Performance Analysis of Keypoint Detectors and Binary Descriptors Under Varying Degrees of Photometric and Geometric Transformations.

Detecting image correspondences by feature matching forms the basis of numerous computer vision applications. Several detectors and descriptors have been presented in the past, addressing the efficient generation of features from interest points (keypoints) in an image. In this paper, we investigate eight binary descriptors (AKAZE, BoostDesc, BRIEF, BRISK, FREAK, LATCH, LUCID, and ORB) and eight interest point detector (AGAST, AKAZE, BRISK, FAST, HarrisLapalce, KAZE, ORB, and StarDetector). We have decoupled the detection and description phase to analyze the interest point detectors and then evaluate the performance of the pairwise combination of different detectors and descriptors. We conducted experiments on a standard dataset and analyzed the comparative performance of each method under different image transformations. We observed that: (1) the FAST, AGAST, ORB detectors were faster and detected more keypoints, (2) the AKAZE and KAZE detectors performed better under photometric changes while ORB was more robust against geometric changes, (3) in general, descriptors performed better when paired with the KAZE and AKAZE detectors, (4) the BRIEF, LUCID, ORB descriptors were relatively faster, and (5) none of the descriptors did particularly well under geometric transformations, only BRISK, FREAK, and AKAZE showed reasonable resiliency.