Relevant bibliographies by topics / Gaussian filtering and smoothing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gaussian filtering and smoothing'

Author: Grafiati
Published: 3 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Gaussian filtering and smoothing"

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Imanuddin, Imanuddin, Raza Oktafian, and Munawir Munawir. "Image Smoothing Menggunakan Metode Mean Filtering." JOINTECS (Journal of Information Technology and Computer Science) 4, no. 2 (2019): 57. http://dx.doi.org/10.31328/jointecs.v4i2.1007.

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Pelembutan Citra (Image smoothing) bertujuan untuk menekan gangguan (noise) pada citra.Gangguan tersebut biasanya muncul sebagai akibat dari hasil penerokan yang tidak bagus (sensor noise, photographic grain noise) atau akibat saluran transmisi (pada pengiriman data).Penelitian ini telah menghasilkan sebuah program aplikasi untuk image smoothing dengan beberapa metode yaitu mean filtering,grayscale dan gaussian filtering. Citra uji yang digunakan pada penelitian ini menggunakan satu sampel gambar. Citra tersebut di-load dan ditampilkan pada program. Kemudian dilakuan proses image smoothing dengan menggunakan metode grayscale,gaussian dan mean.
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Peng, Anjie, Gao Yu, Yadong Wu, Qiong Zhang, and Xiangui Kang. "A Universal Image Forensics of Smoothing Filtering." International Journal of Digital Crime and Forensics 11, no. 1 (2019): 18–28. http://dx.doi.org/10.4018/ijdcf.2019010102.

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Digital image smoothing filtering operations, including the average filtering, Gaussian filtering and median filtering are always used to beautify the forged images. The detection of these smoothing operations is important in the image forensics field. In this article, the authors propose a universal detection algorithm which can simultaneously detect the average filtering, Gaussian low-pass filtering and median filtering. Firstly, the high-frequency residuals are used as being the feature extraction domain, and then the feature extraction is established on the local binary pattern (LBP) and the autoregressive model (AR). For the LBP model, the authors exploit that both of the relationships between the central pixel and its neighboring pixels and the relationships among the neighboring pixels are differentiated for the original images and smoothing filtered images. A method is further developed to reduce the high dimensionality of LBP-based features. Experimental results show that the proposed detector is effective in the smoothing forensics, and achieves better performance than the previous works, especially on the JPEG images.
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Deisenroth, Marc Peter, Ryan Darby Turner, Marco F. Huber, Uwe D. Hanebeck, and Carl Edward Rasmussen. "Robust Filtering and Smoothing with Gaussian Processes." IEEE Transactions on Automatic Control 57, no. 7 (2012): 1865–71. http://dx.doi.org/10.1109/tac.2011.2179426.

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Šimandl, Miroslav, and Jakub Královec. "Filtering, Prediction and Smoothing with Gaussian Sum Representation." IFAC Proceedings Volumes 33, no. 15 (2000): 1157–62. http://dx.doi.org/10.1016/s1474-6670(17)39910-x.

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Jeng, Yih-Nen, P. G. Huang, and You-Chi Cheng. "Decomposition of one-dimensional waveform using iterative Gaussian diffusive filtering methods." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2095 (2008): 1673–95. http://dx.doi.org/10.1098/rspa.2007.0031.

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The Gaussian smoothing method is shown to have a wide transition zone around the cut-off frequency selected to filter a given dataset. We proposed two iterative Gaussian smoothing methods to tighten the transition zone: one being approximately diffusive and the other being strictly diffusive. The first version smoothes repeatedly the remaining high-frequency parts and the second version requires an additional step to further smooth the resulting smoothed response in each of the smoothing operation. Based on the choice of the criterion for accuracy, the smoothing factor and the number of iterations are derived for an infinite data length in both methods. By contrast, for a finite-length data string, results of the interior points (sufficiently away from the two endpoints) obtained by both methods can be shown to exhibit an approximate diffusive property. The upper bound of the distance affected by the error propagation inward due to the lack of data beyond the two ends is numerically estimated. Numerical experiments also show that results of employing the iterative Gaussian smoothing method are almost the same as those obtained by the strict diffusive version, except that the error propagation distance induced by the latter is slightly deeper than that of the former. The proposed method has been successfully applied to decompose the wave formation of a number of test cases including two sets of real experimental data.
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Zhang, Qing, Hao Jiang, Yongwei Nie, and Wei-Shi Zheng. "Pyramid Texture Filtering." ACM Transactions on Graphics 42, no. 4 (2023): 1–11. http://dx.doi.org/10.1145/3592120.

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We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation---the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement. Code is available at https://rewindl.github.io/pyramid_texture_filtering/.
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Fujita, Shu, and Norishige Fukushima. "Hyperspectral Gaussian Filtering : Edge-Preserving Smoothing for Hyperspectral Image and Its Separable Acceleration." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 5–6. http://dx.doi.org/10.1299/jsmeicam.2015.6.5.

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Särkkä, Simo, and Juha Sarmavuori. "Gaussian filtering and smoothing for continuous-discrete dynamic systems." Signal Processing 93, no. 2 (2013): 500–510. http://dx.doi.org/10.1016/j.sigpro.2012.09.002.

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Guoyan, Wang, A. V. Fomichev, and Dy Yiran. "Research on Improved Gaussian Smoothing Filters for SLAM Application." Mekhatronika, Avtomatizatsiya, Upravlenie 20, no. 12 (2019): 756–64. http://dx.doi.org/10.17587/mau.20.756-764.

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To address the navigation issues of the planetary rover and construct a map for the unknown environment as well as the surface of the planets in our solar system, the simultaneous localization and mapping can be seen as an alternative method. In terms of the navigation with the laser sensor, the Kalman filter and its improving algorithms, such as EKF and UKF are widely used in the the process of processing information. Nevertheless, these filter algorithms suffer from low accuracy and significant computation expensive. The EKF algorithm has a linearization process, the UKF algorithm is better matched in a nonlinear system than the EKF algorithm, but it has more computational complexity. The GP-RTSS filtering algorithm, based on a Gaussian filter, is significantly superior to the EKF and UKF algorithms regarding the sensor fusion accuracy. The Gaussian Process can be used in different non-linear system, does not need prediction model and linearization. However, the main barrier in the process of implementing the GP-RTSS algorithm is that the Gaussian core function requires a lot of computation. In this paper, an algorithm, so-called DIS RTSS filter under a distributed computation scheme, derived from the GP-RTSS Gaussia n smoothing and filter, is proposed. The distributed system can effectively reduce the cost of computation (computation expense and memory). Moreover, four fusion methods for the DIS RTSS filter, i.e., DIS RTP, DIS RTGP, DIS RTB, DIS RTrB are discussed in this paper. The experiments show that among the four algorithms described above, the DIS RTGP algorithm is the most effective solution for practical implementation. The DIS RTSS filtering algorithm can realize a high processing rate and can theoretically process an infinite number of data samples.
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Cedeño, Angel L., Ricardo Albornoz, Rodrigo Carvajal, Boris I. Godoy, and Juan C. Agüero. "A Two-Filter Approach for State Estimation Utilizing Quantized Output Data." Sensors 21, no. 22 (2021): 7675. http://dx.doi.org/10.3390/s21227675.

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Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitalization process for storing or transmitting the measurements through a communication channel using a minimum amount of resources. Thus, obtaining suitable state estimates in this context is essential. In this paper, Gaussian sum filtering and smoothing algorithms are developed in order to deal with noisy measurements that are also subject to quantization. In this approach, the probability mass function of the quantized output given the state is characterized by an integral equation. This integral was approximated by using a Gauss–Legendre quadrature; hence, a model with a Gaussian mixture structure was obtained. This model was used to develop filtering and smoothing algorithms. The benefits of this proposal, in terms of accuracy of the estimation and computational cost, are illustrated via numerical simulations.
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Dissertations / Theses on the topic "Gaussian filtering and smoothing"

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Nguyen, Thi Ngoc Minh. "Lissage de modèles linéaires et gaussiens à régimes markoviens. : Applications à la modélisation de marchés de matières premières." Electronic Thesis or Diss., Paris, ENST, 2016. https://pastel.hal.science/tel-03689917.

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Les travaux présentés dans cette thèse portent sur l'analyse et l'application de méthodes de Monte Carlo séquentielles pour l'estimation de chaînes de Markov cachées. Ces méthodes sont utilisées pour approcher les lois conditionnelles des états cachés sachant les observations. Nous nous intéressons en particulier à la méthode des deux filtres qui permet d'estimer les lois marginales des états sachant les observations. Nous prouvons tout d'abord des résultats de convergence pour l'ensemble de ces méthodes sous des hypothèses faibles sur le modèle de Markov caché. En ajoutant des hypothèses de mélange fort, nous montrons que les constantes des inégalités de déviation ainsi que les variances asymptotiques sont uniformément majorées en temps. Nous nous intéressons par la suite à l'utilisation de ces modèles et de la méthode des deux filtres à la modélisation, l’inférence et la prédiction des marchés de matières premières. Les marchés sont modélisés par une extension du modèle de Gibson-Schwartz portant sur le prix spot et le convenience yield avec la dynamique de ces variables est contrôlée par une chaîne de Markov discrète cachée identifiant le régime dans lequel se trouve le marché. A chaque régime correspond un ensemble de paramètres régissant la dynamique des variables d'état, ce qui permet de définir différents comportements pour le prix spot et le convenience yield. Nous proposons un nouvel algorithme de type Expectation Maximization basé sur une méthode de deux-filtres pour approcher la loi postérieure des états cachés sachant des observations. Les performances de cet algorithme sont évaluées sur les données du pétrole brut du marché Chicago Mercantile Exchange<br>The work presented in this thesis focuses on Sequential Monte Carlo methods for general state space models. These procedures are used to approximate any sequence of conditional distributions of some hidden state variables given a set observations. We are particularly interested in two-filter based methods to estimate the marginal smoothing distribution of a state variable given past and future observations. We first prove convergence results for the estimators produced by all two-filter based Sequential Monte Carlo methods under weak assumptions on the hidden Markov model. Under additional strong mixing assumptions which are more restrictive but still standard in this context, we show that the constants of the deviation inequalities and the asymptotic variances are uniformly bounded in time. Then, a Conditionally Linear and Gaussian hidden Markov model is introduced to explain commodity markets regime shifts. The markets are modeled by extending the Gibson-Schwartz model on the spot price and the convenience yield. It is assumed that the dynamics of these variables is controlled by a discrete hidden Markov chain identifying the regimes. Each regime corresponds to a set of parameters driving the state space model dynamics. We propose a Monte Carlo Expectation Maximization algorithm to estimate the parameters of the model based on a two-filter method to approximate the intermediate quantity. This algorithm uses explicit marginalization (Rao Blackwellisation) of the linear states to reduce Monte Carlo variance. The algorithm performance is illustrated using Chicago Mercantile Exchange (CME) crude oil data
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Svensson, Christian. "Bayesian Filtering and Smoothing to Measure Damper Characteristics." Thesis, KTH, Maskinkonstruktion (Inst.), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-202293.

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During the development of high performance damper products for vehicle applications, it is essential to measure the dampers’ characteristics to ensure their performance. Some of the measurements are performed in dynamometers. The dynamometer actuates the damper with a predefined position signal and measures the actual position and the resulting force. These measurements suffer from noise; the problem is especially bad if the signals are studied with respect to velocity. This is due to that the velocity is not directly measurable; it must be calculated from position by differentiation, which amplifies the noise. The purpose of this work was to improve these measurements by doing acomparison of different Bayesian filters and smoothers. The comparison included the Kalman filter, the extended Kalman filter, the unscented Kalman filter, the Rauch-Tung-Striebel smoother, the extended Rauch-Tung-Striebel smoother and the unscented Rauch-Tung-Striebel smoother. The smoothers are only applicable in offline applications, while the filters could be used in real time. The filters reduced the noise in the position signal and greatly reduced the noise in the velocity signal. The smoothers showed the same behaviors as the filters, but with much less noise. Only small improvements were visible in the force signal.<br>Under utvecklingen av högpesterande dämparprodukter för fordonstillämpningar är det ett måste att mäta dämparnas karakteristik för att säkerställa deras prestanda. Vissa av mätningarna görs i dynamometrar. Dynamometern aktuerar dämparen med en fördefinierad positionssignal och mäter den faktiska positionen samt den resulterande kraften. Dessa mätningar påverkas av brus, problemet är extra tydligt om signalerna studeras med avseende på hastighet. Detta beror på att hastigheten inte är direkt mätbar, utan måste räknas ut genom derivering, vilken förstärker bruset. Syftet med detta arbete var att förbättra dessa mätningar genom att göra en jämförelse mellan olika Bayesiska filter och glättare. I jämförelsen ingick Kalmanfiltret, det utökade Kalmanfiltret, det oparfymerade Kalmanfiltret, Rauch-Tung-Stribel glättaren, den utökade Rauch-Tung-Striebelglättaren och den oparfymerade Rauch-Tung-Stribelglättaren. Glättarna kan endast användas offline, medan filternakan användas i realtid. Filterna minskade bruset i positionssignalen och minskade bruset avsevärt i hastighetssignalen. Glättarna uppvisade samma beteende som filterna, men med mycket mindre brus. Endast små förbättringar sågs i kraftsignalen.
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Bunch, Peter Joseph. "Particle filtering and smoothing for challenging time series models." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708151.

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Li, Jian-Cheng. "Generation of simulated ultrasound images using a Gaussian smoothing function." Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1179261418.

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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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Laverick, Kiarn T. "Quantum State Smoothing: General Properties and Applications to Linear Gaussian Systems." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/412411.

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Filtering and smoothing are classical estimation techniques that provide an estimate of the state of a classical system based on measurement information in the past (prior to the estimation time) and the past-future (prior and posterior to the estimation time), respectively. However, with the advent of quantum technologies, the need to estimate the state of an individual quantum system has also arisen. While the filtering technique is easily generalized and applied to quantum systems, it was not so simple for the smoothing technique. Applying the direct quantum analog of the smoothing theory often leads to an estimate that is unphysical, indicating that the classical theory is incompatible with quantum systems. The reason for this incompatibility is that the operators describing the future measurements on the system, called the retrofiltered effect, and the state conditioned on the past measurement, i.e., the filtered state, do not necessarily commute. One way to solve this issue is the quantum state smoothing theory. In order to deal with past and future information, the theory introduces the concept of a hidden measurement record, gathered by a secondary observer, say Bob, in order to define the true quantum state, a state containing maximal information about the system. With this concept of a true state, it is then possible to construct a valid smoothed quantum state, that is, a state conditioned on a past-future measurement record. In this thesis I delve into the quantum state smoothing theory. I begin by reformu-lating the quantum state smoothing theory as an optimal estimation problem, that is, minimizing a particular expected cost function. I show that the smoothed state is the optimal estimator for two cost functions, the trace-square deviation from and relative entropy with the true state. Additionally, I show, for a closely related cost function, the linear infidelity, that the smoothed state is suboptimal, while the pure state corre-sponding to the largest eigenvalue of the smoothed state is optimal. I then investigate under what conditions the smoothed quantum state reduces to a classically smoothed state, finding a sufficient condition. This sufficient condition requires the true state to be described probabilistically in a fixed basis. Subsequently, in an attempt to remove some of the restrictions on how Bob measures the system, I hypothesize a weaker suffi-cient condition of only requiring the filtered state and retrofiltered effect to be described probabilistically in a fixed basis. This hypothesis is disproven with a counter example. The remainder of the thesis is dedicated to a particular class of quantum system, the linear Gaussian quantum (LGQ) systems. I apply the quantum state smoothing theory to the LGQ systems, obtaining closed-form expressions for the smoothed quantum state.These closed-form expressions allow for numerous properties of the smoothed quantum state to be determined that would otherwise be arduous to even verify in the general setting. In particular, I investigate the behaviour of the smoothed quantum state in the low and high measurement efficiency limit. Furthermore, I derive a necessary and sufficient condition on the true state of the system that, in the event that Bob’s measurement is unknown, restricts the possible true states of the system based on the observer’s, say Alice’s, measurement choice. From the dynamical form of the LGQ state smoothing equations, I derive a necessary and sufficient condition for differentiable evolution of the smoothed quantum state. Lastly, I investigate the optimal measurement strategy for Alice and Bob in order to maximize the relative purity recovery. I pose three hypotheses and test the validity of each against two physical systems. One of these hypotheses provides an approximately optimal solution. To further verify this hypothesis, I generalize the hypothesis to qubit systems and test against an example system, again verifying the hypothesis.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Environment and Sc<br>Science, Environment, Engineering and Technology<br>Full Text
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Huber, Marco [Verfasser]. "Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications / Marco Huber." Karlsruhe : KIT-Bibliothek, 2015. http://d-nb.info/1068263369/34.

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Sancar, Yilmaz Aysun. "Edge Preserving Smoothing With Directional Consistency." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608511/index.pdf.

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Images may be degraded by some random error which is called noise. Noise may occur during image capture, transmission or processing and its elimination is achieved by smoothing filters. Linear smoothing filters blur the edges and edges are important characteristics of images and must be preserved. Various edge preserving smoothing filters are proposed in the literature. In this thesis, most common smoothing and edge preserving smoothing filters are discussed and a new method is proposed by modifying Ambrosio Tortorelli approximation of Mumford Shah Model. New method takes into edge direction consistency account and produces sharper results at comparable scales.
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Jansson, Patric. "Precise kinematic GPS positioning with Kalman filtering and smoothing : theory and applications /." Stockholm : Tekniska högsk, 1998. http://www.lib.kth.se/abs98/jans0520.pdf.

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Caputi, Mauro J. "NonGaussian estimation using a modified Gaussian sum adaptive filter." Diss., This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-07282008-135232/.

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Books on the topic "Gaussian filtering and smoothing"

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Kallianpur, G. White noise theory of prediction, filtering, and smoothing. Gordon and Breach Science Publishers, 1988.

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Krishnan, Venkatarama. Nonlinear Filtering and Smoothing: An Introduction to Martingales, Stochastic Integrals and Estimation. Dover Publications, Inc., 2005.

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Krishnan, Venkatarama. Nonlinear filtering and smoothing: An introduction to martingales, stochastic integrals, and estimation. Dover Publications, 2005.

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Le Jan, Y. (Yves), 1952- and Li X.-M. (Xue-Mei) 1964-, eds. The geometry of filtering. Birkhäuser, 2010.

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Nicklas, Richard B. An application of a Kalman Filter Fixed Interval Smoothing Algorithm to underwater target tracking. Naval Postgraduate School, 1989.

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Lipt͡ser, R. Sh. Statistics of random processes. 2nd ed. Edited by Shiri͡aev Alʹbert Nikolaevich. Springer, 2001.

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Bayesian Filtering and Smoothing. Cambridge University Press, 2023.

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Särkkä, Simo. Bayesian Filtering and Smoothing. Cambridge University Press, 2014.

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Särkkä, Simo. Bayesian Filtering and Smoothing. Cambridge University Press, 2013.

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Sarkka, Simo. Bayesian Filtering and Smoothing. Cambridge University Press, 2013.

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Book chapters on the topic "Gaussian filtering and smoothing"

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Paulus, Dietrich W. R., and Joachim Hornegger. "Filtering and Smoothing Signals." In Pattern Recognition of Images and Speech in C++. Vieweg+Teubner Verlag, 1997. http://dx.doi.org/10.1007/978-3-663-13991-1_19.

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Paulus, Dietrich W. R., and Joachim Hornegger. "Filtering and Smoothing Signals." In Pattern Recognition and Image Processing in C++. Vieweg+Teubner Verlag, 1995. http://dx.doi.org/10.1007/978-3-322-87867-0_19.

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Gu, Chong. "Regression with Gaussian-Type Responses." In Smoothing Spline ANOVA Models. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3683-0_3.

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Gu, Chong. "Regression with Gaussian-Type Responses." In Smoothing Spline ANOVA Models. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5369-7_3.

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Lüdtke, Stefan, Alejandro Molina, Kristian Kersting, and Thomas Kirste. "Gaussian Lifted Marginal Filtering." In KI 2019: Advances in Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30179-8_19.

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Wu, Yuanxin, Dewen Hu, Meiping Wu, and Xiaoping Hu. "Quasi-Gaussian Particle Filtering." In Computational Science – ICCS 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758501_92.

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van Schuppen, Jan H. "Filtering of Gaussian Systems." In Communications and Control Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66952-2_8.

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Eggermont, Paul P. B., and Vincent N. LaRiccia. "Kalman Filtering for Spline Smoothing." In Springer Series in Statistics. Springer New York, 2009. http://dx.doi.org/10.1007/b12285_9.

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Gómez, Víctor. "Wiener–Kolmogorov Filtering and Smoothing." In Multivariate Time Series With Linear State Space Structure. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28599-3_7.

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Fadali, M. Sami. "Prediction and Smoothing." In Introduction to Random Signals, Estimation Theory, and Kalman Filtering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8063-5_11.

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Conference papers on the topic "Gaussian filtering and smoothing"

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Tian, Yihan, Ce Li, Xuli Guo, and Zhongbo Jiang. "Gaussian Sliding Window Smoothing for Human Action Segmentation." In 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2024. http://dx.doi.org/10.1109/icsece61636.2024.10729446.

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Aravind, V. Lambodara, and C. Bagyalakshmi. "Underwater Image Enhancement Using Gaussian Smoothing and Edge Detection." In 2025 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64420.2025.11005112.

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Zhou, Jiachen, Daniel Frisch, and Uwe D. Hanebeck. "Inverse Gaussian Process Interpolation for High-Quality Assumed Gaussian Filtering." In 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2024. http://dx.doi.org/10.1109/mfi62651.2024.10705784.

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Giraldo-Grueso, Felipe, Andrey A. Popov, and Renato Zanetti. "Gaussian Mixture-Based Point Mass Filtering." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706279.

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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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Vats, Divyanshu, and Jose M. F. Moura. "Recursive filtering and smoothing for discrete index gaussian reciprocal processes." In 2009 43rd Annual Conference on Information Sciences and Systems (CISS). IEEE, 2009. http://dx.doi.org/10.1109/ciss.2009.5054749.

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Stevenson, Robert L. "Nonlinear filtering structure for smoothing discontinuous signals corrupted with Gaussian noise." In SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, edited by Edward R. Dougherty, Jaakko T. Astola, and Charles G. Boncelet, Jr. SPIE, 1992. http://dx.doi.org/10.1117/12.58378.

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Vats, Divyanshu, and Jose M. F. Moura. "Recursive filtering and smoothing for Gaussian reciprocal processes with continuous indices." In 2009 IEEE International Symposium on Information Theory - ISIT. IEEE, 2009. http://dx.doi.org/10.1109/isit.2009.5205710.

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Hartikainen, Jouni, and Simo Sarkka. "Kalman filtering and smoothing solutions to temporal Gaussian process regression models." In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2010. http://dx.doi.org/10.1109/mlsp.2010.5589113.

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Cao, Pengtao, Min Zhang, and Zhenchun Li. "Adaptive Filtering De-noising Method Based on Generalized S - Transform and Gaussian Smoothing." In International Geophysical Conference, Beijing, China, 24-27 April 2018. Society of Exploration Geophysicists and Chinese Petroleum Society, 2018. http://dx.doi.org/10.1190/igc2018-083.

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Reports on the topic "Gaussian filtering and smoothing"

1

Ito, Kazufumi, and Kaiqi Xiong. Gaussian Filters for Nonlinear Filtering Problems. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada453855.

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Papantoni-Kazakos, P., and Haralampos Tsaknakis. Outliner Resistant Filtering and Smoothing. 2D Version. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada193319.

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Chen, Stanley F., and Ronald Rosenfeld. A Gaussian Prior for Smoothing Maximum Entropy Models. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada360974.

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Elliott, Robert J. The Existence of Smooth Densities for the Prediction Filtering and Smoothing Problems. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada189865.

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Ahn, Hyungsok, and Raisa E. Feldman. Optimal Filtering of a Gaussian Signal in the Presence of Levy Noise. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada336874.

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Weyermuller, Scott P. A Pseudolite Close Proximity Static Position Solution Using Bias Kalman Filtering and Pseudorange Smoothing Techniques. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada387206.

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Sinpurwalla, Nozer D., and Jingxian Chen. Filtering, Smoothing, and Extrapolations in Dose-Response Experiments: With Application to Data on Respiratory Tumor in Rats. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada293968.

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Campodonico, Sylvia, and Jingxian Chen. A Computer Program for 'Filtering, Smoothing, Extrapolation in Dose-Response Experiments With Application to Data on Respiratory Tumor of Rats',. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada293862.

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