presented a feature detector, the Locally Contrasting Keypoints detector (LOCKY) [11,12], which extracts blob keypoints directly from the Brightness Clustering Transform (BCT) of an image. The BCT also exploits the technique of integral images, and performs a fast search through different scale spaces by the strategy of coarse-to-fine.…”