IEEE Robotics & Automation Magazine - June 2012 - 80
Feature Descriptor
In the feature description step, the region around each
detected feature is converted into a compact descriptor that
can be matched against other descriptors. The simplest
descriptor of a feature is its appearance, that is, the intensity
of the pixels in a patch around the feature point. In this case,
error metrics such as the sum of squared differences (SSDs)
or the normalized cross correlation (NCC) can be used to
compare intensities [20]. Contrary to SSD, NCC compensates well for slight brightness changes. An alternative and
more robust image similarity measure is the Census transform [21], which converts each image patch into a binary
vector representing which neighbors have their intensity
above or below the intensity of the central pixel. The patch
similarity is then measured through Hamming distance.
changes consists in applying the detector at lower-scale and
upper-scale versions of the same image [Figure 2(a)]. Invariance to perspective changes is instead attained by approximating the perspective distortion as an affine one.
SIFT is a feature devised for object and place recognition and found to give outstanding results for VO. The
SIFT detector starts by convolving the upper and lower
scales of the image with a DoG operator and then takes the
local minima or maxima of the output across scales and
space (Figure 2). The power of SIFT is in its robust descriptor, which will be explained in the following section. The
SURF detector builds upon the SIFT but uses box filters to
approximate the Gaussian, resulting in a faster computation compared to SIFT, which is achieved with integral
images [90].
(a)
(b)
Figure 2. The original image (a, left) is smoothed with four Gaussian filters with different sigmas, and this is repeated after
downsampling the image of a factor two. Finally, (b) DoG images are computed by taking the difference between successive
Gaussian-smoothed images. SIFT features are found as local minima or maxima of DoG images across scales and space.
80
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IEEE ROBOTICS & AUTOMATION MAGAZINE
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JUNE 2012
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