TY - JOUR
AU1 - Khan, Nabeel
AU2 - McCane, Brendan
AU3 - Mills, Steven
AB - Independent evaluation of the performance of feature descriptors is an important part of the process of developing better computer vision systems. In this paper, we compare the performance of several state-of-the art image descriptors including several recent binary descriptors. We test the descriptors on an image recognition application and a feature matching application. Our study includes several recently proposed methods and, despite claims to the contrary, we find that SIFT is still the most accurate performer in both application settings. We also find that general purpose binary descriptors are not ideal for image recognition applications but perform adequately in a feature matching application.
TI - Better than SIFT?
JF - Machine Vision and Applications
DO - 10.1007/s00138-015-0689-7
DA - 2015-05-17
UR - https://www.deepdyve.com/lp/springer-journals/better-than-sift-600I2vGVEE
SP - 819
EP - 836
VL - 26
IS - 6
DP - DeepDyve
ER -