Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/22/6596 |
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| author | María-Baralida Tomás Belén Ferrer David Mas |
| author_facet | María-Baralida Tomás Belén Ferrer David Mas |
| author_sort | María-Baralida Tomás |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:45:36Z |
| format | Article |
| id | doaj.art-2e33ecf85f4343e880c62d276148779b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:45:36Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2e33ecf85f4343e880c62d276148779b2023-11-20T21:25:17ZengMDPI AGSensors1424-82202020-11-012022659610.3390/s20226596Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location AccuracyMaría-Baralida Tomás0Belén Ferrer1David Mas2University Institute of Physics Applied to the Sciences and Technologies, University of Alicante, P.O. Box 99, 03080 Alicante, SpainUniversity Institute of Physics Applied to the Sciences and Technologies, University of Alicante, P.O. Box 99, 03080 Alicante, SpainUniversity Institute of Physics Applied to the Sciences and Technologies, University of Alicante, P.O. Box 99, 03080 Alicante, SpainA 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.https://www.mdpi.com/1424-8220/20/22/6596peak-lockingcross-correlationsubpixelGaussian fittingthin-plate splinespolynomial fitting |
| spellingShingle | María-Baralida Tomás Belén Ferrer David Mas Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy Sensors peak-locking cross-correlation subpixel Gaussian fitting thin-plate splines polynomial fitting |
| title | Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy |
| title_full | Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy |
| title_fullStr | Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy |
| title_full_unstemmed | Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy |
| title_short | Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy |
| title_sort | influence of neighborhood size and cross correlation peak fitting method on location accuracy |
| topic | peak-locking cross-correlation subpixel Gaussian fitting thin-plate splines polynomial fitting |
| url | https://www.mdpi.com/1424-8220/20/22/6596 |
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