Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 22; p. 6596
• Main Authors: Tomás, María-Baralida, Ferrer, Belén, Mas, David
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18.11.2020; MDPI
• Subjects: Accuracy; Algorithms; Aliasing; Bias; Cross correlation; Domains; Error analysis; Gaussian fitting; Low pass filters; Neighborhoods; Noise; peak-locking; polynomial fitting; subpixel; thin-plate splines
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20226596

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.