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Artykuły w czasopismach na temat „Kernel filtering”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 31 lipca 2025

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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 (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

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Ramadhan, Helmi Sunjaya, and Erwin Budi Setiawan. "Social Media Based Film Recommender System (Twitter) on Disney+ with Hybrid Filtering Using Support Vector Machine." sinkron 8, no. 4 (2023): 2215–25. http://dx.doi.org/10.33395/sinkron.v8i4.12876.

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In the current era, the culture of watching TV shows and movies has been made easier by the presence of the internet. Now, watching movies on platforms can be done from anywhere, one of which is Disney+. At times, people find it challenging to decide which film to watch given the multitude of genres and film titles available on these platforms. One solution to this issue is a recommendation system that can suggest films based on ratings. The recommendation system to be utilized involves Collaborative Filtering, Content-Based Filtering, and Hybrid Filtering. This is because Collaborative Filter

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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 (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 isotr

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4

Zayed, M. Ramadan. "Effect of kernel size on Wiener and Gaussian image filtering." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 3 (2019): 1455–60. https://doi.org/10.12928/TELKOMNIKA.v17i3.11192.

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In this paper, the effect of the kernel size of Wiener and Gaussian filters on their image restoration qualities has been studied and analyzed. Four sizes of such kernels, namely 3x3, 5x5, 7x7 and 9x9 were simulated. Two different types of noise with zero mean and several variances have been used: Gaussian noise and speckle noise. Several image quality measuring indices have been applied in the computer simulations. In particular, mean absolute error (MAE), mean square error (MSE) and structural similarity (SSIM) index were used. Many images were tested in the simulations; however the results

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5

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

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Huang, Di, Xishan Zhang, Rui Zhang, et al. "DWM: A Decomposable Winograd Method for Convolution Acceleration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (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 co

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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 (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 his

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8

Liu, Ning, and Thomas Schumacher. "Improved Denoising of Structural Vibration Data Employing Bilateral Filtering." Sensors 20, no. 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 o

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9

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 (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 algo

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10

Hu, Yongbing, Chenchong Bi, and Yong Chen. "Kernel Adaptive Filtering Algorithm Based on Hyperbolic Tangent Mixed Error Function." Symmetry 16, no. 12 (2024): 1624. https://doi.org/10.3390/sym16121624.

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This paper proposes an adaptive filtering algorithm based on the symmetry Kernel Hyperbolic Tangent Mixed Error Criterion (KHTMC), aimed at addressing the identification of nonlinear systems under non-Gaussian noise environments. The algorithm optimizes signal processing by constructing a mixed cost function that combines the symmetry logarithmic square error and the hyperbolic tangent function and integrates it with the kernel adaptive filtering method. Simulation results show that, compared to existing kernel adaptive filtering algorithms, the KHTMC algorithm exhibits significant advantages

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11

Wang, Hong, Hongyu Han, Sheng Zhang, and Jinhua Ku. "An efficient kernel adaptive filtering algorithm with adaptive alternating filtering mechanism." Digital Signal Processing 159 (April 2025): 104997. https://doi.org/10.1016/j.dsp.2025.104997.

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

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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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14

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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15

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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16

Liu, Bo, Boujemaa Ait-El-Fquih, and Ibrahim Hoteit. "Efficient Kernel-Based Ensemble Gaussian Mixture Filtering." Monthly Weather Review 144, no. 2 (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

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17

Selvi, Oguz. "A note on digital filtering with the second moment norm." GEOPHYSICS 62, no. 4 (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 t

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18

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 (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 structur

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19

Zhao, Zhiqiang, Ping Feng, Jingjuan Guo, et al. "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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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 (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 mat

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21

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 th

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22

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 (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 measurem

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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 modifie

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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 (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

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Khairunnisa, Mutiarahmi, and Suryo Adhi Wibowo. "Multi-Head Voting based on Kernel Filtering for Fine-grained Visual Classification." JOIV : International Journal on Informatics Visualization 9, no. 2 (2025): 690. https://doi.org/10.62527/joiv.9.2.2920.

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Research on Fine-Grained Visual Classification (FGVC) faces a significant challenge in distinguishing objects with subtle differences within intra-class variations and inter-class similarities, which are critical for accurate classification. To address this complexity, many advanced methods have been proposed using feature coding, part-based components for modification, and attention-based efforts to facilitate different classification phases. Vision Transformers (ViT) has recently emerged as a promising competitor compared to other complex methods in FGVC applications for image recognition, w

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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 (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 mod

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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 (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 fil

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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 (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 (2021): 2629–42. http://dx.doi.org/10.1049/ipr2.12251.

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

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

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Banerjee, Amit, and Philippe Burlina. "Efficient Particle Filtering via Sparse Kernel Density Estimation." IEEE Transactions on Image Processing 19, no. 9 (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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Kanagawa, Motonobu, Yu Nishiyama, Arthur Gretton, and Kenji Fukumizu. "Filtering with State-Observation Examples via Kernel Monte Carlo Filter." Neural Computation 28, no. 2 (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 met

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Kristensen, Dennis. "NONPARAMETRIC FILTERING OF THE REALIZED SPOT VOLATILITY: A KERNEL-BASED APPROACH." Econometric Theory 26, no. 1 (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 discu

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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 (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 cond

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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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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 (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 pre

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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, despit

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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 (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

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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

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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

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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 (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 resu

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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 compu

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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 (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 (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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