Improving the performance of SURF on small images
Every implementation of SURF I have come across on the web seems to be particularly bad at extracting a useful number of interest points from small images (say, 100x100 or less).
I have tried a number of approaches:
1) Using various upscaling algorithms (from simple one like nearest-neighbor to more advanced ones - basically every upscaler imagemagick provides) to increase the size of small images before analysis.
2) Other image processing tweaks to bring out features in the image such as contrast enhancement and the use of different RGB weights in the computation of the integral image.
3) (Re-)compression, on the assumption that compression artifacts will appear primarily around existing features, increasing their relative "surface area."
However, none of these has had any measurable effect on the number of interest points extracted from small images.
Is there anything else worth trying? Or is SURF just bad at small images, period? If so, what other algorithms are better for those?
