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Journal articles on the topic 'Kernel filtering'

Author: Grafiati

Published: 6 September 2023

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1

Maeda, Yoshihiro, Norishige Fukushima, and Hiroshi Matsuo. "Taxonomy of Vectorization Patterns of Programming for FIR Image Filters Using Kernel Subsampling and New One." Applied Sciences 8, no. 8 (July 26, 2018): 1235. http://dx.doi.org/10.3390/app8081235.

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This study examines vectorized programming for finite impulse response image filtering. Finite impulse response image filtering occupies a fundamental place in image processing, and has several approximated acceleration algorithms. However, no sophisticated method of acceleration exists for parameter adaptive filters or any other complex filter. For this case, simple subsampling with code optimization is a unique solution. Under the current Moore’s law, increases in central processing unit frequency have stopped. Moreover, the usage of more and more transistors is becoming insuperably complex due to power and thermal constraints. Most central processing units have multi-core architectures, complicated cache memories, and short vector processing units. This change has complicated vectorized programming. Therefore, we first organize vectorization patterns of vectorized programming to highlight the computing performance of central processing units by revisiting the general finite impulse response filtering. Furthermore, we propose a new vectorization pattern of vectorized programming and term it as loop vectorization. Moreover, these vectorization patterns mesh well with the acceleration method of subsampling of kernels for general finite impulse response filters. Experimental results reveal that the vectorization patterns are appropriate for general finite impulse response filtering. A new vectorization pattern with kernel subsampling is found to be effective for various filters. These include Gaussian range filtering, bilateral filtering, adaptive Gaussian filtering, randomly-kernel-subsampled Gaussian range filtering, randomly-kernel-subsampled bilateral filtering, and randomly-kernel-subsampled adaptive Gaussian filtering.

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Nair, Pravin, and Kunal Narayan Chaudhury. "Fast High-Dimensional Kernel Filtering." IEEE Signal Processing Letters 26, no. 2 (February 2019): 377–81. http://dx.doi.org/10.1109/lsp.2019.2891879.

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Douma, Huub, David Yingst, Ivan Vasconcelos, and Jeroen Tromp. "On the connection between artifact filtering in reverse-time migration and adjoint tomography." GEOPHYSICS 75, no. 6 (November 2010): S219—S223. http://dx.doi.org/10.1190/1.3505124.

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Finite-frequency sensitivity kernels in seismic tomography define the volumes inside the earth that influence seismic waves as they traverse through it. It has recently been numerically observed that an image obtained using the impedance kernel is much less contaminated by low-frequency artifacts due to the presence of sharp wave-speed contrasts in the background model, than is an image obtained using reverse-time migration. In practical reverse-time migration, these artifacts are routinely heuristically dampened by Laplacian filtering of the image. Here we show analytically that, for an isotropic acoustic medium with constant density, away from sources and receivers and in a smooth background medium, Laplacian image filtering is identical to imaging with the impedance kernel. Therefore, when imaging is pushed toward using background models with sharp wave-speed contrasts, the impedance kernel image is less prone to develop low-frequency artifacts than is the reverse-time migration image, due to the implicit action of the Laplacian that amplifies the higher-frequency reflectors relative to the low-frequency artifacts. Thus, the heuristic Laplacian filtering commonly used in practical reverse-time migration is fundamentally rooted in adjoint tomography and, in particular, closely connected to the impedance kernel.

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Huang, Di, Xishan Zhang, Rui Zhang, Tian Zhi, Deyuan He, Jiaming Guo, Chang Liu, et al. "DWM: A Decomposable Winograd Method for Convolution Acceleration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4174–81. http://dx.doi.org/10.1609/aaai.v34i04.5838.

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Winograd's minimal filtering algorithm has been widely used in Convolutional Neural Networks (CNNs) to reduce the number of multiplications for faster processing. However, it is only effective on convolutions with kernel size as 3x3 and stride as 1, because it suffers from significantly increased FLOPs and numerical accuracy problem for kernel size larger than 3x3 and fails on convolution with stride larger than 1. In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions. DWM decomposes kernels with large size or large stride to several small kernels with stride as 1 for further applying Winograd method, so that DWM can reduce the number of multiplications while keeping the numerical accuracy. It enables the fast exploring of larger kernel size and larger stride value in CNNs for high performance and accuracy and even the potential for new CNNs. Comparing against the original Winograd, the proposed DWM is able to support all kinds of convolutions with a speedup of ∼2, without affecting the numerical accuracy.

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Yijie Tang, Yijie Tang, Guobing Qian Yijie Tang, Wenqi Wu Guobing Qian, and Ying-Ren Chien Wenqi Wu. "An Efficient Filtering Algorithm against Impulse Noise in Communication Systems." 網際網路技術學刊 24, no. 2 (March 2023): 357–62. http://dx.doi.org/10.53106/160792642023032402014.

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<p>The kernel adaptive filter (KAF), which processes data in the reproducing kernel Hilbert space (RKHS), can improve the performance of conventional adaptive filters in nonlinear systems. However, the presence of impulse noise can seriously degrade the performance of KAF. In this paper, we propose a kernel modified-sign least-mean-square algorithm (KMSLMS) to mitigate the impact of impulse noise in communication systems. Moreover, we apply the nearest-instance-centroid estimation (NICE) algorithm to reduce the computational complexity of our KMSLMS algorithm, called the NICE-KMSLMS algorithm. Finally, computer simulations were used to evaluate the effectiveness of our proposed method. Compared with the conventional kernel least-mean-square algorithm (KLMS), our proposed method can improve the testing mean-squared error (MSE) by 2.32 dB and 7.39 dB for the nonlinear channel equalization and Mackey-Glass chaotic time series prediction problems, respectively. Furthermore, the testing MSE degradation caused by combining the NICE algorithm with our KMSLMS algorithm is negligible but can save about 55% computational cost in terms of the required mean size.</p> <p> </p>

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Cheng, Sheng-Wei, Yi-Ting Lin, and Yan-Tsung Peng. "A Fast Two-Stage Bilateral Filter Using Constant Time O(1) Histogram Generation." Sensors 22, no. 3 (January 25, 2022): 926. http://dx.doi.org/10.3390/s22030926.

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Bilateral Filtering (BF) is an effective edge-preserving smoothing technique in image processing. However, an inherent problem of BF for image denoising is that it is challenging to differentiate image noise and details with the range kernel, thus often preserving both noise and edges in denoising. This letter proposes a novel Dual-Histogram BF (DHBF) method that exploits an edge-preserving noise-reduced guidance image to compute the range kernel, removing isolated noisy pixels for better denoising results. Furthermore, we approximate the spatial kernel using mean filtering based on column histogram construction to achieve constant-time filtering regardless of the kernel radius’ size and achieve better smoothing. Experimental results on multiple benchmark datasets for denoising show that the proposed DHBF outperforms other state-of-the-art BF methods.

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Liu, Ning, and Thomas Schumacher. "Improved Denoising of Structural Vibration Data Employing Bilateral Filtering." Sensors 20, no. 5 (March 5, 2020): 1423. http://dx.doi.org/10.3390/s20051423.

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With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based on the Gaussian filter, the method constructs a bilateral filtering kernel function by multiplying the spatial proximity Gaussian kernel function and the numerical similarity Gaussian kernel function and replaces the current data with the data obtained by weighting the neighborhood data, thereby implementing filtering. By processing the simulated data and experimental data, introducing a time-frequency analysis method and a method for calculating the time-frequency spectrum energy, the denoising abilities of median filtering, wavelet denoising and bilateral filtering were compared. The results show that the bilateral filtering method can better preserve the details of the effective signal while suppressing the noise interference and effectively improve the data quality for structural damage detection. The effectiveness and feasibility of the bilateral filtering method applied to the noise suppression of vibration signals is verified.

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8

CHEN, Xiao-li, and Pei-yu LIU. "Word sequence kernel applied in spam-filtering." Journal of Computer Applications 31, no. 3 (May 18, 2011): 698–701. http://dx.doi.org/10.3724/sp.j.1087.2011.00698.

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9

Nan, Shanghan, and Guobing Qian. "Univariate kernel sums correntropy for adaptive filtering." Applied Acoustics 184 (December 2021): 108316. http://dx.doi.org/10.1016/j.apacoust.2021.108316.

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10

Sun, Zhonggui, Bo Han, Jie Li, Jin Zhang, and Xinbo Gao. "Weighted Guided Image Filtering With Steering Kernel." IEEE Transactions on Image Processing 29 (2020): 500–508. http://dx.doi.org/10.1109/tip.2019.2928631.

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Vestal, Brian E., Nichole E. Carlson, and Debashis Ghosh. "Filtering spatial point patterns using kernel densities." Spatial Statistics 41 (March 2021): 100487. http://dx.doi.org/10.1016/j.spasta.2020.100487.

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12

Liu, Bo, Boujemaa Ait-El-Fquih, and Ibrahim Hoteit. "Efficient Kernel-Based Ensemble Gaussian Mixture Filtering." Monthly Weather Review 144, no. 2 (February 1, 2016): 781–800. http://dx.doi.org/10.1175/mwr-d-14-00292.1.

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Abstract The Bayesian filtering problem for data assimilation is considered following the kernel-based ensemble Gaussian mixture filtering (EnGMF) approach introduced by Anderson and Anderson. In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution is analyzed. Then the focus is on two aspects: (i) the efficient implementation of EnGMF with (relatively) small ensembles, where a new deterministic resampling strategy is proposed preserving the first two moments of the posterior GM to limit the sampling error; and (ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.

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13

Selvi, Oguz. "A note on digital filtering with the second moment norm." GEOPHYSICS 62, no. 4 (July 1997): 1315–20. http://dx.doi.org/10.1190/1.1444233.

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The linear inverse method developed by Backus and Gilbert (1968) relates model estimates to actual earth models by use of a resolving kernel. Seismic source wavelet deconvolution can be treated within the framework of the Backus and Gilbert (1968) inverse theory as presented in Oldenburg (1981) and Treitel and Lines (1982). The model of the Backus and Gilbert theory is the ground impulse response, the mapping kernel is the source wavelet, and the resolving kernel is the convolution between the source wavelet and the shaping filter. Backus and Gilbert formalism introduces several measures for the resolving kernel.

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Zhao, Zhiqiang, Ping Feng, Jingjuan Guo, Caihong Yuan, Tianjiang Wang, Fang Liu, Zhijian Zhao, Zongmin Cui, and Bin Wu. "A hybrid tracking framework based on kernel correlation filtering and particle filtering." Neurocomputing 297 (July 2018): 40–49. http://dx.doi.org/10.1016/j.neucom.2018.02.043.

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15

Rafajłowicz, Ewaryst, Mirosław Pawlak, and Angsar Steland. "Nonlinear Image Processing and Filtering: A Unified Approach Based on Vertically Weighted Regression." International Journal of Applied Mathematics and Computer Science 18, no. 1 (March 1, 2008): 49–61. http://dx.doi.org/10.2478/v10006-008-0005-z.

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Nonlinear Image Processing and Filtering: A Unified Approach Based on Vertically Weighted RegressionA class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving properties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression estimates utilizing the concept of vertical weighting. The method unifies a number of known nonlinear image filtering and denoising algorithms such as bilateral and steering kernel filters. It is shown that vertically weighted filters can be realized by a structure of three interconnected radial basis function (RBF) networks. We also assess the performance of the algorithm by studying industrial images.

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Wu, Jie, Zuren Feng, and Zhigang Ren. "Improved structure-adaptive anisotropic filter based on a nonlinear structure tensor." Cybernetics and Information Technologies 14, no. 1 (March 1, 2014): 112–27. http://dx.doi.org/10.2478/cait-2014-0009.

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Abstract A variety of structure-adaptive filters are proposed to overcome the blurred effects of image structures caused by the classical Gaussian weighted mean filter. However, two major issues are needed to be dealt with carefully for structure-adaptive anisotropic filters. One is to properly construct the filter kernel and the other is to accurately estimate the orientation of the image structures. In this paper we propose to improve the structure-adaptive anisotropic filtering approach based on the nonlinear structure tensor (NLST) analysis technique. According to the anisotropism measurements of image structures, a new kernel construction method is designed to make the filter shape fine adapted to image features. Through the accurately estimated orientation of the image structures, the filter kernels are then properly aligned to perform the filtering process. Experimental results show that the proposed filter denoises the noisy images carefully and image features, such as corners and junctions are well preserved. Compared with some other known filters, the proposed filter obtains great improvements both in Mean Square Error (MSE) and visual quality.

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17

Stock, Michiel, Tapio Pahikkala, Antti Airola, Bernard De Baets, and Willem Waegeman. "A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression." Neural Computation 30, no. 8 (August 2018): 2245–83. http://dx.doi.org/10.1162/neco_a_01096.

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Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

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18

Park, Moon-Ghu, Ho-Cheol Shin, and Eun-Ki Lee. "KERNEL-BASED NOISE FILTERING OF NEUTRON DETECTOR SIGNALS." Nuclear Engineering and Technology 39, no. 6 (December 31, 2007): 725–30. http://dx.doi.org/10.5516/net.2007.39.6.725.

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Smadi, Ahmad AL, Shuyuan Yang, Atif Mehmood, Ahed Abugabah, Min Wang, and Muzaffar Bashir. "Smart pansharpening approach using kernel‐based image filtering." IET Image Processing 15, no. 11 (May 18, 2021): 2629–42. http://dx.doi.org/10.1049/ipr2.12251.

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20

Santamaria, Ignatio. "Kernel Adaptive Filtering: A Comprehensive Introduction [Book Review." IEEE Computational Intelligence Magazine 5, no. 3 (August 2010): 52–55. http://dx.doi.org/10.1109/mci.2010.937324.

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21

Gao Meifeng, 高美凤, and 张晓玄 Zhang Xiaoxuan. "Scale Adaptive Kernel Correlation Filtering for Target Tracking." Laser & Optoelectronics Progress 55, no. 4 (2018): 041501. http://dx.doi.org/10.3788/lop55.041501.

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22

Kumar, Deepak, and Rahul Kumar. "Spam Filtering using SVM with different Kernel Functions." International Journal of Computer Applications 136, no. 5 (February 17, 2016): 16–23. http://dx.doi.org/10.5120/ijca2016908395.

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23

Banerjee, Amit, and Philippe Burlina. "Efficient Particle Filtering via Sparse Kernel Density Estimation." IEEE Transactions on Image Processing 19, no. 9 (September 2010): 2480–90. http://dx.doi.org/10.1109/tip.2010.2047667.

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Chen, Kewei, Stefan Werner, Anthony Kuh, and Yih-Fang Huang. "Nonlinear Adaptive Filtering With Kernel Set-Membership Approach." IEEE Transactions on Signal Processing 68 (2020): 1515–28. http://dx.doi.org/10.1109/tsp.2020.2975370.

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25

Du, Juan, Wen Long Zhang, and Meng Meng Xie. "Research of a New SVM Kernel Function." Applied Mechanics and Materials 543-547 (March 2014): 1659–62. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1659.

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The kernel was the key technology of SVM; the kernel affected the learning ability and generalization ability of support vector machine. Aiming at the specific application of harmful text information recognition, combining traditional kernel function the paper structured a new combination kernel, modeling for the independent harmful vocabulary and co-occur vocabularies, and then evaluation the linear kernel, homogeneous polynomial kernel, non homogeneous polynomial kernel and combination kernel function in the sample experiment. The experimental results of combination kernel function showed that the effect has increased greatly than other kernel functions for the application of harmful text information filtering. Especially the Rcall value achieved satisfactory results.

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Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton, and Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models." Machine Learning 109, no. 5 (January 2, 2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.

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AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic models (or simulation models) available for some parts of a graphical model; this is in particular true in scientific fields where “models” are the central topic of study. Our contribution in this paper is to introduce a novel approach, termed the model-based kernel sum rule (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized probabilistic rules, one can develop various algorithms for hybrid (i.e., nonparametric and model-based) inferences. As an illustrative example, we consider Bayesian filtering in a state space model, where typically there exists an accurate probabilistic model for the state transition process. We propose a novel filtering method that combines model-based inference for the state transition process and data-driven, nonparametric inference for the observation generating process. We empirically validate our approach with synthetic and real-data experiments, the latter being the problem of vision-based mobile robot localization in robotics, which illustrates the effectiveness of the proposed hybrid approach.

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Li, Meng Xin, Gao Ling Su, Jing Hou, and Dai Zheng. "A Survey on Moving Target Tracking in the Intelligent Visual Monitoring System." Applied Mechanics and Materials 599-601 (August 2014): 790–93. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.790.

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Moving target tracking is the key part of intelligent visual surveillance system. Among the various tracking algorithms, the Beysian tracking algorithms and the kernel tracking algorithm are two algorithms that frequently used. The Beysian tracking algorithms mainly conclude Kalman filtering algorithm, extended Kalman filtering algorithm and particle filtering algorithm. Mean Shift is the most representative algorithm of the kernel target tracking. In this survey, the status and development of target tracking algorithms has been studied more extensively with providing a few examples of modified tracking algorithms. Then a comparison was presented based on the limitations and scope of applications. Finally, the paper showed further research prospects of moving target tracking are introduced.

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Lopac, Nikola, Irena Jurdana, Jonatan Lerga, and Nobukazu Wakabayashi. "Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data." Journal of Marine Science and Engineering 9, no. 4 (April 18, 2021): 439. http://dx.doi.org/10.3390/jmse9040439.

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The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling more efficient extraction of useful information. This paper proposes a locally-adaptive filtering algorithm based on the radial basis function (RBF) kernel smoother with variable width. The kernel width is calculated using the asymmetrical combined-window relative intersection of confidence intervals (RICI) algorithm, whose parameters are adjusted by applying the particle swarm optimization (PSO) based procedure. The proposed RBF-RICI algorithm’s filtering performances are analyzed on several simulated, synthetic noisy signals, showing its efficiency in noise suppression and filtering error reduction. Moreover, compared to the competing filtering algorithms, the proposed algorithm provides better or competitive filtering performance in most considered test cases. Finally, the proposed algorithm is applied to the noisy measured maritime data, proving to be a possible solution for a successful practical application in data filtering in maritime transport and other sectors.

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Guo, Shiyao, Yuxia Sheng, Li Chai, and Jingxin Zhang. "Kernel graph filtering—A new method for dynamic sinogram denoising." PLOS ONE 16, no. 12 (December 2, 2021): e0260374. http://dx.doi.org/10.1371/journal.pone.0260374.

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Low count PET (positron emission tomography) imaging is often desirable in clinical diagnosis and biomedical research, but its images are generally very noisy, due to the very weak signals in the sinograms used in image reconstruction. To address this issue, this paper presents a novel kernel graph filtering method for dynamic PET sinogram denoising. This method is derived from treating the dynamic sinograms as the signals on a graph, and learning the graph adaptively from the kernel principal components of the sinograms to construct a lowpass kernel graph spectrum filter. The kernel graph filter thus obtained is then used to filter the original sinogram time frames to obtain the denoised sinograms for PET image reconstruction. Extensive tests and comparisons on the simulated and real life in-vivo dynamic PET datasets show that the proposed method outperforms the existing methods in sinogram denoising and image enhancement of dynamic PET at all count levels, especially at low count, with a great potential in real life applications of dynamic PET imaging.

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Kanagawa, Motonobu, Yu Nishiyama, Arthur Gretton, and Kenji Fukumizu. "Filtering with State-Observation Examples via Kernel Monte Carlo Filter." Neural Computation 28, no. 2 (February 2016): 382–444. http://dx.doi.org/10.1162/neco_a_00806.

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This letter addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e., the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowledge of the observation model is provided only by examples of state-observation pairs. This setting is important and appears when state variables are defined as quantities that are very different from the observations. We propose kernel Monte Carlo filter, a novel filtering method that is focused on this setting. Our approach is based on the framework of kernel mean embeddings, which enables nonparametric posterior inference using the state-observation examples. The proposed method represents state distributions as weighted samples, propagates these samples by sampling, estimates the state posteriors by kernel Bayes’ rule, and resamples by kernel herding. In particular, the sampling and resampling procedures are novel in being expressed using kernel mean embeddings, so we theoretically analyze their behaviors. We reveal the following properties, which are similar to those of corresponding procedures in particle methods: the performance of sampling can degrade if the effective sample size of a weighted sample is small, and resampling improves the sampling performance by increasing the effective sample size. We first demonstrate these theoretical findings by synthetic experiments. Then we show the effectiveness of the proposed filter by artificial and real data experiments, which include vision-based mobile robot localization.

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Kristensen, Dennis. "NONPARAMETRIC FILTERING OF THE REALIZED SPOT VOLATILITY: A KERNEL-BASED APPROACH." Econometric Theory 26, no. 1 (June 19, 2009): 60–93. http://dx.doi.org/10.1017/s0266466609090616.

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A kernel weighted version of the standard realized integrated volatility estimator is proposed. By different choices of the kernel and bandwidth, the measure allows us to focus on specific characteristics of the volatility process. In particular, as the bandwidth vanishes, an estimator of the realized spot volatility is obtained. We denote this the filtered spot volatility. We show consistency and asymptotic normality of the kernel smoothed realized volatility and the filtered spot volatility. We consider boundary issues and propose two methods to handle these. The choice of bandwidth is discussed and data-driven selection methods are proposed. A simulation study examines the finite sample properties of the estimators.

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Li, Ya Qin, and Yang Hua Xu. "A Novel Filtering Algorithm Based on Least Square Support Vector." Advanced Materials Research 532-533 (June 2012): 1732–35. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1732.

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In this paper, we proposed a novel filtering algorithm that using the Ricker wavelet kernel to reduce the noise. The algorithm based on Support vector machine (SVM) which is a machine learning method on the base of statistical learning theory. Those parameters of the new algorithm affect the rising edge, the band width and central frequency of passband. The experimental results of synthetic seismic data show that the filter with the Ricker wavelet kernel works better than other methods．

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33

Seitz, Stella. "Optimized Cluster Reconstruction." Symposium - International Astronomical Union 173 (1996): 151–52. http://dx.doi.org/10.1017/s0074180900231173.

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Zhao, Chenyang, and Zhijie Zhang. "Dynamic Error Correction of Filament Thermocouples with Different Structures of Junction based on Inverse Filtering Method." Micromachines 11, no. 1 (December 30, 2019): 44. http://dx.doi.org/10.3390/mi11010044.

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Since filament thermocouple is limited by its junction structure and dynamic characteristics, the actual heat conduction process cannot be reproduced during the transient thermal shock. In order to solve this problem, we established a thermocouple dynamic calibration system with laser pulse as excitation source to transform the problem of the restoring excitation source acting on the surface temperature of thermocouple junction into the problem of solving the one-dimensional (1D) inverse heat conduction process, proposed a two-layer domain filtering kernel regularization method for double conductors of thermocouple, analyzed the factors causing unstable two-layer domain solution, and solved the regular solution of two-layer domain by the filtering kernel regularization strategy. By laser narrow pulse calibration experiment, we obtained experimental samples of filament thermocouples with two kinds of junction structures, butt-welded and ball-welded; established error estimation criterion; and obtained the optimal filtering kernel parameters by the proposed regularization strategy, respectively. The regular solutions solved for different thermocouples were very close to the exact solution under the optimal strategy, indicating that the proposed regularization method can effectively approach the actual surface temperature of the thermocouple junction.

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Danesh, Ahmad Reza, and Mehdi Habibi. "A signed pulse-train-based image processor-array for parallel kernel convolution in vision sensors." Sensor Review 40, no. 4 (June 26, 2020): 521–28. http://dx.doi.org/10.1108/sr-10-2019-0242.

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Purpose The purpose of this paper is to design a kernel convolution processor. High-speed image processing is a challenging task for real-time applications such as product quality control of manufacturing lines. Smart image sensors use an array of in-pixel processors to facilitate high-speed real-time image processing. These sensors are usually used to perform the initial low-level bulk image filtering and enhancement. Design/methodology/approach In this paper, using pulse-width modulated signals and regular nearest neighbor interconnections, a convolution image processor is presented. The presented processor is not only capable of processing arbitrary size kernels but also the kernel coefficients can be any arbitrary positive or negative floating number. Findings The performance of the proposed architecture is evaluated on a Xilinx Virtex-7 field programmable gate array platform. The peak signal-to-noise ratio metric is used to measure the computation error for different images, filters and illuminations. Finally, the power consumption of the circuit in different operating conditions is presented. Originality/value The presented processor array can be used for high-speed kernel convolution image processing tasks including arbitrary size edge detection and sharpening functions, which require negative and fractional kernel values.

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LI Xue-qing, 李雪晴, 杨德东 YANG De-dong, 毛. 宁. MAO Ning, and 杨福才 YANG Fu-cai. "Depth kernel correlation filtering tracking based on multi-template." Chinese Journal of Liquid Crystals and Displays 32, no. 12 (2017): 993–98. http://dx.doi.org/10.3788/yjyxs20173212.0993.

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Phan, Nghia Quoc, Phuong Hoai Dang, and Hiep Xuan Huynh. "Similarity Kernel for User-based Collaborative Filtering Recommendation System." EAI Endorsed Transactions on Context-aware Systems and Applications 4, no. 12 (July 6, 2017): 152759. http://dx.doi.org/10.4108/eai.6-7-2017.152759.

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Babaud, Jean, Andrew P. Witkin, Michel Baudin, and Richard O. Duda. "Uniqueness of the Gaussian Kernel for Scale-Space Filtering." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8, no. 1 (January 1986): 26–33. http://dx.doi.org/10.1109/tpami.1986.4767749.

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Yang Jianfeng, 杨剑锋, and 张建鹏 Zhang Jianpeng. "Long Time Target Tracking Based on Kernel Correlation Filtering." Laser & Optoelectronics Progress 56, no. 2 (2019): 021502. http://dx.doi.org/10.3788/lop56.021502.

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Dai, Tao, Weizhi Lu, Wei Wang, Jilei Wang, and Shu-Tao Xia. "Entropy-based bilateral filtering with a new range kernel." Signal Processing 137 (August 2017): 223–34. http://dx.doi.org/10.1016/j.sigpro.2017.02.005.

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Coufal, David. "Convergence rates of kernel density estimates in particle filtering." Statistics & Probability Letters 153 (October 2019): 164–70. http://dx.doi.org/10.1016/j.spl.2019.06.013.

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Zhang, Victoria Ying, and Albert Kai-sun Wong. "Kernel-based particle filtering for indoor tracking in WLANs." Journal of Network and Computer Applications 35, no. 6 (November 2012): 1807–17. http://dx.doi.org/10.1016/j.jnca.2012.07.005.

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Wang, Yang, Guo-Wei Wei, and Siyang Yang. "Iterative Filtering Decomposition Based on Local Spectral Evolution Kernel." Journal of Scientific Computing 50, no. 3 (May 14, 2011): 629–64. http://dx.doi.org/10.1007/s10915-011-9496-0.

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Blazadonakis, Michalis E., and Michalis Zervakis. "Wrapper filtering criteria via linear neuron and kernel approaches." Computers in Biology and Medicine 38, no. 8 (August 2008): 894–912. http://dx.doi.org/10.1016/j.compbiomed.2008.05.005.

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Miraliakbari, A., S. Sok, Y. O. Ouma, and M. Hahn. "COMPARATIVE EVALUATION OF PAVEMENT CRACK DETECTION USING KERNEL-BASED TECHNIQUES IN ASPHALT ROAD SURFACES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 689–94. http://dx.doi.org/10.5194/isprs-archives-xli-b1-689-2016.

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With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

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Miraliakbari, A., S. Sok, Y. O. Ouma, and M. Hahn. "COMPARATIVE EVALUATION OF PAVEMENT CRACK DETECTION USING KERNEL-BASED TECHNIQUES IN ASPHALT ROAD SURFACES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 689–94. http://dx.doi.org/10.5194/isprsarchives-xli-b1-689-2016.

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With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

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Florea, Camelia, Mihaela Gordan, Bogdan Orza, and Aurel Vlaicu. "Compressed Domain Computationally Efficient Processing Scheme for JPEG Image Filtering." Advanced Engineering Forum 8-9 (June 2013): 480–89. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.480.

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Image filtering is one of the principal tools used in computer vision applications. Real systems store and manipulate high resolution images in compressed forms, therefore the implementation of the entire processing chain directly in the compressed domain became essential. This includes almost always linear filtering operations implemented by convolution. Linear image filtering implementation directly on the JPEG images is challenging for several reasons, including the complexity of transposing the pixel level convolution in the compressed domain, which may increase the processing time, despite avoiding the decompression. In this paper we propose a new computationally efficient solution for JPEG image filtering (as a spatial convolution between the input image and a given kernel) directly in the DCT based compressed domain. We propose that the convolution operation to be applied just on the periodical extensions of the DCT basis images, as an off-line processing, obtaining the filtered DCT basis images, which are used in data decompression. While this doesn't solve the near block boundaries filtering artefacts for large convolution kernels, for most practical cases, it provides good quality results at a very low computational complexity. These kind of implementations can run at real-time rates/ speeds and are suitable for developments of applications on digital cameras/ DSP/FPGA.

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HUANG, QIU, GENGSHENG L. ZENG, and GRANT T. GULLBERG. "AN ANALYTICAL INVERSION OF THE 180° EXPONENTIAL RADON TRANSFORM WITH A NUMERICALLY GENERATED KERNEL." International Journal of Image and Graphics 07, no. 01 (January 2007): 71–85. http://dx.doi.org/10.1142/s0219467807002544.

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This work presents an inversion algorithm for the exponential Radon transform (ERT) over 180° range of view angles. The algorithm can be applied to two-dimensional parallel beam geometry in single photon emission computed tomography. First the differentiation of the ERT over π is backprojected. A convolutional relation between this backprojected differentiation and the original image is then established. In order to invert the convolution relation, the least-squares method is utilized to obtain a numerically generated filtering kernel, which readily restores the original image. The advantages of the proposed algorithm are, first, it only requires half the view angles of the conventional inversion algorithm, second, it deals with truncation in ERT data in certain situations, and third, the numerically generated filtering kernel can be pre-calculated and stored for later applications. The algorithm is an analytical approach except for the pre-calculated inverse kernel.

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Torres-Huitzil, Cesar. "Resource Efficient Hardware Architecture for Fast Computation of Running Max/Min Filters." Scientific World Journal 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/108103.

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Running max/min filters on rectangular kernels are widely used in many digital signal and image processing applications. Filtering with ak×kkernel requires ofk2−1comparisons per sample for a direct implementation; thus, performance scales expensively with the kernel sizek. Faster computations can be achieved by kernel decomposition and using constant time one-dimensional algorithms on custom hardware. This paper presents a hardware architecture for real-time computation of running max/min filters based on the van Herk/Gil-Werman (HGW) algorithm. The proposed architecture design uses less computation and memory resources than previously reported architectures when targeted to Field Programmable Gate Array (FPGA) devices. Implementation results show that the architecture is able to compute max/min filters, on1024×1024images with up to255×255kernels, in around 8.4 milliseconds, 120 frames per second, at a clock frequency of 250 MHz. The implementation is highly scalable for the kernel size with good performance/area tradeoff suitable for embedded applications. The applicability of the architecture is shown for local adaptive image thresholding.

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Wu, Qishuai, Yingsong Li, and Wei Xue. "A Kernel Recursive Maximum Versoria-Like Criterion Algorithm for Nonlinear Channel Equalization." Symmetry 11, no. 9 (August 21, 2019): 1067. http://dx.doi.org/10.3390/sym11091067.

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In this paper, a kernel recursive maximum Versoria-like criterion (KRMVLC) algorithm has been constructed, derived, and analyzed within the framework of nonlinear adaptive filtering (AF), which considers the benefits of logarithmic second-order errors and the symmetry maximum-Versoria criterion (MVC) lying in reproducing the kernel Hilbert space (RKHS). In the devised KRMVLC, the Versoria approach aims to resist the impulse noise. The proposed KRMVLC algorithm was carefully derived for taking the nonlinear channel equalization (NCE) under different non-Gaussian interferences. The achieved results verify that the KRMVLC is robust against non-Gaussian interferences and performs better than those of the popular kernel AF algorithms, like the kernel least-mean-square (KLMS), kernel least-mixed-mean-square (KLMMN), and Kernel maximum Versoria criterion (KMVC).

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