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First of all, I suggest you to get a brief introfuction on how CUDA works and how the memory is managed to get better optimization (what is a CUDA Kernel and the block size). You can find some articles even in the OpenCV.org website.

Two action are always required:the copy of the images in the GPU memory (upload) and the download of the images from the GPU memory. Keep in minds that CUDA works with kernel and block, and you've to set the dimension and manage the memory at is best to achieve the maximum performance. Then what I can say to you is, yes, is possibile for sure, but how many images per times you can process is up to your graphic card specs. Here you can find an example of SIFT/SURF usage in OpenCV. I don't know how this is implemented so maybe the best things is just try it and find if it does the job and eventually think about optimize it.

Don't forget that you need OpenCV with CUDA enabled, for this feature it is required to compile the package from the sources.

First of all, I suggest you to get a brief introfuction on how CUDA works and how the memory is managed to get better optimization (what is a CUDA Kernel and the block size). You can find some articles even in the OpenCV.org website.

Two action are always required:the copy of the images in the GPU memory (upload) and the download of the images from the GPU memory. Keep in minds that CUDA works with kernel and block, and you've to set the dimension and manage the memory at is best to achieve the maximum performance. Then what I can say to you is, yes, is possibile for sure, but how many images per times you can process is up to your graphic card specs. Here you can find an example of SIFT/SURF CUDA usage in OpenCV. I don't know how this is implemented so maybe the best things is just try it and find if it does the job and eventually think about optimize it.

Don't forget that you need OpenCV with CUDA enabled, for this feature it is required to compile the package from the sources.