2014
DOI: 10.1007/s10851-014-0536-x
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Guaranteed Ellipse Fitting with a Confidence Region and an Uncertainty Measure for Centre, Axes, and Orientation

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Cited by 47 publications
(52 citation statements)
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“…Since minimizing the orthogonal distance between a point and an ellipse has no closed-form solution, these routines resort to iterative techniques that are not guaranteed to converge on an ellipse. A commonly used algebraic method is the simple and robust DEF method developed by Fitzgibbon et al [62] that minimizes the sum of squared algebraic distances between the points and the ellipse, y F ( , ) [59,63] developed an algorithm based on the optimization of the approximate maximum likelihood distance which seeks a balance between the costly geometric methods and stable algebraic techniques. This algorithm-termed the FGEF method-also includes error estimation for the geometrically meaningful ellipse parameters (center coordinates, axes and orientation) which we have extended to include an estimate of the differential phase error, d δϕ .…”
Section: Appendix a Ellipse Fitting Methodsmentioning
confidence: 99%
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“…Since minimizing the orthogonal distance between a point and an ellipse has no closed-form solution, these routines resort to iterative techniques that are not guaranteed to converge on an ellipse. A commonly used algebraic method is the simple and robust DEF method developed by Fitzgibbon et al [62] that minimizes the sum of squared algebraic distances between the points and the ellipse, y F ( , ) [59,63] developed an algorithm based on the optimization of the approximate maximum likelihood distance which seeks a balance between the costly geometric methods and stable algebraic techniques. This algorithm-termed the FGEF method-also includes error estimation for the geometrically meaningful ellipse parameters (center coordinates, axes and orientation) which we have extended to include an estimate of the differential phase error, d δϕ .…”
Section: Appendix a Ellipse Fitting Methodsmentioning
confidence: 99%
“…This algorithm-termed the FGEF method-exhibits a smaller bias in the differential phase estimate over a relatively large phase range (centered on 2 π ) compared to the more commonly used 'direct ellipse fit' (DEF) technique [62]. Additionally, [63] includes error estimations for the geometrically meaningful ellipse parameters (center coordinates, axes and orientation). We have extended their work to include an estimate of the statistical uncertainty in the differential phase, d δϕ .…”
Section: Improved Ellipse Fittingmentioning
confidence: 99%
“…The code fit an ellipse to the points using the approximate maximum‐likelihood (AML) method of Szpak et al (). Crater center (decimal degrees), major ( a ) and minor ( b ) axes (kilometers), tilt angle (0° to 180°, counterclockwise for the major axis, where 0° means the major axis is in an East‐West direction), ellipticity e = a / b , and eccentricity ε = (1– b 2 / a 2 ) 1/2 were recorded along with standard errors for each value.…”
Section: Generating a New Global Lunar Crater Database And Informatimentioning
confidence: 99%
“…We found a perfectly suited algorithm recently described by Szpak et al (2015) that offers a solution for exactly this type of problem. The D and R uncertainties can be described as independent Gaussian noise with zero mean that is inhomogeneous, i.e.…”
Section: Size Of the Shadow From A Best-fitting Ellipsementioning
confidence: 99%
“…Finally, a confidence region in the plane of the ellipse is calculated, illustrating the zone in which the true ellipse is located at a given probability. We chose a planar 68.3% confidence region to be consistent with other literature in the field instead of the 95% region suggested by Szpak et al (2015), e.g. for industrial machine vision applications.…”
Section: Size Of the Shadow From A Best-fitting Ellipsementioning
confidence: 99%