问题
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?
回答1:
It depends what you want to do. a 100x100 image does not contain a lot of information. Also, the descriptor area that SURF needs to make a meaningful descriptor is very small for 100x100 images. Depending on what you want to do, try the following:
- Use the whole 100 image as the descriptor size. Don't detect interest points at all, but place a single interest point in the centre of the 100x100 image (at 50, 50) and make the descriptor using the whole image data. This can help you to detect similar of those small images. Use upright SURF or orientation independent SURF.
- Use the double-image size flag to get more interest points.
- Decrease the dimension of the descriptor by using smaller surface and less squares. This actually works quite well for object recognition, but not so well for 3D reconstruction.
To sum it up. It all depends on what you want to achieve. Feel free to drop me a message (maybe on the Github repo of SURF)
来源：https://stackoverflow.com/questions/15045644/improving-the-performance-of-surf-on-small-images