No matches found with pictures of same area but with slight perspective shift.

[HaiderAbasi]

Matcher Works where the images just have an affine shift in my opinion like if image was translated,rotated or resized but when there is a perspective transformation it fails giving 0 matches. 1) Either good features were not extracted. 2) Maybe Ransac Failed. 3) OpenCv SiftCpu version easily finds the matches and computes a homography matrix even OpenCV SurfGPU find the matches in these images. Need Someone to explain to me why matcher is not working

and how can i resolve it?

[Celebrandil]

Do you mind uploading the images and the parameters you used for the matching code? It seems weird, but maybe there is some logical explanation.

[HaiderAbasi]

@Celebrandil Link To the images. https://jmp.sh/8IvnR76

Parameters for SiftFeaturesDetection int numOctaves = 5; / Number of octaves in Gaussian pyramid / // 5 float initBlur = 1.0f; / Amount of initial Gaussian blurring in standard deviations / float thresh = 3.5f; / Threshold on difference of Gaussians for feature pruning /// 3.5 default float minScale = 0.0f; / Minimum acceptable scale to remove fine-scale features /// default 0.0 bool upScale = false; / Whether to upscale image before extraction /

Parameters for finding Homography FindHomography(siftData1, homography, &numMatches, 10000, 0.0f, 0.80f, 5.0);// max amb 0.80 int numFit = ImproveHomography(siftData1, homography, 5, 0.00f, 0.80f, 3.0);

[Celebrandil]

I would recommend you to change the maxAmbiguity parameter from 0.80f to 0.90f or even up to 1.00f to completely disregard it. The parameter sets the threshold for how similar two possible matches are allowed to be for a match to be accepted. For man-made structures where you have many corners that look similar (such as houses) and when you have a lot of features, you have lots of similar structures and it's too harsh to set the parameter to 0.80f. I believe that In OpenCV you don't have such a test at all unless you implement it yourself. I get about 800 matches or so, which seems a bit few. I might look at it a bit deep later. My hunch is that there are loads of false positive matches due to the similarities between structures.

[HaiderAbasi]

Changing the max ambiguity parameter improved the results considerably. 800 matches is much better than none.Increasing max ambiguity to more than or equal to 1 starts getting lesser matches 0.9 seems optimal at this stage Would now try comparing the homography computed from the SiftCPU Version and upload the results for comparison. Thank you for the effort btw.

[Celebrandil]

No problem. I'm looking forward to hearing about your comparison.