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I am using SIFT to match features between two videos. I would like my HSV/CannyEdge filter trackbar GUI to automatically adjust parameters based on what will result in optimal feature-matching results. Because the feature matching is happening on a frame-by-frame basis, the parameter values (for what is optimal) might differ as I feature-match throughout the video.

Trackbar GUI. Applies adjustable image processing filters

Because there are so many possible parameter value variations, I am wondering what is the best way to automatically find the best combination. I would apply this technique to both images for the feature matching

Doing this manually is too time-consuming and I need an automatic way of maybe with brute-forcing every parameter value combination to find the best feature-matching result between the two images.

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  • If there was an easy method for automatic parameter adjustment, it would already be built into that matching function. In the first place, you need to understand the exact effect of these parameters and figure out if some kind of rule of thumb can be implemented for each. Commented Jan 9, 2023 at 14:58
  • You should specify which implementation of SIFT matcher you are using (is OpenCV code?) and also the track bar code (it is in C++, python?) Commented Jan 9, 2023 at 17:42
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