An analysis of the effect of different image preprocessing techniques on the performance of SURF: Speeded Up Robust Features

In this paper, we analyze the effect of different image preprocessing techniques on the performance of Speeded Up Robust Features, SURF. We investigate the effects of the techniques like Histogram Equalization, Multiscale Retinex, and Image Adaptive Contrast Enhancement (IACE) scheme that we propose, on the SURF in terms of its feature points detection, and computational time for extracting the descriptors. We then test the effect of these image preprocessing techniques on the repeatability of the state-of-the-art detectors like Harris-Affine, Hessian-Affine, MSER, Edge Based Regions, Intensity Based Regions, and SURF. We carry out the repeatability test on the standard images which have been used as a benchmark for the evaluation of the performance of other schemes for the detection of feature points. Finally, we propose a method for scaling large resolution images that can be used in conjunction with the IACE method to enhance the matching speed of SURF, along with maintaining the accuracy and the standard of its performance.