Abstract
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.
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