Research on Horizontal Line Fitting Algorithm Based on Robust Estimation

2016 ◽  
pp. 383-395 ◽  
Author(s):  
Chonghui Li ◽  
Yabo Luo ◽  
Yong Zheng ◽  
Chao Zhang
Keyword(s):  
Robust Estimation ◽  
Horizontal Line ◽  
Fitting Algorithm ◽  
Line Fitting

scholarly journals Amoeba: Automated Molecular Excitation Bayesian Line-fitting Algorithm

2021 ◽  
Vol 923 (2) ◽  
pp. 261
Author(s):  
Anita Petzler ◽  
J. R. Dawson ◽  
Mark Wardle
Keyword(s):  
Optical Depth ◽  
Bayesian Approach ◽  
Spectral Feature ◽  
Emission Spectra ◽  
Rotational State ◽  
Validity And Reliability ◽  
Fitting Algorithm ◽  
Molecular Excitation ◽  
Gaussian Decomposition ◽  
Line Fitting

Abstract The hyperfine transitions of the ground-rotational state of the hydroxyl radical (OH) have emerged as a versatile tracer of the diffuse molecular interstellar medium. We present a novel automated Gaussian decomposition algorithm designed specifically for the analysis of the paired on-source and off-source optical depth and emission spectra of these OH transitions. In contrast to existing automated Gaussian decomposition algorithms, Amoeba (Automated Molecular Excitation Bayesian line-fitting Algorithm) employs a Bayesian approach to model selection, fitting all four optical-depth and four emission spectra simultaneously. Amoeba assumes that a given spectral feature can be described by a single centroid velocity and full width at half maximum, with peak values in the individual optical-depth and emission spectra then described uniquely by the column density in each of the four levels of the ground-rotational state, thus naturally including the real physical constraints on these parameters. Additionally, the Bayesian approach includes informed priors on individual parameters that the user can modify to suit different data sets. Here we describe Amoeba and establish its validity and reliability in identifying and fitting synthetic spectra with known (but hidden) parameters, finding that the code performs very well in a series of practical tests. Amoeba’s core algorithm could be adapted to the analysis of other species with multiple transitions interconnecting shared levels (e.g., the 700 MHz lines of the first excited rotational state of CH). Users are encouraged to adapt and modify Amoeba to suit their own use cases.

scholarly journals ChainLineNet: Deep-Learning-Based Segmentation and Parameterization of Chain Lines in Historical Prints

2021 ◽  
Vol 7 (7) ◽  
pp. 120
Author(s):  
Aline Sindel ◽  
Thomas Klinke ◽  
Andreas Maier ◽  
Vincent Christlein
Keyword(s):  
Deep Learning ◽  
Transmitted Light ◽  
Horizontal Line ◽  
Generative Adversarial Network ◽  
Qualitative And Quantitative ◽  
Adversarial Network ◽  
Line Region ◽  
Fourier Filtering ◽  
Line Segmentation ◽  
Line Fitting

The paper structure of historical prints is sort of a unique fingerprint. Paper with the same origin shows similar chain line distances. As the manual measurement of chain line distances is time consuming, the automatic detection of chain lines is beneficial. We propose an end-to-end trainable deep learning method for segmentation and parameterization of chain lines in transmitted light images of German prints from the 16th Century. We trained a conditional generative adversarial network with a multitask loss for line segmentation and line parameterization. We formulated a fully differentiable pipeline for line coordinates’ estimation that consists of line segmentation, horizontal line alignment, and 2D Fourier filtering of line segments, line region proposals, and differentiable line fitting. We created a dataset of high-resolution transmitted light images of historical prints with manual line coordinate annotations. Our method shows superior qualitative and quantitative chain line detection results with high accuracy and reliability on our historical dataset in comparison to competing methods. Further, we demonstrated that our method achieves a low error of less than 0.7 mm in comparison to manually measured chain line distances.

Robust estimation and inference for bivariate line-fitting in allometry

2011 ◽  
Vol 53 (4) ◽  
pp. 652-672 ◽  
Author(s):  
Sara Taskinen ◽  
David I. Warton
Keyword(s):  
Robust Estimation ◽  
Fitting In ◽  
Line Fitting

Robust Estimation of Integrated Hydraulics and Parameters in Water Distribution Systems

2001 ◽  
Author(s):  
Mietek A. Brdys ◽  
Kazimierz Duzinkiewicz ◽  
Michal Grochowski ◽  
Tomasz Rutkowski
Keyword(s):  
Robust Estimation ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems
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