Evaluation of three local descriptors on low resolution images for robot navigation

This paper presents an evaluation of the SIFT (Scale Invariant Feature Transform), Colour SIFT, and SURF (Speeded Up Robust Feature) descriptors on very low resolution images. The performance of the three descriptors are compared against each other on the precision and recall measures using ground truth correct matching data. Our experimental results show that both SIFT and Colour SIFT are more robust under changes of viewing angle and viewing distance but SURF is superior under changes of illumination and blurring. In terms of computation time, the SURF descriptors offer themselves as a good alternative to SIFT and CSIFT.

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