Abstract
Not all interest points are equally interesting. The
most valuable interest points lead to optimal performance
of the computer vision method in which they are employed.
But a measure of this kind will be dependent on the chosen
vision application. We propose a more general performance
measure based on spatial invariance of interest points under
changing acquisition parameters by measuring the spatial
recall rate. The scope of this paper is to investigate the
performance of a number of existing well-established interest
point detection methods. Automatic performance evaluation
of interest points is hard because the true correspondence
is generally unknown. We overcome this by providing
an extensive data set with known spatial correspondence.
The data is acquired with a camera mounted on a 6-axis industrial
robot providing very accurate camera positioning.
Furthermore the scene is scanned with a structured light
scanner resulting in precise 3D surface information. In total
60 scenes are depicted ranging from model houses, building
material, fruit and vegetables, fabric, printed media and
more. Each scene is depicted from 119 camera positions and
19 individual LED illuminations are used for each position.
The LED illumination provides the option for artificially relighting
the scene from a range of light directions. This data set has given us the ability to systematically evaluate the performance
of a number of interest point detectors. The highlights
of the conclusions are that the fixed scale Harris corner
detector performs overall best followed by the Hessian
based detectors and the difference of Gaussian (DoG). The
methods based on scale space features have an overall better
performance than other methods especially when varying
the distance to the scene, where especially FAST corner detector,
Edge Based Regions (EBR) and Intensity Based Regions
(IBR) have a poor performance. The performance of
Maximally Stable Extremal Regions (MSER) is moderate.
We observe a relatively large decline in performance with
both changes in viewpoint and light direction. Some of our
observations support previous findings while others contradict
these findings.
Original language | English |
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Journal | International Journal of Computer Vision |
Volume | 97 |
Issue number | 1 |
Pages (from-to) | 18-35 |
ISSN | 0920-5691 |
DOIs | |
Publication status | Published - 2012 |
Keywords
- Interest point detectors
- Performance evaluation
- Benchmark data set
- Scene matching
- Object recognition