PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

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But using a plane to plane homography limits the possible test cases to planar scenes only.
Because of this limitation it is questionable if the results of previous detector evaluations will
hold for realistic, non-planar scenes, especially for changing viewpoints. If interest regions are
primarily detected on depth discontinuities and viewed from a different viewpoint the appearance
changes significantly which will result in lower matching performance. Therefore the results
of the detector evaluation may change considerably if using 3D scenes. Figure 4.1 shows an
example for Hessian-Affine regions and MSER regions. A significant number of Hessian-Affine
regions are located on depth discontinuities while the MSER detector seems to avoid such areas.
This motivates our approach to apply the evaluation of local detectors to complex, realistic,
and practically relevant scenes. The basic idea of enabling this extension is to exploit the
properties of the trifocal geometry [44]. Instead of defining the ground truth for 2 images we
propose to use 3 images of the scene. A fundamental property of the trifocal geometry allows the
coordinate transfer of a point correspondence from 2 views into the third view. And this transfer
is not restricted to planes but is valid for arbitrary scenes. The proposed evaluation framework
compares different local detectors according to 3 measures. A repeatability score measures
the capabilities of local detectors to produce detections repetitive at the same locations in the
presence of viewpoint changes. A matching score compares the descriptive and discriminative
qualities of the detected regions. The matching score will also reflect the cases when a local
detector tends to produce detections on depth discontinuities. As a last measure the absolute
number of correct matches is introduced which is interesting in object recognition where a higher
number of matches increases the robustness against partial occlusions as well as in geometry
estimation where a higher number usually increases the accuracy.
(a)
(b)
Figure 4.1: (a) Hessian-Affine regions (a significant fraction of detections are located on depth
corners) (b) MSER regions (no detections on depth corners)