Cleaning images in order to perform feature detection I have a set of images (e.g. cars) and trying to perform feature detection using SIFT (actually, SIFT modification - A-SIFT, which is more robust to affine transformations) and I'm getting an extremely low results in matching even on the same models, same color and closely same view point.
I'm barely sure, that the problem is car's surface is glossy (that's leads to huge variation of reflections on surface) and shadows &amp; lighting (that's depends on location where photo was taken). Is there's image processing algorithms that can help me clean up images and/or decrease influence of that factors?
I had a quick look at CiteSeerX and Google Scholar but found nothing.
A common preprocessing tactic is to smooth the image prior to feature extraction using for instance a Gaussian kernel or a low pass filter. This allows for the high frequency spurious details to be removed.