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Fitting discrete polynomial curve and surface to noisy data
Annals of Mathematics and Artificial Intelligence, 2014Fitting geometric models such as lines, circles or planes is an essential task in image analysis and computer vision. This paper deals with the problem of fitting a discrete polynomial curve to given 2D integer points in the presence of outliers. A 2D discrete polynomial curve is defined as a set of integer points lying between two polynomial curves ...
Fumiki Sekiya, Akihiro Sugimoto
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Detecting Curved Edges in Noisy Images in Sublinear Time
Journal of Mathematical Imaging and Vision, 2016Detecting edges in noisy images is a fundamental task in image processing. Motivated, in part, by various real-time applications that involve large and noisy images, in this paper we consider the problem of detecting long curved edges under extreme computational constraints, that allow processing of only a fraction of all image pixels.
Yi-Qing Wang +3 more
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Smoothing: Computing Curves from Noisy Data
2009The previous two chapters have introduced the Matlab and R code needed to specify basis function systems and then to define curves by combining these coefficient arrays. For example, we saw how to construct a basis object such as heightbasis to define growth curves and how to combine it with a matrix of coefficients such as heightcoef so as to define ...
Giles Hooker +2 more
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A numerical procedure for curve fitting of noisy infrared spectra
Analytica Chimica Acta, 1998A method for the detection of overfitting of noisy spectra is presented. When fitting data that contains random noise, the autocorrelation function (RL) of residuals at lag 1 (R1) approaches zero and then shows a tendency toward more negative values, while the Wald–Wolfowitz test tends to give more positive values, as the data is overfitted.
Carlos Alciaturi +2 more
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Transformations for the Computer Detection of Curves in Noisy Pictures
Computer Graphics and Image Processing, 1975Abstract Transformations which map noisy feature points originating from the same curve in a picture into dense regions are considered. These transformations are to be followed by clustering to detect curves in the original picture. Properties of the transformations are treated as they relate to this subsequent clustering.
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Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves
Biometrics, 2001Summary.We propose a method of analyzing collections of related curves in which the individual curves are modeled as spline functions with random coefficients. The method is applicable when the individual curves are sampled at variable and irregularly spaced points.
Colin O. Wu, John Rice
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Discrete Polynomial Curve Fitting to Noisy Data
2012A discrete polynomial curve is defined as a set of points lying between two polynomial curves. This paper deals with the problem of fitting a discrete polynomial curve to given integer points in the presence of outliers. We formulate the problem as a discrete optimization problem in which the number of points included in the discrete polynomial curve ...
Akihiro Sugimoto, Fumiki Sekiya
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Properties of transforms for the detection of curves in noisy pictures
Computer Graphics and Image Processing, 1978Performance characteristics of Hough-like transforms for curve detection are presented. These results are based on previous work. Included are formulations of the transform method, homogeneity and packing in parameter space, sensitivity, estimation, and determination of required low-level measurement accuracies subject to cost constraints, and changes ...
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Curve Reconstruction from Noisy and Unordered Samples
Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, 2014An algorithm for the reconstruction of closed and open curves from clouds of their noisy and unordered samples is presented. Each curve is reconstructed as a polygonal path represented by its vertices, which are determined in an iterative process comprising evolutionary and decimation stages.
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Determining perceptually significant points on noisy boundary curves
Pattern Recognition Letters, 1991One method of describing the shape of objects in images is to locate points of maximum curvature on object boundaries. These are commonly believed to be the most perceptually significant points on digital curves. However, our work indicates that estimators of point curvature become highly unreliable in the presence of noise.
D. P. Illing, P. T. Fairney
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