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Journal articles on the topic 'Robust fitting'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Dunlap, Brett I. "Robust and variational fitting." Physical Chemistry Chemical Physics 2, no. 10 (2000): 2113–16. http://dx.doi.org/10.1039/b000027m.

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2

Elsaied, Hanan, and Roland Fried. "ROBUST FITTING OF INARCH MODELS." Journal of Time Series Analysis 35, no. 6 (June 27, 2014): 517–35. http://dx.doi.org/10.1111/jtsa.12079.

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3

Yu, Jieqi, Sanjeev R. Kulkarni, and H. Vincent Poor. "Robust ellipse and spheroid fitting." Pattern Recognition Letters 33, no. 5 (April 2012): 492–99. http://dx.doi.org/10.1016/j.patrec.2011.11.025.

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4

Domínguez-Soria, Víctor D., Gerald Geudtner, José Luis Morales, Patrizia Calaminici, and Andreas M. Köster. "Robust and efficient density fitting." Journal of Chemical Physics 131, no. 12 (September 28, 2009): 124102. http://dx.doi.org/10.1063/1.3216476.

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5

Ladrón de Guevara, I., J. Muñoz, O. D. de Cózar, and E. B. Blázquez. "Robust Fitting of Circle Arcs." Journal of Mathematical Imaging and Vision 40, no. 2 (December 22, 2010): 147–61. http://dx.doi.org/10.1007/s10851-010-0249-8.

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6

Chang, Chung, and R. Todd Ogden. "Robust fitting for neuroreceptor mapping." Statistics in Medicine 28, no. 6 (March 15, 2009): 1004–16. http://dx.doi.org/10.1002/sim.3510.

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7

Aigner, Martin, and Bert JÃ¼ttler. "Robust fitting of parametric curves." PAMM 7, no. 1 (December 2007): 1022201–2. http://dx.doi.org/10.1002/pamm.200700009.

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8

Tew, David P. "Communication: Quasi-robust local density fitting." Journal of Chemical Physics 148, no. 1 (January 7, 2018): 011102. http://dx.doi.org/10.1063/1.5013111.

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9

Welsh, A. H., and A. F. Ruckstuhl. "Robust fitting of the binomial model." Annals of Statistics 29, no. 4 (August 2001): 1117–36. http://dx.doi.org/10.1214/aos/1013699996.

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10

Vorobyov, S. A., Yue Rong, N. D. Sidiropoulos, and A. B. Gershman. "Robust iterative fitting of multilinear models." IEEE Transactions on Signal Processing 53, no. 8 (August 2005): 2678–89. http://dx.doi.org/10.1109/tsp.2005.850343.

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11

Wang, Yidi, Michael J. Keith, Benjamin Stappers, and Wei Zheng. "Robust fitting for pulsar timing analysis." Monthly Notices of the Royal Astronomical Society 468, no. 3 (March 8, 2017): 2637–44. http://dx.doi.org/10.1093/mnras/stx579.

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12

Wang, Hanzi, and David Suter. "Using symmetry in robust model fitting." Pattern Recognition Letters 24, no. 16 (December 2003): 2953–66. http://dx.doi.org/10.1016/s0167-8655(03)00156-9.

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13

Bai, Xiuqin, Weixin Yao, and John E. Boyer. "Robust fitting of mixture regression models." Computational Statistics & Data Analysis 56, no. 7 (July 2012): 2347–59. http://dx.doi.org/10.1016/j.csda.2012.01.016.

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14

Oja, Erkki, and Liuyue Wang. "Robust fitting by nonlinear neural units." Neural Networks 9, no. 3 (April 1996): 435–44. http://dx.doi.org/10.1016/0893-6080(95)00071-2.

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15

D'Hondt, J., R. Fruhwirth, P. Vanlaer, and W. Waltenberger. "Sensitivity of robust vertex fitting algorithms." IEEE Transactions on Nuclear Science 51, no. 5 (October 2004): 2037–44. http://dx.doi.org/10.1109/tns.2004.832296.

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16

Karlis, D., and I. Ntzoufras. "Robust fitting of football prediction models." IMA Journal of Management Mathematics 22, no. 2 (October 21, 2010): 171–82. http://dx.doi.org/10.1093/imaman/dpq013.

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17

Inui, Kentaro, Shun'ichi Kaneko, and Satoru Igarashi. "Robust line fitting using LMedS clustering." Systems and Computers in Japan 34, no. 14 (October 14, 2003): 92–100. http://dx.doi.org/10.1002/scj.1225.

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18

Pham, Trung Thanh, Tat-Jun Chin, Konrad Schindler, and David Suter. "Interacting Geometric Priors For Robust Multimodel Fitting." IEEE Transactions on Image Processing 23, no. 10 (October 2014): 4601–10. http://dx.doi.org/10.1109/tip.2014.2346025.

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19

Wen, Fei, Rendong Ying, Zheng Gong, and Peilin Liu. "Efficient Algorithms for Maximum Consensus Robust Fitting." IEEE Transactions on Robotics 36, no. 1 (February 2020): 92–106. http://dx.doi.org/10.1109/tro.2019.2943061.

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20

Rubsamen, Michael, and Alex B. Gershman. "Robust Adaptive Beamforming Using Multidimensional Covariance Fitting." IEEE Transactions on Signal Processing 60, no. 2 (February 2012): 740–53. http://dx.doi.org/10.1109/tsp.2011.2174233.

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21

Al-Maadeed, Somaya, Resheed Almotaeryi, Richard Jiang, and Ahmed Bouridane. "Robust human silhouette extraction with Laplacian fitting." Pattern Recognition Letters 49 (November 2014): 69–76. http://dx.doi.org/10.1016/j.patrec.2014.06.001.

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22

Serfling, Robert. "Efficient and Robust Fitting of Lognormal Distributions." North American Actuarial Journal 6, no. 4 (October 2002): 95–109. http://dx.doi.org/10.1080/10920277.2002.10596067.

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23

Brazauskas, Vytaras. "Robust and Efficient Fitting of Loss Models." North American Actuarial Journal 13, no. 3 (July 2009): 356–69. http://dx.doi.org/10.1080/10920277.2009.10597561.

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24

Castelani, Emerson, Ronaldo Lopes, Wesley Shirabayashi, and Francisco Sobral. "RAFF.jl: Robust Algebraic Fitting Function in Julia." Journal of Open Source Software 4, no. 39 (July 2, 2019): 1385. http://dx.doi.org/10.21105/joss.01385.

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25

Guo, Jianfeng, and Jiameng Yang. "An iterative procedure for robust circle fitting." Communications in Statistics - Simulation and Computation 48, no. 6 (February 5, 2018): 1872–79. http://dx.doi.org/10.1080/03610918.2018.1425443.

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26

Duchesne, Thierry, and Jacques Rioux. "“Efficient and Robust Fitting of Lognormal Distributions,” Robert Serfling, October 2002." North American Actuarial Journal 7, no. 3 (July 2003): 112–16. http://dx.doi.org/10.1080/10920277.2003.10596108.

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27

Zhao, Mingyang, Xiaohong Jia, Lubin Fan, Yuan Liang, and Dong-Ming Yan. "Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models." IEEE Transactions on Image Processing 30 (2021): 3828–43. http://dx.doi.org/10.1109/tip.2021.3065799.

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28

Deschrijver, D., T. Dhaene, and D. De Zutter. "Robust Parametric Macromodeling Using Multivariate Orthonormal Vector Fitting." IEEE Transactions on Microwave Theory and Techniques 56, no. 7 (July 2008): 1661–67. http://dx.doi.org/10.1109/tmtt.2008.924346.

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29

Medley, Daniela O., Carlos Santiago, and Jacinto C. Nascimento. "Deep Active Shape Model for Robust Object Fitting." IEEE Transactions on Image Processing 29 (2020): 2380–94. http://dx.doi.org/10.1109/tip.2019.2948728.

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30

Paláncz, B., J. L. Awange, A. Somogyi, N. Rehány, T. Lovas, B. Molnár, and Y. Fukuda. "A robust cylindrical fitting to point cloud data." Australian Journal of Earth Sciences 63, no. 5 (July 3, 2016): 665–73. http://dx.doi.org/10.1080/08120099.2016.1230147.

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31

Pham, Trung T., Tat-Jun Chin, Jin Yu, and David Suter. "The Random Cluster Model for Robust Geometric Fitting." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 8 (August 2014): 1658–71. http://dx.doi.org/10.1109/tpami.2013.2296310.

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32

Xiao, Xueming, Hutao Cui, and Yang Tian. "Robust plane fitting algorithm for landing hazard detection." IEEE Transactions on Aerospace and Electronic Systems 51, no. 4 (October 2015): 2864–75. http://dx.doi.org/10.1109/taes.2015.140378.

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33

Fleishman, Shachar, Daniel Cohen-Or, and Cláudio T. Silva. "Robust moving least-squares fitting with sharp features." ACM Transactions on Graphics 24, no. 3 (July 2005): 544–52. http://dx.doi.org/10.1145/1073204.1073227.

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34

Zanderigo, Francesca, Robert Todd Ogden, Chung Chang, Stephen Choy, Andrew Wong, and Ramin Vaziri Parsey. "Robust Fitting of [11C]-WAY-100635 PET Data." Journal of Cerebral Blood Flow & Metabolism 30, no. 7 (February 24, 2010): 1366–72. http://dx.doi.org/10.1038/jcbfm.2010.20.

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Fitting of a positron emission tomography (PET) time–activity curve is typically accomplished according to the least squares (LS) criterion, which is optimal for data having Gaussian distributed errors, but not robust in the presence of outliers. Conversely, quantile regression (QR) provides robust estimates not heavily influenced by outliers, sacrificing a little efficiency relative to LS when no outliers are present. Given these considerations, we hypothesized that QR would improve parameter estimate accuracy as measured by reduced intersubject variance in distribution volume ( V T) compared with LS in PET modeling. We compare V T values after applying QR with those using LS on 49 controls studied with [11C]-WAY-100635. QR decreases the standard deviation of the V T estimates (relative improvement range: 0.08% to 3.24%), while keeping the within-group average V T values almost unchanged. QR variance reduction results in fewer subjects required to maintain the same statistical power in group analysis without additional hardware and/or image registration to correct head motion.

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35

Mejía-Rodríguez, Daniel, and Andreas M. Köster. "Robust and efficient variational fitting of Fock exchange." Journal of Chemical Physics 141, no. 12 (September 28, 2014): 124114. http://dx.doi.org/10.1063/1.4896199.

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36

Ogden, R. Todd, C. Chang, J. J. Mann, and R. V. Parsey. "Robust fitting for kinetic modeling of PET data." NeuroImage 31 (January 2006): T64. http://dx.doi.org/10.1016/j.neuroimage.2006.04.055.

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37

Song, Weixing, Weixin Yao, and Yanru Xing. "Robust mixture regression model fitting by Laplace distribution." Computational Statistics & Data Analysis 71 (March 2014): 128–37. http://dx.doi.org/10.1016/j.csda.2013.06.022.

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38

Chin, Tat-Jun, Zhipeng Cai, and Frank Neumann. "Robust Fitting in Computer Vision: Easy or Hard?" International Journal of Computer Vision 128, no. 3 (August 9, 2019): 575–87. http://dx.doi.org/10.1007/s11263-019-01207-y.

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39

Beltrão, J. F., J. B. C. Silva, and J. C. Costa. "Robust polynomial fitting method for regional gravity estimation." GEOPHYSICS 56, no. 1 (January 1991): 80–89. http://dx.doi.org/10.1190/1.1442960.

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Standard polynomial fitting methods are inconsistent in their formulation. The regional field is approximated by a polynomial fitted to the observed field. As a result, in addition to the nonuniqueness in the definition of the regional field, the fitted polynomial is strongly influenced by the residual field (observed field minus regional field). We present a regional‐residual separation method for gravity data which uses a robust procedure to determine the coefficients of a polynomial fitted to the observations. Under the hypothesis that the regional can be modeled correctly by the polynomial surface, the proposed method minimizes the influence of the residual field in the fitted surface. The proposed method was applied to real gravity data from Ceará state, Brazil, and produced information on zones of possible crustal thickening and the occurrence of lower‐crustal granulitic rocks thrust into the shallow subsurface.

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40

Clarke, Brenton R., Peter L. McKinnon, and Geoff Riley. "A fast robust method for fitting gamma distributions." Statistical Papers 53, no. 4 (September 30, 2011): 1001–14. http://dx.doi.org/10.1007/s00362-011-0404-3.

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41

Agarwal, Pankaj K., Sariel Har-Peled, and Hai Yu. "Robust Shape Fitting via Peeling and Grating Coresets." Discrete & Computational Geometry 39, no. 1-3 (September 12, 2007): 38–58. http://dx.doi.org/10.1007/s00454-007-9013-2.

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42

Hawkins, Douglas M., and Dost Muhammad Khan. "A procedure for robust fitting in nonlinear regression." Computational Statistics & Data Analysis 53, no. 12 (October 2009): 4500–4507. http://dx.doi.org/10.1016/j.csda.2009.07.006.

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43

Wang, Yiru, Yinlong Liu, Xuechen Li, Chen Wang, Manning Wang, and Zhijian Song. "GORFLM: Globally Optimal Robust Fitting for Linear Model." Signal Processing: Image Communication 84 (May 2020): 115834. http://dx.doi.org/10.1016/j.image.2020.115834.

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44

Politis, Dimitris N. "An algorithm for robust fitting of autoregressive models." Economics Letters 102, no. 2 (February 2009): 128–31. http://dx.doi.org/10.1016/j.econlet.2008.12.004.

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45

Nurunnabi, A., Y. Sadahiro, and R. Lindenbergh. "ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 30, 2017): 63–70. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-63-2017.

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Abstract:

This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63 meter (m); the proposed method on the other hand fit cylinders of on average 1.02&thinsp;m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.

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46

Carr, Ana C., and Sean D. Moore. "Robust Quantification of Polymerase Chain Reactions Using Global Fitting." PLoS ONE 7, no. 5 (May 31, 2012): e37640. http://dx.doi.org/10.1371/journal.pone.0037640.

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47

Briczinski, Stan J., Chun-Hsien Wen, John D. Mathews, John F. Doherty, and Qina N. Zhou. "Robust Voltage Fitting Techniques for Meteor Doppler Speed Determination." IEEE Transactions on Geoscience and Remote Sensing 44, no. 12 (December 2006): 3490–96. http://dx.doi.org/10.1109/tgrs.2006.881122.

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48

Wimmer, M., F. Stulp, S. Pietzsch, and B. Radig. "Learning Local Objective Functions for Robust Face Model Fitting." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 8 (August 2008): 1357–70. http://dx.doi.org/10.1109/tpami.2007.70793.

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49

Tennakoon, Ruwan, Alireza Sadri, Reza Hoseinnezhad, and Alireza Bab-Hadiashar. "Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting." IEEE Transactions on Image Processing 27, no. 9 (September 2018): 4182–94. http://dx.doi.org/10.1109/tip.2018.2834821.

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50

Tennakoon, Ruwan B., Alireza Bab-Hadiashar, Zhenwei Cao, Reza Hoseinnezhad, and David Suter. "Robust Model Fitting Using Higher Than Minimal Subset Sampling." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 2 (February 1, 2016): 350–62. http://dx.doi.org/10.1109/tpami.2015.2448103.

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