FAB: Fast Angular Binary Descriptor for Matching Corner Points in Video Imagery

Image matching is a fundamental step in several computer vision applications where the requirement is fast, accurate, and robust matching of images in the presence of different transformations. Detection and more importantly description of low-level image features proved to be a more appropriate choice for this purpose, such as edges, corners, or blobs. Modern descriptors use binary values to store neighbourhood information of feature points for matching because binary descriptors are fast to compute and match. This paper proposes a descriptor called Fast Angular Binary (FAB) descriptor that illustrates the neighbourhood of a corner point using a binary vector. It is different from conventional descriptors because of selecting only the useful neighbourhood of corner point instead of the whole circular area of specific radius. The descriptor uses the angle of corner points to reduce the search space and increase the probability of finding an accurate match using binary descriptor. Experiments show that FAB descriptor’s performance is good, but the calculation and matching time is significantly less than BRIEF, the best known binary descriptor, and AMIE, a descriptor that uses entropy and average intensities of informative part of a corner point for the description

Evaluating the angular sensitivity of corner detectors

Several popular corner detectors were evaluated on imagery containing corners with a variety of internal angles. Even in a noise-free environment, differences in performance were found. A null hypothesis approach was taken in evaluating whether these performance differences were significant, taking into account correctly the size of the dataset and the number of discrepancies. It was found that some of these performance differences are statistically significant, allowing recommendations to be made regarding which detectors should be used when a problem has corners of known internal angles. © 2011 IEEE