Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy [Figures, results and programs]
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http://hdl.handle.net/10045/110141
Títol: | Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy [Figures, results and programs] |
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Autors: | Tomás, M. Baralida | Ferrer, Belén | Mas, David |
Grups d'investigació o GITE: | Grupo de Análisis de Imagen, Sistemas Ópticos y Visión (IMAOS+V) |
Centre, Departament o Servei: | Universidad de Alicante. Instituto Universitario de Física Aplicada a las Ciencias y las Tecnologías | Universidad de Alicante. Departamento de Óptica, Farmacología y Anatomía | Universidad de Alicante. Departamento de Ingeniería Civil |
Paraules clau: | Peak-locking | Cross-correlation | Subpixel | Gaussian fitting | Thin-plate splines | Polynomial fitting |
Àrees de coneixement: | Óptica | Mecánica de Medios Contínuos y Teoría de Estructuras |
Data de publicació: | 5-de novembre-2020 |
Resum: | 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 extremely 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, may be attributted to an 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, 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. |
Descripció: | Figures, results and programs in Matlab format. Paper available in http://hdl.handle.net/10045/110428 |
URI: | http://hdl.handle.net/10045/110141 |
Idioma: | eng |
Tipus: | software |
Drets: | Creative Commons Attribution-ShareAlike 4.0 International License |
Revisió científica: | no |
Apareix a la col·lecció: | INV - IMAOS+V - Software y Aplicaciones |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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Programs_and_Results.zip | 673,4 kB | ZIP archive | Obrir | |
Figures.zip | 2,17 MB | ZIP archive | Obrir | |
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