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1 April 2020 Peak-locking minimization by three adjustment methods
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Abstract
A technique for obtaining subpixel resolution when tracking through cross-correlation consists of interpolating the obtained function and then refine the peak location. Although the technique provides accurate location results, the peak is always biased towards the closest integer coordinate. This effect is known as peak-locking error and is a major limit to the experimental accuracy of this calculation technique. This error may be different depending on the algorithm used to fit and interpolate the correlation peak but no systematic analysis was found in the literature. In our study we explore the three most common interpolation methods: thin-plate splines, second-order polynomial fit and Gaussian fit together with the influence of the extent of local interpolation area around the peak. Additionally, we have checked the influence of the image blurring on the results, since it is reported as one effective method to reduce the peak-locking error. Finally, the optimal adjustment found is the Gaussian fit with no blur and a neighborhood around the correlation peak of 11x11 pixels.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
María Baralida Tomás, David Mas, and Belen Ferrer "Peak-locking minimization by three adjustment methods", Proc. SPIE 11353, Optics, Photonics and Digital Technologies for Imaging Applications VI, 113531I (1 April 2020); https://doi.org/10.1117/12.2554024
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