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It’s All in the Details: Guiding Fine-Feature Characteristics in Artificial Medical Images using Diffusion Models

Hofmeijer EI, Zu X, van der Heijden F, Tan CO.   +3 more
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Identifying Skeleton Curves in Noisy Data

Communications in Statistics - Simulation and Computation, 2012
This article presents a Bayesian method to reconstruct the centerline in noisy data using B-spline curves. The method is illustrated on simulated two- and three-dimensional data and is applied to recover the centerline of the colon in single photon emission computed tomography images.
Larissa Stanberry, Hanna Jankowski
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Semi-parametric classification of noisy curves

Pattern Recognition, 2003
We propose a novel semi-parametric modeling strategy for classifying noisy curves. This strategy uses a family of non-linear parametric models to describe known aspects of the signal and its propagation, with a non-parametric component incorporating unmodeled characteristics.
Richard H. Glendinning, Amanda J. Goode
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Feature Curve Co‐Completion in Noisy Data

Computer Graphics Forum, 2018
AbstractFeature curves on 3D shapes provide important hints about significant parts of the geometry and reveal their underlying structure. However, when we process real world data, automatically detected feature curves are affected by measurement uncertainty, missing data, and sampling resolution, leading to noisy, fragmented, and incomplete feature ...
Anne Gehre, Isaak Lim, Leif Kobbelt
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Phase Information and Space Filling Curves in Noisy Motion Estimation [PDF]

IEEE Transactions on Image Processing, 2009
This correspondence presents a novel approach for translational motion estimation based on the phase of the Fourier transform. It exploits the equality between the averaging of a group of successive frames and the convolution of the reference one with an impulse train function.
Bruni V, De Canditiis D, Vitulano D
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Fitting the most probable curve to noisy observations

Proceedings of International Conference on Image Processing, 2002
Orthogonal distance regression (ODR) is widely used to fit a curve through scattered points in the plane. It generally produces good fits, but intuition and practice suggest it can give biased results when fitting closed convex curves or corners. ODR gives the maximum likelihood estimate of the best curve under a straightforward stochastic model for ...
Garry N. Newsam, Nicholas J. Redding
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Subtractive clustering: A tool for reconstructing noisy curves

2014 International Conference on Signal Processing and Integrated Networks (SPIN), 2014
A new approach for reconstructing noisy curves has been proposed in this paper. Direct use of curve fitting on the noisy data yields very poor results and so some form of smoothing is required to eliminate this noise. Subtractive clustering combined with traditional curve fitting has been used to generate the original curves.
Kavita Khanna, Navin Rajpal
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On the optimal detection of curves in noisy pictures

Communications of the ACM, 1971
Abstract : A technique for recognizing systems of lines is presented, in which the heuristic of the problem is not embedded in the recognition algorithm but is expressed in a figure of merit. A multistage decision process is then able to recognize in the input picture the optimal system of lines according to the given figure of merit. Due to the global
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<title>Estimation of tangents to a noisy discrete curve</title>

SPIE Proceedings, 2004
A new notion of discrete tangent, called order d discrete tangent, adapted to noisy curves, is proposed. It is based on the definition of discrete tangents given by A. Vialard in 1996, on the definition of fuzzy segments and on the linear algorithm of fuzzy segments recognition.
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Uncertainty quantification and estimation of closed curves based on noisy data

Computational Statistics, 2021
Estimating closed curves based on noisy data has been a popular and yet a challenging problem in many fields of applications. Yet, uncertainty quantification of such estimation methods has received much less attention in the literature. The primary challenge stems from the fact that the parametrization of a closed curve is not generally unique and ...
Luming Chen, Sujit K. Ghosh
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