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Journal articles on the topic 'Geometric fitting'

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

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1

Martínez-Morales, José L. "Geometric data fitting." Abstract and Applied Analysis 2004, no. 10 (2004): 831–80. http://dx.doi.org/10.1155/s1085337504401043.

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2

Xiao, Guobao, Hanzi Wang, Taotao Lai, and David Suter. "Hypergraph modelling for geometric model fitting." Pattern Recognition 60 (December 2016): 748–60. http://dx.doi.org/10.1016/j.patcog.2016.06.026.

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3

Isack, Hossam, and Yuri Boykov. "Energy-Based Geometric Multi-model Fitting." International Journal of Computer Vision 97, no. 2 (July 12, 2011): 123–47. http://dx.doi.org/10.1007/s11263-011-0474-7.

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4

Wang, Tao, Zhaoyao Shi, and Bo Yu. "A parameterized geometric fitting method for ellipse." Pattern Recognition 116 (August 2021): 107934. http://dx.doi.org/10.1016/j.patcog.2021.107934.

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5

Ahn, Sung-Joon. "Geometric Fitting of Parametric Curves and Surfaces." Journal of Information Processing Systems 4, no. 4 (December 31, 2008): 153–58. http://dx.doi.org/10.3745/jips.2008.4.4.153.

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6

Kanatani, Kenichi, Prasanna Rangarajan, Yasuyuki Sugaya, and Hirotaka Niitsuma. "HyperLS for Parameter Estimation in Geometric Fitting." IPSJ Transactions on Computer Vision and Applications 3 (2011): 80–94. http://dx.doi.org/10.2197/ipsjtcva.3.80.

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7

Chan, T. O., and D. D. Lichti. "3D CATENARY CURVE FITTING FOR GEOMETRIC CALIBRATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-5/W12 (September 5, 2012): 259–64. http://dx.doi.org/10.5194/isprsarchives-xxxviii-5-w12-259-2011.

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8

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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9

Akar, Nail. "Fitting Matrix Geometric Distributions by Model Reduction." Stochastic Models 31, no. 2 (April 3, 2015): 292–315. http://dx.doi.org/10.1080/15326349.2014.1003271.

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10

Song, Peng, Zhongqi Fu, and Ligang Liu. "Grasp planning via hand-object geometric fitting." Visual Computer 34, no. 2 (November 7, 2016): 257–70. http://dx.doi.org/10.1007/s00371-016-1333-x.

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11

Kanatani, Kenichi, and Yasuyuki Sugaya. "Performance evaluation of iterative geometric fitting algorithms." Computational Statistics & Data Analysis 52, no. 2 (October 2007): 1208–22. http://dx.doi.org/10.1016/j.csda.2007.05.013.

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12

Veelaert, Peter. "Constructive Fitting and Extraction of Geometric Primitives." Graphical Models and Image Processing 59, no. 4 (July 1997): 233–51. http://dx.doi.org/10.1006/gmip.1997.0433.

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13

AHN, SUNG JOON, and WOLFGANG RAUH. "GEOMETRIC LEAST SQUARES FITTING OF CIRCLE AND ELLIPSE." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 07 (November 1999): 987–96. http://dx.doi.org/10.1142/s0218001499000549.

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The least squares fitting of geometric features to given points minimizes the squares sum of error-of-fit in predefined measures. By the geometric fitting, the error distances are defined with the orthogonal, or shortest, distances from the given points to the geometric feature to be fitted. For the geometric fitting of circle and ellipse, robust algorithms are proposed which are based on the coordinate descriptions of the corresponding point on the circle/ellipse for the given point, where the connecting line of the two points is the shortest path from the given point to the circle/ellipse.
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14

Zhang, Zongliang, Hongbin Zeng, Jonathan Li, Yiping Chen, Chenhui Yang, and Cheng Wang. "Geometric Multi-Model Fitting by Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10081–82. http://dx.doi.org/10.1609/aaai.v33i01.330110081.

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This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations.
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15

Chernov, N., Q. Huang, and H. Ma. "Does the Best-Fitting Curve Always Exist?" ISRN Probability and Statistics 2012 (September 13, 2012): 1–25. http://dx.doi.org/10.5402/2012/895178.

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Fitting geometric shapes to observed data points (images) is a popular task in computer vision and modern statistics (errors-in-variables regression). We investigate the problem of existence of the best fit using geometric and probabilistic approaches.
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16

Chan, T. O., and D. D. Lichti. "Geometric Modelling of Octagonal Lamp Poles." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 6, 2014): 145–50. http://dx.doi.org/10.5194/isprsarchives-xl-5-145-2014.

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Lamp poles are one of the most abundant highway and community components in modern cities. Their supporting parts are primarily tapered octagonal cones specifically designed for wind resistance. The geometry and the positions of the lamp poles are important information for various applications. For example, they are important to monitoring deformation of aged lamp poles, maintaining an efficient highway GIS system, and also facilitating possible feature-based calibration of mobile LiDAR systems. In this paper, we present a novel geometric model for octagonal lamp poles. The model consists of seven parameters in which a rotation about the z-axis is included, and points are constrained by the trigonometric property of 2D octagons after applying the rotations. For the geometric fitting of the lamp pole point cloud captured by a terrestrial LiDAR, accurate initial parameter values are essential. They can be estimated by first fitting the points to a circular cone model and this is followed by some basic point cloud processing techniques. The model was verified by fitting both simulated and real data. The real data includes several lamp pole point clouds captured by: (1) Faro Focus 3D and (2) Velodyne HDL-32E. The fitting results using the proposed model are promising, and up to 2.9 mm improvement in fitting accuracy was realized for the real lamp pole point clouds compared to using the conventional circular cone model. The overall result suggests that the proposed model is appropriate and rigorous.
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17

Lin, Shuyuan, Guobao Xiao, Yan Yan, David Suter, and Hanzi Wang. "Hypergraph Optimization for Multi-Structural Geometric Model Fitting." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8730–37. http://dx.doi.org/10.1609/aaai.v33i01.33018730.

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Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points. However, a hypergraph becomes extremely complicated when the input data include a large number of data points (usually contaminated with noises and outliers), which will significantly increase the computational burden. In order to overcome the above problem, we propose a novel hypergraph optimization based model fitting (HOMF) method to construct a simple but effective hypergraph. Specifically, HOMF includes two main parts: an adaptive inlier estimation algorithm for vertex optimization and an iterative hyperedge optimization algorithm for hyperedge optimization. The proposed method is highly efficient, and it can obtain accurate model fitting results within a few iterations. Moreover, HOMF can then directly apply spectral clustering, to achieve good fitting performance. Extensive experimental results show that HOMF outperforms several state-of-the-art model fitting methods on both synthetic data and real images, especially in sampling efficiency and in handling data with severe outliers.
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18

Livadiotis. "Geometric Interpretation of Errors in Multi-Parametrical Fitting Methods Based on Non-Euclidean Norms." Stats 2, no. 4 (October 29, 2019): 426–38. http://dx.doi.org/10.3390/stats2040029.

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The paper completes the multi-parametrical fitting methods, which are based on metrics induced by the non-Euclidean Lq-norms, by deriving the errors of the optimal parameter values. This was achieved using the geometric representation of the residuals sum expanded near its minimum, and the geometric interpretation of the errors. Typical fitting methods are mostly developed based on Euclidean norms, leading to the traditional least–square method. On the other hand, the theory of general fitting methods based on non-Euclidean norms is still under development; the normal equations provide implicitly the optimal values of the fitting parameters, while this paper completes the puzzle by improving understanding the derivations and geometric meaning of the optimal errors.
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19

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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20

Hyde, Homer Clark, Walter Sandtner, Janice Robertson, Alper Dagcan, Benoit Roux, Francisco Bezanilla, and Ana M. Correa. "3D Geometric Monte Carlo Fitting of LRET Data." Biophysical Journal 98, no. 3 (January 2010): 521a. http://dx.doi.org/10.1016/j.bpj.2009.12.2832.

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21

Hoburg, Warren, Philippe Kirschen, and Pieter Abbeel. "Data fitting with geometric-programming-compatible softmax functions." Optimization and Engineering 17, no. 4 (August 4, 2016): 897–918. http://dx.doi.org/10.1007/s11081-016-9332-3.

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22

Xiao, Guobao, Hanzi Wang, Yan Yan, and David Suter. "Superpixel-Guided Two-View Deterministic Geometric Model Fitting." International Journal of Computer Vision 127, no. 4 (May 19, 2018): 323–39. http://dx.doi.org/10.1007/s11263-018-1100-8.

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23

BEKTAS, SEBAHATTIN. "LEAST SQUARES FITTING OF ELLIPSOID USING ORTHOGONAL DISTANCES." Boletim de Ciências Geodésicas 21, no. 2 (June 2015): 329–39. http://dx.doi.org/10.1590/s1982-21702015000200019.

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In this paper, we present techniques for ellipsoid fitting which are based on minimizing the sum of the squares of the geometric distances between the data and the ellipsoid. The literature often uses "orthogonal fitting" in place of "geometric fitting" or "best-fit". For many different purposes, the best-fit ellipsoid fitting to a set of points is required. The problem offitting ellipsoid is encounteredfrequently intheimage processing, face recognition, computer games, geodesy etc. Today, increasing GPS and satellite measurementsprecisionwill allow usto determine amore realistic Earth ellipsoid. Several studies have shown that the Earth, other planets, natural satellites, asteroids and comets can be modeled as triaxial ellipsoids Burša and Šima (1980), Iz et all (2011). Determining the reference ellipsoid for the Earth is an important ellipsoid fitting application, because all geodetic calculations are performed on the reference ellipsoid. Algebraic fitting methods solve the linear least squares (LS) problem, and are relatively straightforward and fast. Fitting orthogonal ellipsoid is a difficult issue. Usually, it is impossible to reach a solution with classic LS algorithms. Because they are often faced with the problem of convergence. Therefore, it is necessary to use special algorithms e.g. nonlinear least square algorithms. We propose to use geometric fitting as opposed to algebraic fitting. This is computationally more intensive, but it provides scope for placing visually apparent constraints on ellipsoid parameter estimation and is free from curvature bias Ray and Srivastava (2008).
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24

Danish, Muhammad Yameen, and Muhammad Aslam. "Fitting and Analyzing Randomly Censored Geometric Extreme Exponential Distribution." Pakistan Journal of Statistics and Operation Research 12, no. 2 (June 3, 2016): 301. http://dx.doi.org/10.18187/pjsor.v12i2.1242.

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25

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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26

Wong, Hoi Sim, Tat-Jun Chin, Jin Yu, and David Suter. "Mode seeking over permutations for rapid geometric model fitting." Pattern Recognition 46, no. 1 (January 2013): 257–71. http://dx.doi.org/10.1016/j.patcog.2012.07.005.

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27

López-Rubio, Ezequiel, Karl Thurnhofer-Hemsi, Elidia Beatriz Blázquez-Parra, Óscar David de Cózar-Macías, and M. Carmen Ladrón-de-Guevara-Muñoz. "A fast robust geometric fitting method for parabolic curves." Pattern Recognition 84 (December 2018): 301–16. http://dx.doi.org/10.1016/j.patcog.2018.07.019.

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28

Borkowski, J., B. J. Matuszewski, J. Mroczka, and L. K. Shark. "Geometric matching of circular features by least squares fitting." Pattern Recognition Letters 23, no. 7 (May 2002): 885–94. http://dx.doi.org/10.1016/s0167-8655(01)00164-7.

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29

Kineri, Yuki, Mingsi Wang, Hongwei Lin, and Takashi Maekawa. "-spline surface fitting by iterative geometric interpolation/approximation algorithms." Computer-Aided Design 44, no. 7 (July 2012): 697–708. http://dx.doi.org/10.1016/j.cad.2012.02.011.

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30

Xiao’an, Sun, Chen Shuzhen, and Shen Qiang. "Geometric calibration of lens using B-spline surface fitting." Wuhan University Journal of Natural Sciences 3, no. 4 (December 1998): 440–42. http://dx.doi.org/10.1007/bf02830046.

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31

Kanatani, Kenichi, and Yasuyuki Sugaya. "Unified Computation of Strict Maximum Likelihood for Geometric Fitting." Journal of Mathematical Imaging and Vision 38, no. 1 (May 27, 2010): 1–13. http://dx.doi.org/10.1007/s10851-010-0206-6.

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32

Li, Beichen, Jingyu Yang, Xinyang Zeng, Huanjing Yue, and Wei Xiang. "Automatic Gauge Detection via Geometric Fitting for Safety Inspection." IEEE Access 7 (2019): 87042–48. http://dx.doi.org/10.1109/access.2019.2925087.

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33

UNNIKRISHNAN, RANJITH, JEAN-FRANÇOIS LALONDE, NICOLAS VANDAPEL, and MARTIAL HEBERT. "SCALE SELECTION FOR GEOMETRIC FITTING IN NOISY POINT CLOUDS." International Journal of Computational Geometry & Applications 20, no. 05 (October 2010): 543–75. http://dx.doi.org/10.1142/s0218195910003438.

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In recent years, there has been a resurgence in the use of raw point cloud data as the geometric primitive of choice for several modeling tasks such as rendering, editing and compression. Algorithms using this representation often require reliable additional information such as the curve tangent or surface normal at each point. Estimation of these quantities requires the selection of an appropriate scale of analysis to accommodate sensor noise, density variation and sparsity in the data. To this goal, we present a new class of locally semi-parametric estimators that allows analysis of accuracy with finite samples, as well as explicitly addresses the problem of selecting optimal support volume for local fitting. Experiments on synthetic and real data validate the behavior predicted by the model, and show competitive performance and improved stability over leading alternatives that require a preset scale.
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34

Salehi, Danial, Dariush Sardari, and Milad Jozani. "Semi-empirical relationship for the energy absorption buildup factor in some biological samples." Nuclear Technology and Radiation Protection 31, no. 4 (2016): 382–87. http://dx.doi.org/10.2298/ntrp1604382s.

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Energy absorption buildup factors in the energy range of 0.2 MeV to 2 MeV using a geometric progression fitting approximation in some selected essential amino acids, fatty acids and carbohydrate molecules have been obtained. A semi empirical relation-ship describing energy absorption buildup factors as a function of penetration depth, Compton scattering and energy absorption cross-section is used. This semi empirical method was defined in an earlier work on water and soft tissue by one of the present authors. We used this method for the calculating energy absorption buildup factor in biological samples. The results are compared with the energy absorption buildup factors data of the geometric progression fitting method. Good agreement between semi empirical and geometric progression fitting methods has been observed, so that average deviation is less than 2 % for all samples.
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35

Pairel, Eric. "Three-Dimensional Verification of Geometric Tolerances With the “Fitting Gauge” Model." Journal of Computing and Information Science in Engineering 7, no. 1 (July 13, 2006): 26–30. http://dx.doi.org/10.1115/1.2410022.

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Thanks to the “fitting gauge” conceptual model, developed in our lab, any geometric tolerance can be interpreted in the form of a virtual three-dimensional gauge, which is able to be assembled with the part to be inspected. From a file containing the sampled points of the part to inspect, the experimental software, using this conceptual model, permits one to build the virtual gauge defined by the geometric tolerance and to check that it can be assembled and adjusted, according to a precise order, with clouds of points representing the part. Checking the geometric tolerances is thus strictly in conformity with their standardized meaning and it is extremely simplified.
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36

Hu, Shuixian, Ruomei Wang, and Fan Zhou. "An efficient multi-layer garment virtual fitting algorithm based on the geometric method." International Journal of Clothing Science and Technology 29, no. 1 (March 6, 2017): 25–38. http://dx.doi.org/10.1108/ijcst-06-2015-0068.

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Purpose The purpose of this paper is to present an efficient algorithm for multi-layer garment fitting simulation based on the geometric method to solve the low time cost problem during penetration detection and processing. This is more practical to design a CAD system to preview the multi-layer garment fitting effect in daily life. Design/methodology/approach The construction of a multi-layer garment based on existing 3D garments is a suitable method because this method is similar to the daily method of multi-layer dressing. The major problem is the penetration phenomenon between different garments because these 3D garment’s geometric shapes are constructed in different situations. In this paper, an efficient algorithm of multi-layer garment simulation is reported. A face-face intersection detection algorithm is designed to detect the penetration region between multi-layer garments fast and a geometric penetration processing algorithm is presented to solve the penetration phenomenon during multi-layer garment simulation. Findings This method can quickly detect the penetration between faces, and then deal with the penetration for multi-layer garment construction. Experimental results show that this method can not only remove the penetration but basically maintain the trend of wrinkles efficiently. At the same time, the garments used in the experiment have almost more than 5,800 faces, but the resolving time is under five seconds. Originality/value The main originalities of the multi-layer garment virtual fitting algorithm based on the geometric method are highly efficient both in terms of time cost and fitting effect. Based on this method, the technology of multi-layer garment virtual fitting can be used to design a novel CAD system to preview the multi-layer garment fitting effect in real time. This is a pressing requirement of virtual garment applications.
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37

Poniatowska, Małgorzata, and Andrzej Werner. "Fitting Spatial Models of Geometric Deviations of Free-Form Surfaces Determined in Coordinate Measurements." Metrology and Measurement Systems 17, no. 4 (January 1, 2010): 599–610. http://dx.doi.org/10.2478/v10178-010-0049-x.

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Fitting Spatial Models of Geometric Deviations of Free-Form Surfaces Determined in Coordinate MeasurementsLocal geometric deviations of free-form surfaces are determined as normal deviations of measurement points from the nominal surface. Different sources of errors in the manufacturing process result in deviations of different character, deterministic and random. The different nature of geometric deviations may be the basis for decomposing the random and deterministic components in order to compute deterministic geometric deviations and further to introduce corrections to the processing program. Local geometric deviations constitute a spatial process. The article suggests applying the methods of spatial statistics to research on geometric deviations of free-form surfaces in order to test the existence of spatial autocorrelation. Identifying spatial correlation of measurement data proves the existence of a systematic, repetitive processing error. In such a case, the spatial modelling methods may be applied to fitting a surface regression model representing the deterministic deviations. The first step in model diagnosing is to examine the model residuals for the probability distribution and then the existence of spatial autocorrelation.
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38

Bayer, Jason D., Matthew Epstein, and Jacques Beaumont. "FittingC2Continuous Parametric Surfaces to Frontiers Delimiting Physiologic Structures." Computational and Mathematical Methods in Medicine 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/278479.

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We present a technique to fitC2continuous parametric surfaces to scattered geometric data points forming frontiers delimiting physiologic structures in segmented images. Such mathematical representation is interesting because it facilitates a large number of operations in modeling. While the fitting ofC2continuous parametric curves to scattered geometric data points is quite trivial, the fitting ofC2continuous parametric surfaces is not. The difficulty comes from the fact that each scattered data point should be assigned a unique parametric coordinate, and the fit is quite sensitive to their distribution on the parametric plane. We present a new approach where a polygonal (quadrilateral or triangular) surface is extracted from the segmented image. This surface is subsequently projected onto a parametric plane in a manner to ensure a one-to-one mapping. The resulting polygonal mesh is then regularized for area and edge length. Finally, from this point, surface fitting is relatively trivial. The novelty of our approach lies in the regularization of the polygonal mesh. Process performance is assessed with the reconstruction of a geometric model of mouse heart ventricles from a computerized tomography scan. Our results show an excellent reproduction of the geometric data with surfaces that areC2continuous.
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39

Wang, Wei, Yi Zhang, and Jian Guo Yang. "Modeling of Compound Errors for CNC Machine Tools." Advanced Materials Research 472-475 (February 2012): 1796–99. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.1796.

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In this paper, a synthesis modeling method of geometric and thermal error is presented. Through the analysis of machine error data at varying temperatures, the error distribution rule is obtained. Based on the different characteristics of geometric error and thermal error, error separation method has been carried out in the modeling. Using polynomial fitting for geometric error and linear fitting for thermal error, a synthesis mathematical model has been proposed. This error compensation method concerns the variations of geometric errors at different temperatures in the machine working, thus a comprehensive analysis is made on the error and its regularity from the overall temperature rise to the heat steady-state. Both at low and high temperatures in the machine working, the experimental validations show that the positioning errors of the machine tool are reduced effectively after applying the error compensation approach.
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40

Zhao, Xiang Jun, Mei Lu, and Jian Hua Gong. "Geometric Features Sensitive Mesh Segmentation Orient to Patch-Based Fitting." Applied Mechanics and Materials 130-134 (October 2011): 2081–85. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2081.

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Segmenting meshes into natural regions is useful for patch-based mesh fitting. In this paper, we present a novel algorithm for segmenting meshes into characteristic patches and provide a corresponding geometric proxy for each patch. We extend the powerful optimization technique of variational shape approximation by allowing for several different primitives to represent the geometric proxy of a surface region. Our method has the particular advantage of robustness. As the principal curvatures of the surfaces become more equal, the returned results are become closer to the surfaces of geometry primitives, i.e. planes, cylinders, or cones, or rotating surface which are the most common patch types in the reverse engineering. The expected result that we recover surface structures more robustly and thus get better approximations, is validated and demonstrated on a number of examples.
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41

Azorin-Lopez, Jorge, Marc Sebban, Andres Fuster-Guillo, Marcelo Saval-Calvo, and Amaury Habrard. "Iterative multilinear optimization for planar model fitting under geometric constraints." PeerJ Computer Science 7 (September 29, 2021): e691. http://dx.doi.org/10.7717/peerj-cs.691.

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Planes are the core geometric models present everywhere in the three-dimensional real world. There are many examples of manual constructions based on planar patches: facades, corridors, packages, boxes, etc. In these constructions, planar patches must satisfy orthogonal constraints by design (e.g. walls with a ceiling and floor). The hypothesis is that by exploiting orthogonality constraints when possible in the scene, we can perform a reconstruction from a set of points captured by 3D cameras with high accuracy and a low response time. We introduce a method that can iteratively fit a planar model in the presence of noise according to three main steps: a clustering-based unsupervised step that builds pre-clusters from the set of (noisy) points; a linear regression-based supervised step that optimizes a set of planes from the clusters; a reassignment step that challenges the members of the current clusters in a way that minimizes the residuals of the linear predictors. The main contribution is that the method can simultaneously fit different planes in a point cloud providing a good accuracy/speed trade-off even in the presence of noise and outliers, with a smaller processing time compared with previous methods. An extensive experimental study on synthetic data is conducted to compare our method with the most current and representative methods. The quantitative results provide indisputable evidence that our method can generate very accurate models faster than baseline methods. Moreover, two case studies for reconstructing planar-based objects using a Kinect sensor are presented to provide qualitative evidence of the efficiency of our method in real applications.
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42

Forbes, Alistair B. "Uncertainty evaluation associated with fitting geometric surfaces to coordinate data." Metrologia 43, no. 4 (August 2006): S282—S290. http://dx.doi.org/10.1088/0026-1394/43/4/s16.

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43

Zhang, Zongliang, Jonathan Li, Yulan Guo, Xin Li, Yangbin Lin, Guobao Xiao, and Cheng Wang. "Robust procedural model fitting with a new geometric similarity estimator." Pattern Recognition 85 (January 2019): 120–31. http://dx.doi.org/10.1016/j.patcog.2018.07.027.

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44

Moroni, Giovanni, and Stefano Petrò. "Geometric tolerance evaluation: A discussion on minimum zone fitting algorithms." Precision Engineering 32, no. 3 (July 2008): 232–37. http://dx.doi.org/10.1016/j.precisioneng.2007.08.007.

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45

Chattopadhyay, Swarup, C. A. Murthy, and Sankar K. Pal. "Fitting truncated geometric distributions in large scale real world networks." Theoretical Computer Science 551 (September 2014): 22–38. http://dx.doi.org/10.1016/j.tcs.2014.05.003.

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46

Gofuku, Syuuichi, Shigehumi Tamura, Takeshi Kajiwara, and Takashi Maekawa. "3207 B-spline Fitting by Geometric Algorithm with Normal Constraints." Proceedings of Design & Systems Conference 2007.17 (2007): 307–8. http://dx.doi.org/10.1299/jsmedsd.2007.17.307.

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47

KINERI, Yuki, and Takashi MAEKAWA. "3307 Constrained B-spline surface fitting by iterative geometric algorithm." Proceedings of Design & Systems Conference 2012.22 (2012): _3307–1_—_3307–8_. http://dx.doi.org/10.1299/jsmedsd.2012.22._3307-1_.

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48

Higham, Desmond J., Marija Rašajski, and Nataša Pržulj. "Fitting a geometric graph to a protein–protein interaction network." Bioinformatics 24, no. 8 (March 14, 2008): 1093–99. http://dx.doi.org/10.1093/bioinformatics/btn079.

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49

Abdullin, Dinar, Gregor Hagelueken, Robert I. Hunter, Graham M. Smith, and Olav Schiemann. "Geometric model-based fitting algorithm for orientation-selective PELDOR data." Molecular Physics 113, no. 6 (September 17, 2014): 544–60. http://dx.doi.org/10.1080/00268976.2014.960494.

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50

Zhang, Chao, Xuequan Lu, Katsuya Hotta, and Xi Yang. "G2MF-WA: Geometric multi-model fitting with weakly annotated data." Computational Visual Media 6, no. 2 (April 2, 2020): 135–45. http://dx.doi.org/10.1007/s41095-020-0166-8.

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Abstract:
Abstract In this paper we address the problem of geometric multi-model fitting using a few weakly annotated data points, which has been little studied so far. In weak annotating (WA), most manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. SuchWA data can naturally arise through interaction in various tasks. For example, in the case of homography estimation, one can easily annotate points on the same plane or object with a single label by observing the image. Motivated by this, we propose a novel method to make full use of WA data to boost multi-model fitting performance. Specifically, a graph for model proposal sampling is first constructed using the WA data, given the prior that WA data annotated with the same weak label has a high probability of belonging to the same model. By incorporating this prior knowledge into the calculation of edge probabilities, vertices (i.e., data points) lying on or near the latent model are likely to be associated and further form a subset or cluster for effective proposal generation. Having generated proposals, a-expansion is used for labeling, and our method in return updates the proposals. This procedure works in an iterative way. Extensive experiments validate our method and show that it produces noticeably better results than state-of-the-art techniques in most cases.
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