Abstract
A known technique to obtain subpixel resolution by using object tracking through cross-correlation consists of interpolating the obtained correlation function and then refining peak location. Although the technique provides accurate results, peak location is usually biased toward the closest integer coordinate. This effect is known as the peak-locking error and it strongly limits this calculation technique’s experimental accuracy. This error may differ depending on the scene and algorithm used to fit and interpolate the correlation peak, but in general, it may be attributed to a sampling problem and the presence of aliasing. Many studies in the literature analyze this effect in the Fourier domain. Here, we propose an alternative analysis on the spatial domain. According to our interpretation, the peak-locking error may be produced by a non-symmetrical sample distribution, thus provoking a bias in the result. According to this, the peak interpolant function, the size of the local domain and low-pass filters play a relevant role in diminishing the error. Our study explores these effects on different samples taken from the DIC Challenge database, and the results show that, in general, peak fitting with a Gaussian function on a relatively large domain provides the most accurate results.
Highlights
Cross-correlation is a useful technique for establishing similarity between two signals.As correlation can derive from minimizing the mean square error between two signals or images [1], it is a robust tool for comparing images corrupted by Gaussian noise, which is the normal case in most circumstances during image processing where illumination is good enough
We tested the accuracy of the commonest subpixel tracking methods based on cross-correlation
We focused on the methods that employ local interpolation in a small area around the correlation peak to refine the maximum location
Summary
As correlation can derive from minimizing the mean square error between two signals or images [1], it is a robust tool for comparing images corrupted by Gaussian noise, which is the normal case in most circumstances during image processing where illumination is good enough. Despite the many advantages and applications of the cross-correlation [2,3,4,5,6], its standard formulation presents two main drawbacks: the dependence of the correlation result on image and template amplitudes to, produce high peaks when a dark template is compared to a bright object or vice versa, even though their similarity is minimum [7] and the limited resolution, which is set, by construction, to one pixel.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
