The availability of a large number of local invariant feature detectors has rendered the task of evaluating them an important issue in vision research. However, the maximum number of scenes utilized for performance comparison has so far been relatively small. This paper presents an evaluation framework and results based on it utilizing a large number of scenes, providing insights into the performance of local feature detectors under varying JPEG compression ratio, blur, and uniform light changes.
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