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your used reference image is probably not very good for feature detection. there are large homogeneous regions and repetitive patterns. but maybe some outlier rejection could help, e.g. a ratio test: reject matches if distance of best match/distance of second best match > 0.8 (best match = nearest neighbor). if results are still bad maybe an approach with hough transformation (detection of straight lines) would be better.

your Your used reference image is probably not very good for feature detection. there are It consists of large homogeneous regions and repetitive patterns. but patterns.

But maybe some outlier rejection could help, e.g. a ratio test: reject Reject matches if distance of best match/distance of second best match > 0.8 (best match = nearest neighbor). if neighbor).

If results are still bad bad, maybe an approach with hough transformation (detection of straight lines) would be better.