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Relevant bibliographies by topics / Gaussian Method

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Academic literature on the topic 'Gaussian Method'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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Journal articles on the topic "Gaussian Method"

1

Zickert, Gustav, and Can Evren Yarman. "Gaussian mixture model decomposition of multivariate signals." Signal, Image and Video Processing 16, no. 2 (2021): 429–36. http://dx.doi.org/10.1007/s11760-021-01961-y.

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AbstractWe propose a greedy variational method for decomposing a non-negative multivariate signal as a weighted sum of Gaussians, which, borrowing the terminology from statistics, we refer to as a Gaussian mixture model. Notably, our method has the following features: (1) It accepts multivariate signals, i.e., sampled multivariate functions, histograms, time series, images, etc., as input. (2) The method can handle general (i.e., ellipsoidal) Gaussians. (3) No prior assumption on the number of mixture components is needed. To the best of our knowledge, no previous method for Gaussian mixture m

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Hu, Jintong, Bin Xia, Bin Chen, Wenming Yang, and Lei Zhang. "GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 3554–62. https://doi.org/10.1609/aaai.v39i4.32369.

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Implicit neural representations (INRs) have revolutionized arbitrary-scale super-resolution (ASSR) by modeling images as continuous functions. Most existing INR-based ASSR networks first extract features from the given low-resolution image using an encoder, and then render the super-resolved result via a multi-layer perceptron decoder. Although these approaches have shown promising results, their performance is constrained by the limited representation ability of discrete latent codes in the encoded features. In this paper, we propose a novel ASSR method named GaussianSR that overcomes this li

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Gao, Lin, Jie Yang, Bo-Tao Zhang, et al. "Real-time Large-scale Deformation of Gaussian Splatting." ACM Transactions on Graphics 43, no. 6 (2024): 1–17. http://dx.doi.org/10.1145/3687756.

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Neural implicit representations, including Neural Distance Fields and Neural Radiance Fields, have demonstrated significant capabilities for reconstructing surfaces with complicated geometry and topology, and generating novel views of a scene. Nevertheless, it is challenging for users to directly deform or manipulate these implicit representations with large deformations in a real-time fashion. Gaussian Splatting (GS) has recently become a promising method with explicit geometry for representing static scenes and facilitating high-quality and real-time synthesis of novel views. However, it can

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Muhammad Ali, Muhammad. "Parallel Gaussian Elimination Method." AL-Rafidain Journal of Computer Sciences and Mathematics 5, no. 2 (2008): 59–77. http://dx.doi.org/10.33899/csmj.2008.163986.

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Babich, V. M., and M. M. Popov. "Gaussian summation method (review)." Radiophysics and Quantum Electronics 32, no. 12 (1989): 1063–81. http://dx.doi.org/10.1007/bf01038632.

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Yu, Zehao, Torsten Sattler, and Andreas Geiger. "Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes." ACM Transactions on Graphics 43, no. 6 (2024): 1–13. http://dx.doi.org/10.1145/3687937.

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Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time. However, leveraging 3D Gaussians for surface reconstruction poses significant challenges due to the explicit and disconnected nature of 3D Gaussians. In this work, we present Gaussian Opacity Fields (GOF), a novel approach for efficient, high-quality, and adaptive surface reconstruction in unbounded scenes. Our GOF is derived from ray-tracing-based volume rendering of 3D Gaussians, enabling direct geometry extraction from 3D Gauss

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Koga, Toshikatsu, and Ajit J. Thakkar. "Improvement of the long-range behavior of Gaussian basis sets using asymptotic constraints." Canadian Journal of Chemistry 70, no. 2 (1992): 362–65. http://dx.doi.org/10.1139/v92-051.

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It is suggested that atomic orbitals with improved long-range behavior can be obtained by using energy-optimized Gaussian basis sets to which Gaussians have been added to satisfy a subset of some recently derived constraints that must be satisfied by the exact Hartree–Fock orbitals. This procedure is demonstrated by illustrative calculations for helium. This method is found to be superior to the adhoc method of adding diffuse Gaussians in an even-tempered fashion. Keywords: Gaussian basis sets, long-range behavior, asymptotic constraints.

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Zhou, Jingqiu, Lue Fan, Xuesong Chen, Linjiang Huang, Si Liu, and Hongsheng Li. "GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10788–96. https://doi.org/10.1609/aaai.v39i10.33172.

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In this paper, we present GaussianPainter, the first method to paint a point cloud into 3D Gaussians given a reference image. GaussianPainter introduces an innovative feed-forward approach to overcome the limitations of time-consuming test-time optimization in 3D Gaussian splatting. Our method addresses a critical challenge in the field: the non-uniqueness problem inherent in the large parameter space of 3D Gaussian splatting. This space, encompassing rotation, anisotropic scales, and spherical harmonic coefficients, introduces the challenge of rendering similar images from substantially diffe

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Xiang, Zhi-Zheng, Chun Xie, and Itaru Kitahara. "BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting." Applied Sciences 15, no. 12 (2025): 6881. https://doi.org/10.3390/app15126881.

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We present a method for enhancing 3D Gaussian Splatting primitives with brushstroke-aware stylization. Previous approaches to 3D style transfer are typically limited to color or texture modifications, lacking an understanding of artistic shape deformation. In contrast, we focus on individual 3D Gaussian primitives, exploring their potential to enable style transfer that incorporates both color- and brushstroke-inspired local geometric stylization. Specifically, we introduce additional texture features for each Gaussian primitive and apply a texture mapping technique to achieve brushstroke-like

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Li, Xiaoqing, and Xiaoling Ji. "Complex Gaussian functions expansion method applied to truncated Gaussian beams." Journal of Modern Optics 58, no. 12 (2011): 1060–64. http://dx.doi.org/10.1080/09500340.2011.595835.

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Dissertations / Theses on the topic "Gaussian Method"

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Zhao, Yong. "Ensemble Kalman filter method for Gaussian and non-Gaussian priors /." Access abstract and link to full text, 2008. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/3305718.

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Menard, Kenneth A. "Gaussian beam resonator formalism using the yy method." Master's thesis, University of Central Florida, 1995. http://digital.library.ucf.edu/cdm/ref/collection/RTD/id/21214.

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University of Central Florida College of Engineering Thesis<br>A simple and powerful new paraxial ray formalism is shown to provide an alternate method for designing Gaussian Beam Resonators. The theory utilizes the Delano yybar diagram approach and is an extensio of the recent work by Shack and Kessler for laser systems. The method is shown to be complementary to the conventional ABCD method and is founded upon J.A. Arnaud's pioneering ideas for complex rays. The thesis develops an analytic formulation of a ray based complex wavefront curvature and yields a clearly generalized description

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Rosato, Andrea. "A Gaussian Process Learning Method for Nonlinear Optimal Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22463/.

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This thesis is focused on discrete-time nonlinear optimal control techniques enhanced via a supervised learning approach based on Gaussian Process regression. Since optimal control strategies are strongly model-based, a perfect knowledge of the real system is required in order to obtain the best performances; however, it is not always possible to satisfy these requirements, since model uncertainties due to, e.g., unavailable information or hard to model dynamical effects may be present leading to a suboptimal solution for the real problem. The basic idea is to exploit measurement data to reduc

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Xu, Zhiguang. "Modeling Non-Gaussian Time-correlated Data Using Nonparametric Bayesian Method." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406068732.

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Tye, Thomas N. "Application of digital signal processing methods to very high frequency omnidirectional range (VOR) signals in the design of an airborne flight measurement system." Ohio University / OhioLINK, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177702951.

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Patil, Dhiraj Dilip. "On the simulations of correlated nakagami-m fading channels using sum-of-sinusoids method." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4521.

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Thesis (M.S.) University of Missouri-Columbia, 2006.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 27, 2007) Includes bibliographical references.

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Hogan, John. "Constitutive modelling of elastomers using the finite element method." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322740.

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Källman, Erik. "Quantum Dynamics Simulations Using the Standard Matching Pursuit Gaussian Wavepacket Method : Practical Considerations." Thesis, Uppsala universitet, Teoretisk kemi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-228505.

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In any quantum dynamics method that approximates wave functions as a linearly combined basis set, non-orthogonality can be is a problem. It has been proven in previous studies that, by using the most standard form of Matching Pursuit in combination with a Gaussian wave packet ansatz, exact quantum-mechanical correspondence can be obtained for particle tunneling in one and two dimensions. This study is an attempt to prove that this approach can be generally applicable to systems of arbitrary dimension propagating with an an-harmonic potential, and that adaptive initial state sampling can be use

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Tirler, Günter, Peter Dalgaard, Wolfgang Hörmann, and Josef Leydold. "An Error in the Kinderman-Ramage Method and How to Fix It." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/218/1/document.pdf.

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An error in the Gaussian random variate generator by Kinderman and Ramage is described that results in the generation of random variates with an incorrect distribution. An additional statement that corrects the original algorithm is given.<br>Series: Preprint Series / Department of Applied Statistics and Data Processing

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Webb, Grayson. "A Gaussian Mixture Model based Level Set Method for Volume Segmentation in Medical Images." Thesis, Linköpings universitet, Beräkningsmatematik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148548.

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This thesis proposes a probabilistic level set method to be used in segmentation of tumors with heterogeneous intensities. It models the intensities of the tumor and surrounding tissue using Gaussian mixture models. Through a contour based initialization procedure samples are gathered to be used in expectation maximization of the mixture model parameters. The proposed method is compared against a threshold-based segmentation method using MRI images retrieved from The Cancer Imaging Archive. The cases are manually segmented and an automated testing procedure is used to find optimal parameters f

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Books on the topic "Gaussian Method"

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Neal, Radford M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. University of Toronto, 1997.

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Hohmann, Andreas. Inexact Gauss Newton methods for parameter dependent nonlinear problems. Shaker, 1994.

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Ewald, Schomig, and United States. National Aeronautics and Space Administration., eds. A new approach to mixed H2/H [infinity]-controller synthesis using gradient-based parameter optimization method: Final technical report ... for the period of March 15, 1900 to March 14, 1993. National Aeronautics and Space Administration, 1993.

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Ewald, Schomig, and United States. National Aeronautics and Space Administration., eds. A new approach to mixed H2/H [infinity]-controller synthesis using gradient-based parameter optimization method: Final technical report ... for the period of March 15, 1900 to March 14, 1993. National Aeronautics and Space Administration, 1993.

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International Conference on Computational Mathematics. The International Conference on Computational Mathematics: Proceedings. ICM&MG, 2002.

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Kovvali, Narayan. Theory and Applications of Gaussian Quadrature Methods. Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01517-5.

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David, Middleton. Non-Gaussian statistical communication theory. Wiley-IEEE Press, 2012.

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Hendeby, Gustaf. Fundamental estimation and detection limits in linear non-gaussian systems. Linköpings universitet, 2005.

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Piterbarg, V. I. Asymptotic methods in the theory of Gaussian processes and fields. American Mathematical Society, 1996.

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Poirier, Raymond. Handbook of Gaussian basis sets: A compendium for Ab-initio molecular orbital calculations. Elsevier, 1985.

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Book chapters on the topic "Gaussian Method"

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Grabe, Michael. "Method of Least Squares." In Generalized Gaussian Error Calculus. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03305-6_8.

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Bauerschmidt, Roland, David C. Brydges, and Gordon Slade. "Gaussian Fields." In Introduction to a Renormalisation Group Method. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9593-3_2.

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Peterson, Andrew F., and Malcolm M. Bibby. "Generalized Gaussian Quadrature." In An Introduction to the Locally-Corrected Nyström Method. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01710-0_5.

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Colliac, Jean-Philippe. "Gaussian Optics." In Intraocular Lens Calculations. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-50666-6_3.

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AbstractThe paraxial or Gaussian approximation in geometric optics simplifies the calculation of the optical properties of the human eye. In this chapter the analytical equations that allow the calculation of the cardinal elements of an optical system are explained in detail. Determining the focal length, the principal planes and the nodal points of the eye will permit calculating the position and magnification of the image. The matrix method is an alternative way to perform paraxial ray tracing and also calculate the Gaussian cardinal elements of an optical system. These foundations lead to t

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Evensen, Geir, Femke C. Vossepoel, and Peter Jan van Leeuwen. "Maximum a Posteriori Solution." In Springer Textbooks in Earth Sciences, Geography and Environment. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96709-3_3.

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AbstractWe will now introduce a fundamental approximation used in most practical data-assimilation methods, namely the definition of Gaussian priors. This approximation simplifies the Bayesian posterior, which allows us to compute the maximum a posteriori (MAP) estimate and sample from the posterior pdf. This chapter will introduce the Gaussian approximation and then discuss the Gauss–Newton method for finding the MAP estimate. This method is the starting point for many of the data-assimilation algorithms discussed in the following chapters.

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Kusuoka, Shigeo. "Gaussian K-scheme: justification for KLNV method." In Advances in Mathematical Economics. Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54324-4_3.

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Frye, D., G. C. Lie, S. J. Chakravorty, A. Preiskorn, and E. Clementi. "Hylleraas Configuration Interaction Method using Gaussian Functions." In Modern Techniques in Computational Chemistry: MOTECC™-89. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-010-9057-5_5.

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An, Qinli, Jianfeng Chen, and Zhonghai Yin. "Cellular Iterative Position Method with Gaussian Noise." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46981-1_13.

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Hao, Jianying, Yue Liu, and Dongdong Weng. "A BRDF Representing Method Based on Gaussian Process." In Computer Vision - ACCV 2014 Workshops. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16631-5_40.

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Iwasaki, Kei. "Efficient Image-Based Rendering Method Using Spherical Gaussian." In Mathematics for Industry. Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55007-5_6.

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Conference papers on the topic "Gaussian Method"

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Yang, Shangdong, and Jue Wu. "Gaussian Blur Optimization Method: Linear Texture Filtering Under Gaussian Function Separation." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906123.

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Wang, Yiyang, Weiyu Zeng, and Bin Qin. "AD-Gaussian: an Audio Driven Face Synthesis Method Leveraging Differential Attention and 3D Gaussian Splatting." In 2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC). IEEE, 2024. https://doi.org/10.1109/icairc64177.2024.10900108.

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Kim, Sieun, Kyungjin Lee, and Youngki Lee. "Color-cued Efficient Densification Method for 3D Gaussian Splatting." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00082.

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Wang, Jianming, and Penglang Shui. "Outlier-robust parameter estimation method of compound-Gaussian clutter with inverse Gaussian texture based on truncated moments." In Sixteenth International Conference on Signal Processing Systems (ICSPS 2024), edited by Robert Minasian and Li Chai. SPIE, 2025. https://doi.org/10.1117/12.3060370.

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Le Loc’h, G., H. Beucher, A. Galli, and B. Doligez. "Improvement In The Truncated Gaussian Method: Combining Several Gaussian Functions." In ECMOR IV - 4th European Conference on the Mathematics of Oil Recovery. European Association of Geoscientists & Engineers, 1994. http://dx.doi.org/10.3997/2214-4609.201411149.

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Li, Hui, Bo Feng, and Huazhong Wang. "Efficient Gaussian packet migration method using characteristic Gaussian packet frames." In SEG Technical Program Expanded Abstracts 2013. Society of Exploration Geophysicists, 2013. http://dx.doi.org/10.1190/segam2013-1182.1.

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Balbuena Ortega, A., M. L. Arroyo Carrasco, J. A. Dávila Pintle, M. M. Méndez Otero, and M. D. Iturbe Castillo. "New method to characterize Gaussian beams." In International Commission for Optics (ICO 22), edited by Ramón Rodríguez-Vera and Rufino Díaz-Uribe. SPIE, 2011. http://dx.doi.org/10.1117/12.902192.

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Zhou, Shuo, Xinliang Wu, Miaomiao He, Lei Ren, and Yunfei Guo. "Gaussian Process based Tracklets Association Method." In 2021 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2021. http://dx.doi.org/10.1109/iccais52680.2021.9624483.

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Tian, Ying, and Wenli Du. "Initial-Parameter-Criterion Based Gaussian Mixture Model Monitoring Method for Non-Gaussian Process." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8483586.

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Yang, Bo, and Yingyong Bu. "A Novel Gaussian Kernel Paramter Choosing Method." In 2009 Third International Symposium on Intelligent Information Technology Application. IEEE, 2009. http://dx.doi.org/10.1109/iita.2009.170.

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Reports on the topic "Gaussian Method"

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Tan, Cheng-Yang. Calculating emittance for Gaussian and Non-Gaussian distributions by the method of correlations for slits. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/892489.

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Rans, Richard. PR-352-15600-Z01 USM Uncertainty Estimate From Diagnostics. Pipeline Research Council International, Inc. (PRCI), 2020. http://dx.doi.org/10.55274/r0010919.

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A method to calculate a common set of Ultrasonic meter Gaussian quadrature �0.809R/�0.309R velocity diagnostics has been developed for multi-path Ultrasonic meters with Gaussian and proprietary path configurations. An uncertainty model has been developed using the changes between the meter's �0.809R/�0.309R calibration velocity diagnostics and the meter's �0.809R/�0.309R first flow/recalibration velocity diagnostics to estimate a Maximum Gaussian Risk/Probable Gaussian Risk calibration transfer uncertainty.

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Sun, Hao. A PENALIZED H-LIKELIHOOD METHOD FOR GAUSSIAN SPATIAL ADDITIVE MODEL ON REGULAR LATTICE. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-55.

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Dai, Fan. Matrix--free Likelihood Method for Exploratory Factor Analysis with High-dimensional Gaussian Data. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-993.

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Li, Baisong, and Bo Xu. PR-469-19604-Z01 Auto Diagnostic Method Development for Ultrasonic Flow Meter. Pipeline Research Council International, Inc. (PRCI), 2022. http://dx.doi.org/10.55274/r0012204.

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The objectives of this research are to develop methods for performing ultrasonic flow meter (USM) diagnostic evaluation automatically and a software tool with all necessary attachments. USM-based diagnostics have been established and thirteen categories of knowledge rules of existing cases have been learned and integrated. A search engine for relevant standards, specifications, and other documents of the measurement system has been developed, which enables the free search of text content. Further, with the assistance of modern reasoning techniques, the authorized user only needs to configure a

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Wilkerson, Stephen A. A Method for the Calculation of Abscissas and Weight Factors Using Gaussian Integration for Integrands with a Logarithmic Singularity. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada199341.

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Weber, M. H. Application of the Gaussian beam method to refraction data from the subduction zone beneath Vancouver Island and the North American Mainland. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/129015.

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Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.

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Recently the statistical characterizations of ﬁnancial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We ﬁnd that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matr

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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest

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Hale, D. Migration by the Kirchhoff, slant stack, and Gaussian beam methods. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7115487.

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