Detecting, identifying, and recognizing salient regions or feature points

in images is a very important and fundamental problem to the computer vision

and robotics community. Tasks like landmark detection and visual odometry,

but also object recognition benefit from stable and repeatable salient features

that are invariant to a variety of effects like rotation, scale changes, view point

changes, noise, or change in illumination conditions. Recently, two promising new

approaches, SIFT and SURF, have been published. In this paper we compare and

evaluate how well different available implementations of SIFT and SURF perform

in terms of invariancy and runtime efficiency.