Relevant bibliographies by topics / Robust model fitting

Academic literature on the topic 'Robust model fitting'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Robust model fitting"

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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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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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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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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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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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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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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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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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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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Yang, Jingjing, and David W. Scott. "Robust fitting of a Weibull model with optional censoring." Computational Statistics & Data Analysis 67 (November 2013): 149–61. http://dx.doi.org/10.1016/j.csda.2013.05.009.

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Dissertations / Theses on the topic "Robust model fitting"

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Xing, Yanru. "Robust mixture regression model fitting by Laplace distribution." Kansas State University, 2013. http://hdl.handle.net/2097/16534.

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Master of Science
Department of Statistics
Weixing Song
A robust estimation procedure for mixture linear regression models is proposed in this report by assuming the error terms follow a Laplace distribution. EM algorithm is imple- mented to conduct the estimation procedure of missing information based on the fact that the Laplace distribution is a scale mixture of normal and a latent distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in this literature. A sensitivity study is also conducted based on a real data example to illustrate the application of the proposed method.
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Wang, Hanzi. "Robust statistics for computer vision : model fitting, image segmentation and visual motion analysis." Monash University, Dept. of Electrical and Computer Systems Engineering, 2004. http://arrow.monash.edu.au/hdl/1959.1/5345.

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Yang, Li. "Robust fitting of mixture of factor analyzers using the trimmed likelihood estimator." Kansas State University, 2014. http://hdl.handle.net/2097/18118.

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Master of Science
Department of Statistics
Weixin Yao
Mixtures of factor analyzers have been popularly used to cluster the high dimensional data. However, the traditional estimation method is based on the normality assumptions of random terms and thus is sensitive to outliers. In this article, we introduce a robust estimation procedure of mixtures of factor analyzers using the trimmed likelihood estimator (TLE). We use a simulation study and a real data application to demonstrate the robustness of the trimmed estimation procedure and compare it with the traditional normality based maximum likelihood estimate.
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Relvas, Carlos Eduardo Martins. "Modelos parcialmente lineares com erros simétricos autoregressivos de primeira ordem." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-28052013-182956/.

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Neste trabalho, apresentamos os modelos simétricos parcialmente lineares AR(1), que generalizam os modelos parcialmente lineares para a presença de erros autocorrelacionados seguindo uma estrutura de autocorrelação AR(1) e erros seguindo uma distribuição simétrica ao invés da distribuição normal. Dentre as distribuições simétricas, podemos considerar distribuições com caudas mais pesadas do que a normal, controlando a curtose e ponderando as observações aberrantes no processo de estimação. A estimação dos parâmetros do modelo é realizada por meio do critério de verossimilhança penalizada, que utiliza as funções escore e a matriz de informação de Fisher, sendo todas essas quantidades derivadas neste trabalho. O número efetivo de graus de liberdade e resultados assintóticos também são apresentados, assim como procedimentos de diagnóstico, destacando-se a obtenção da curvatura normal de influência local sob diferentes esquemas de perturbação e análise de resíduos. Uma aplicação com dados reais é apresentada como ilustração.
In this master dissertation, we present the symmetric partially linear models with AR(1) errors that generalize the normal partially linear models to contain autocorrelated errors AR(1) following a symmetric distribution instead of the normal distribution. Among the symmetric distributions, we can consider heavier tails than the normal ones, controlling the kurtosis and down-weighting outlying observations in the estimation process. The parameter estimation is made through the penalized likelihood by using score functions and the expected Fisher information. We derive these functions in this work. The effective degrees of freedom and asymptotic results are also presented as well as the residual analysis, highlighting the normal curvature of local influence under different perturbation schemes. An application with real data is given for illustration.
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Wong, Hoi Sim. "A preference analysis approach to robust geometric model fitting in computer vision." Thesis, 2013. http://hdl.handle.net/2440/82075.

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Robust model fitting is a crucial task in numerous computer vision applications, where the information of interest is often expressed as a mathematical model. The goal of model fitting is to estimate the model parameters that “best” explain the observed data. However, robust model fitting is a challenging problem in computer vision, since vision data are (1) unavoidably contaminated by outliers due to imperfections in data acquisition and preprocessing, and (2) often contain multiple instances (or structures) of a model. Many robust fitting methods involve generating hypotheses using random sampling, and then (1) score the hypotheses using a robust criterion or (2) use a mode seeking or clustering method to elicit the potential structures in the data. Obtaining a good set of hypotheses is crucial for success, however this is often timeconsuming, especially for heavily contaminated data. In addition, many irrelevant hypotheses are unavoidably generated during sampling process. This frequently becomes an obstacle for accurate estimation, and has been ignored in previous methods. In particular, mode seeking-based fitting methods are very sensitive to the proportion of good/bad hypotheses. This thesis proposes several sampling methods for rapid and effective generation of good hypotheses, and hypothesis filtering methods to remove bad hypotheses for accurate estimation. The techniques developed here can be easily integrated into existing fitting methods to significantly improve fitting accuracy. We also propose a hierarchical fitting method, which recognizes that details in real-life data are organized hierarchically (i.e., large structures cascading down to finer structures). This can avoid excessive parameter tuning to obtain a particular fitting result, whereas existing fitting methods often fit data with a single number of structures and permit only one interpretation of the data. The algorithms in this thesis are motivated by preference (or ranking) analysis, which has been widely used in areas such as information retrieval, artificial intelligence and marketing. Preference analysis provides a sophisticated non-parametric approach to analyzing the data and hypotheses in model fitting problems. The algorithms developed here are shown to be more reliable than previous methods, and to perform well in various vision tasks.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2013
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Hsiao, Yi, and 蕭奕. "Robust Model Fitting - Selection of Tuning Parameters in the Aspect of Gamma Clustering." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9escv5.

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碩士
國立臺灣大學
應用數學科學研究所
107
In 1995, Windham came out with an idea of weighted distribution in his thesis, Robustifying Model Fitting, and he used the idea to find a mean estimator when there are outliers in the original data. There is a tuning parameter in this estimator, and selecting the parameter will affect the mean estimate in the same data. In the same thesis, he also suggested a criterion of selecting the tuning parameter, but we found out that this criterion wasn’t doing well in some simulations. Considering the problem, we propose another criterion which can derive a better mean estimator. Besides, we can also apply this method to clustering problem.
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Yeh, Han-Chun, and 葉漢軍. "A Study of Fitting Local Geoid Model by Robust Weighted Total Least Squares Method -A Case Study of Taichung Area." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/48876762392689670067.

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碩士
國立中興大學
土木工程學系所
105
The objective of this study involved using global navigation satellite system (GNSS) data to achieve reasonable point height accuracy. In this study, the orthometric heights of benchmarks were obtained from first order leveling of Taichung city and GNSS measurements of ellipsoid heights underwent fitting. A traditional fitting method was adopted, in which geoid height was built using generalized least squares combined with a curved surface fitting method. However, because generalized least square calculations do not take into consideration random errors that exist in coefficient matrices and observation vectors, weighted total generalized least square-based calculations were performed to solve these problems. In this study, the combination of weighted total generalized least squares and the quadratic curved surface fitting method improved on the traditional method by considering the covariance matrices of coefficient vectors and observation vectors. The solutions of the new model were subsequently analyzed, elevating point height accuracy to ±1.401 cm. The new method satisfies height accuracy requirements demanded in engineering surveys and provides valuable information for regional geoid height research.
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Hanek, Robert [Verfasser]. "Fitting parametric curve models to images using local self-adapting separation criteria / Robert Hanek." 2004. http://d-nb.info/974166375/34.

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Books on the topic "Robust model fitting"

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Weinberg, Jonathan M. Knowledge, Noise, and Curve-Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198724551.003.0016.

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The psychology of the ‘Gettier effect’ appears robust—but complicated. Contrary to initial reports, more recent and thorough work by several groups of researchers indicates strongly that it is in fact found widely across cultures. Nonetheless, I argue that the pattern of psychological results should not at all be taken to settle the epistemological questions about the nature of knowledge. For the Gettier effect occurs both intermittently and with sensitivity to epistemically irrelevant factors. In short, the effect is noisy. And good principles of model selection indicate that, the noisier one’s data, the more one should prefer simpler curves over those that may be more complicated yet hew closer to the data. While we should not endorse K=JTB at this time, nonetheless the question ‘Is knowledge really just justified true belief?’ ought to be treated as once again in play.
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Book chapters on the topic "Robust model fitting"

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Storer, Markus, Peter M. Roth, Martin Urschler, Horst Bischof, and Josef A. Birchbauer. "Efficient Robust Active Appearance Model Fitting." In Communications in Computer and Information Science, 229–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11840-1_17.

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Chai, Dengfeng, and Qunsheng Peng. "Image Feature Detection as Robust Model Fitting." In Computer Vision – ACCV 2006, 673–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612704_67.

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Sariyanidi, Evangelos, Casey J. Zampella, Robert T. Schultz, and Birkan Tunc. "Inequality-Constrained and Robust 3D Face Model Fitting." In Computer Vision – ECCV 2020, 433–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58545-7_25.

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Ngo, Thanh Trung, Hajime Nagahara, Ryusuke Sagawa, Yasuhiro Mukaigawa, Masahiko Yachida, and Yasushi Yagi. "Adaptive-Scale Robust Estimator Using Distribution Model Fitting." In Computer Vision – ACCV 2009, 287–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12297-2_28.

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Xiao, Fan, Guobao Xiao, Xing Wang, Jin Zheng, Yan Yan, and Hanzi Wang. "A Hierarchical Voting Scheme for Robust Geometric Model Fitting." In Lecture Notes in Computer Science, 11–22. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71607-7_2.

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Rigby, Robert A., and Mikis D. Stasinopoulos. "Robust Fitting of an Additive Model for Variance Heterogeneity." In Compstat, 263–68. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-52463-9_30.

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Tiwari, Lokender, Saket Anand, and Sushil Mittal. "Robust Multi-Model Fitting Using Density and Preference Analysis." In Computer Vision – ACCV 2016, 308–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54190-7_19.

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Gui, Zhenghui, and Chao Zhang. "Robust Active Shape Model Construction and Fitting for Facial Feature Localization." In Lecture Notes in Computer Science, 1029–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527923_107.

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Cootes, Tim F., Mircea C. Ionita, Claudia Lindner, and Patrick Sauer. "Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting." In Computer Vision – ECCV 2012, 278–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33786-4_21.

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Cosío, Fernando Arámbula. "Robust Fitting of a Point Distribution Model of the Prostate Using Genetic Algorithms." In Lecture Notes in Computer Science, 76–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30126-4_10.

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Conference papers on the topic "Robust model fitting"

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Arellano, Claudia, and Rozenn Dahyot. "Robust Bayesian fitting of 3D morphable model." In the 10th European Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2534008.2534013.

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Wang, Hanzi, Guobao Xiao, Yan Yan, and David Suter. "Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.332.

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Tepper, Mariano, and Guillermo Sapiro. "Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.77.

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Trung Thanh Pham, Tat-Jun Chin, Jin Yu, and D. Suter. "The Random Cluster Model for robust geometric fitting." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247740.

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Wang, Hanzi, Jinlong Cai, and Jianyu Tang. "AMSAC: An adaptive robust estimator for model fitting." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738063.

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Zhu, Xiangyu, Dong Yi, Zhen Lei, and Stan Z. Li. "Robust 3D Morphable Model Fitting by Sparse SIFT Flow." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.693.

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Kluger, Florian, Eric Brachmann, Hanno Ackermann, Carsten Rother, Michael Ying Yang, and Bodo Rosenhahn. "CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00469.

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Chong, Mina, Qiming Li, Taotao Lai, Xiaodong Lan, Xiuzhong Wang, and Jun Li. "Outliers Removed via Spectral Clustering for Robust Model Fitting." In 2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2018. http://dx.doi.org/10.1109/iscid.2018.00059.

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Yu, Jin, Tat-Jun Chin, and David Suter. "A global optimization approach to robust multi-model fitting." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995608.

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Luo, Hailing, Guobao Xiao, and Hanzi Wang. "Simple Iterative Clustering on Graphs for Robust Model Fitting." In 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2018. http://dx.doi.org/10.1109/vcip.2018.8698736.

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Reports on the topic "Robust model fitting"

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Rahmani, Mehran, Xintong Ji, and Sovann Reach Kiet. Damage Detection and Damage Localization in Bridges with Low-Density Instrumentations Using the Wave-Method: Application to a Shake-Table Tested Bridge. Mineta Transportation Institute, September 2022. http://dx.doi.org/10.31979/mti.2022.2033.

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This study presents a major development to the wave method, a methodology used for structural identification and monitoring. The research team tested the method for use in structural damage detection and damage localization in bridges, the latter being a challenging task. The main goal was to assess capability of the improved method by applying it to a shake-table-tested prototype bridge with sparse instrumentation. The bridge was a 4-span reinforced concrete structure comprising two columns at each bent (6 columns total) and a flat slab. It was tested to failure using seven biaxial excitations at its base. Availability of a robust and verified method, which can work with sparse recording stations, can be valuable for detecting damage in bridges soon after an earthquake. The proposed method in this study includes estimating the shear (cS) and the longitudinal (cL) wave velocities by fitting an equivalent uniform Timoshenko beam model in impulse response functions of the recorded acceleration response. The identification algorithm is enhanced by adding the model’s damping ratio to the unknown parameters, as well as performing the identification for a range of initial values to avoid early convergence to a local minimum. Finally, the research team detect damage in the bridge columns by monitoring trends in the identified shear wave velocities from one damaging event to another. A comprehensive comparison between the reductions in shear wave velocities and the actual observed damages in the bridge columns is presented. The results revealed that the reduction of cS is generally consistent with the observed distribution and severity of damage during each biaxial motion. At bents 1 and 3, cS is consistently reduced with the progression of damage. The trends correctly detected the onset of damage at bent 1 during biaxial 3, and damage in bent 3 during biaxial 4. The most significant reduction was caused by the last two biaxial motions in bents 1 and 3, also consistent with the surveyed damage. In bent 2 (middle bent), the reduction trend in cS was relatively minor, correctly showing minor damage at this bent. Based on these findings, the team concluded that the enhanced wave method presented in this study was capable of detecting damage in the bridge and identifying the location of the most severe damage. The proposed methodology is a fast and inexpensive tool for real-time or near real-time damage detection and localization in similar bridges, especially those with sparsely deployed accelerometers.
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