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

HarrisAffinePoints / shape estimation
HarrisAffinePoints / shape estimation
3.4. Comparison of the described methods 41
(a)
(b)
(c)
(d)
Figure 3.12: Example for normalization, an isotropic local structure is transformed using two
different affine transformations. (a)(b) Initial detections using the Harris-Affine detector, the
elliptical measurement region is represented using the second moment matrix. (c) Normalization
of the detection in (a). (d) Normalization of the detection in (b). The isotropic structure
(visualized by the two lines) has been reconstructed nicely by the normalization. However, the
normalized detections still differ in an arbitrary rotation.
the evaluation 1 . The table will be very useful if one needs to select the proper method for an
application or getting a general overview of the performance of state-of-the-art methods.
The table contains ratings for invariance, the number of detections, the repeatability score,
the matching score, the speed of the method and an overall rating based on a combination of
the other ratings. In the following the ratings and terminology used in the table are described.
Invariance: In this column the detectors invariance to a class of transformations is given. The
detectors are classified into three groups, no invariance (’none’), invariant to scale change
(’scale’) and invariant to affine transformation (’affine’). One method is rated with ’affine ∗ ’.
This is because the detector is not fully invariant to an affine transformation. For more
details see the description of the detector in the previous section.
Number of detections: The number of detections is quite different for the various detectors.
Although for most methods the number of detections depends on the parameter settings,
1 Implementations were collected by Krystian Mikolajczyk and are available at
http://www.robots.ox.ac.uk/∼vgg/research/affine/