How can I compute the dense disparity map from to stereo images?
How can I compute the dense disparity map from to stereo images ?
My idea so far was to go over all the pixels from the first image, and scan the second image for matches. To compare the similarity of two pixels i computed the squared difference of pixels in a small window around them. The algorithm works relatively well on synthetic images, but fail on pictures that contain reflections, or have larger differences in light intensity. What approach should i use to get better results?
Masters of image processing, please share you knowledge.
Answer
The Otto-Chau stereo matching algorithm is very effective and combines adaptive least squares patch matching with sub-pixel precision together with region growing over the matched images:
Otto, G. P., Chau, T. K. W., 1989. ‘Region-growing’ algorithm for matching of terrain images. Image Vision Computing, 7(2), pp. 83-94.
It's an area based approach so does rely to an extent on reasonable amounts of texture in the images. if you have very sparse image data a feature based approach might work better, perhaps augmented by the adaptive LS matching for accuracy. Correlation approaches are easy to implement and can be fast but because they don't take into account affine warping between images they are often not as robust or as accurate as ALS techniques that use a Newton-Raphson scheme to minimise over a number of warping and radiometric parameters to get a really good fit from image to image.