Experimenting with multiscale FAST detector with images from cell phone camera.
so far so good…
I continue to test SURF, in respect to scale space. Scale space is essentially a pyramid of progressively more blurred or lower resolution images. The idea of scale invariant feature detection is that the “real” feature should be present at several scales – that is should be clearly detectable at several image resolution/blur levels. The interesting thing I see is, that for SURF, at least for test images from Mikolajczyk ‘s dataset, scale space seems doesn’t affect detection rate with viewpoint change. I meant that there is no difference if feature distinct in several scales or only in one. That’s actually reasonable – scale space obviously benefit detection in the blurred images, or noisy images, or repeatability/correspondence in scaled images , and “viewpoint” images form Mikolajczyk ‘s dataset are clear, high resolution and about the same scale. Nevertheless there is some possibility for optimization here.
I have tested several modification of SURF, using original SURF Hessian, extremum of SURF-based Laplacian, Hessian-Laplace – extremum both Hessian and Laplacian and minimal eigenvalue of Hessian. They all give about the same detection rate, but original SURF Hessian give better results. Minimal eigenvalue of Hessian seems better scaling with threshold value – original Hessian absolute value could be very low, but eigenvalues are not. So this approach may have some advantage if there are potential precision loss problem, for example in fixed point calculations. A lot of high-end mobile phones still launched without hardware floating point so it still could be useful in AR or Computer Vision applications.
I’ve started experimenting with markerless tracking. I’ve captured several cityscape image sequences and processed them with SURF detector. I’ve used Nokia N95 viewfinder frames. Here descriptors were oriented:
There are some corresponding features detected in both images, but their descriptors arn’t fit.
Interesting, upright, not oriented descriptors give little different picture – some new correspondences found, some lost.