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

Chapter 3
Local detectors
Research on local detectors can be dated back to 1977 when Hans Moravec has described an
interest operator which is today known as the Moravec operator [79]. In [80] Hans Moravec
described obstacle avoidance and navigation for a mobile robot. He was using his interest
operator to detect interest points in stereo image pairs and images from different viewpoints to
use them as features to build a 3D map of the environment. Feature matching was achieved with
correlation of 6 × 6 pixel image patches around the detected feature locations. The Moravec
operator is based on the auto-correlation function, that is measuring the gray-level difference
between a window and a shifted window in four directions. Calculating the sum of squared
distances in the window gives a measure for every shift. The values are high, if the graylevel
variance is high (textured) and low if there is low gray-level variance (e.g. homogeneous
region). If the measures for every direction are high, the pixel location is a good candidate for
an interest point. The smallest measure is then used as a quality measure for the interest point.
In most cases the detected locations lie on edges and corners. For such cases a little shift already
causes a difference. However, an obvious deficiency is the anisotropic behavior because there is
only a discrete set of shifts. This basic idea was carried on leading to the well known Harris
corner detector [40]. The idea got re-formulated using the structure tensor [9] and the second
moment matrix respectively, leading to different variants of corner detectors [30, 61, 91, 107].
Other approaches [7, 57] use the second derivatives (Hessian matrix [115]) instead of the first
derivatives. All these approaches can be considered belonging to one class of simple interest
point detectors. They all have in common to detect a location only. That means that for a
subsequent task like image matching via cross-correlation the size of the necessary matching
window has to be chosen independently. This limitation shows up if dealing with images which
show scale change. Although the detector might be able to detect the corresponding location,
the correlation window will not contain the same gray-values and the matching will fail.
This limitation was addressed by estimating a proper scale for every detected interest point.
With this information the scale of the matching window can be normalized and cross-correlation
would again work. The first work going into this direction was done by Tony Lindeberg [64] in
1998. Other approaches followed shortly by David Lowe [66] or Krystian Mikolajczyk [72]. This
class of interest operators is usually called scale-invariant interest operators.
However, research again went one step further. According to the success of interest operators
which are invariant to scale change methods were sought to create interest operators invariant to
a larger class of image transformations. This was driven mostly by developments in wide-baseline
image matching where significant perspective distortions occur. Research therein led to a new
class of interest detectors, affine-invariant detectors. In most cases such a detection consists of a
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