The Experts below are selected from a list of 1593 Experts worldwide ranked by ideXlab platform

David Mas - One of the best experts on this subject based on the ideXlab platform.

  • influence of neighborhood size and cross correlation peak fitting method on location accuracy
    Sensors, 2020
    Co-Authors: Mariabaralida Tomas, Belen Ferrer, David Mas
    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.

Mariabaralida Tomas - One of the best experts on this subject based on the ideXlab platform.

  • influence of neighborhood size and cross correlation peak fitting method on location accuracy
    Sensors, 2020
    Co-Authors: Mariabaralida Tomas, Belen Ferrer, David Mas
    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.

Belen Ferrer - One of the best experts on this subject based on the ideXlab platform.

  • influence of neighborhood size and cross correlation peak fitting method on location accuracy
    Sensors, 2020
    Co-Authors: Mariabaralida Tomas, Belen Ferrer, David Mas
    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.

Mas David - One of the best experts on this subject based on the ideXlab platform.

  • Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy [Figures, results and programs]
    2020
    Co-Authors: Tomás M. Baralida, Ferrer Belén, Mas David
    Abstract:

    Figures, results and programs in Matlab format. Paper available in http://hdl.handle.net/10045/110428A 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

  • Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy
    'MDPI AG', 2020
    Co-Authors: Tomás M. Baralida, Ferrer Belén, Mas David
    Abstract:

    Figures, results and programs in Matlab format available in http://hdl.handle.net/10045/110141A 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.This work has been supported by the Generalitat Valenciana and the European Social Fund (FSE) through the Recruitment of Predoctoral Research Staff ACIF/2018/211 included in the FSE Operational Program 2014–2020 of the Valencian Community. Belén Ferrer and María-Baralida Tomás acknowledge the support of the Generalitat Valenciana through Project GV/2020/077

Ojanen Teemu - One of the best experts on this subject based on the ideXlab platform.

  • Topological phase transitions in glassy quantum matter
    'American Physical Society (APS)', 2020
    Co-Authors: Sahlberg Isac, Weststrom Alex, Poyhonen Kim, Ojanen Teemu
    Abstract:

    Amorphous systems have rapidly gained attention as promising platforms for topological matter. In this work, we establish a scaling theory of amorphous topological phase transitions driven by the density of lattice points in two dimensions. By carrying out a finite-size scaling analysis of topological invariants averaged over discrete and continuum random geometries, we discover critical properties of Chern and Z(2) glass transitions. Even for short-range hopping models, the Chern glass phase may persist down to the fundamental lower bound given by the classical percolation threshold. While the topological indices accurately satisfy the postulated one-parameter scaling, they do not generally flow to the Closest Integer value in the thermodynamic limit. Furthermore, the value of the critical exponent describing the diverging localization length varies continuously along the phase boundary and is not fixed by the symmetry class of the Hamiltonian. We conclude that the critical behavior of amorphous topological systems exhibit characteristic features not observed in disordered systems, motivating a wealth of interesting research directions.Peer reviewe

  • Topological phase transitions in glassy quantum matter
    'American Physical Society (APS)', 2019
    Co-Authors: Sahlberg Isac, Weststrom Alex, Poyhonen Kim, Ojanen Teemu
    Abstract:

    Amorphous systems have rapidly gained promise as novel platforms for topological matter. In this work we establish a scaling theory of amorphous topological phase transitions driven by the density of lattice points in two dimensions. By carrying out a finite-size scaling analysis of topological invariants averaged over discrete and continuum random geometries, we discover unique critical properties of Chern and $\mathbb{Z}_2$ glass transitions. Even for short-range hopping models the Chern glass phase may persist down to the fundamental lower bound given by the classical percolation threshold. While the topological indices accurately satisfy the postulated one-parameter scaling, they do not generally flow to the Closest Integer value in the thermodynamic limit. Furthermore, the value of the critical exponent describing the diverging localization length varies continuously along the phase boundary and is not fixed by the symmetry class of the Hamiltonian. We conclude that the critical behaviour of amorphous topological systems exhibit characteristic features not observed in disordered systems, motivating a wealth of new research directions.Comment: 5.5 pages + 2.5-page appendix, 3+3 figures, updated figures in the appendi