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Artykuły w czasopismach na temat „Vector Median Filter”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Morillas, Samuel, and Valentín Gregori. "Robustifying Vector Median Filter." Sensors 11, no. 8 (2011): 8115–26. http://dx.doi.org/10.3390/s110808115.

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Liu, Yike. "Noise reduction by vector median filtering." GEOPHYSICS 78, no. 3 (2013): V79—V87. http://dx.doi.org/10.1190/geo2012-0232.1.

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The scalar median filter (SMF) is often used to reduce noise in scalar geophysical data. We present an extension of the SMF to a vector median filter (VMF) for suppressing noise contained in geophysical data represented by multidimensional, multicomponent vector fields. Although the SMF can be applied to each component of a vector field individually, the VMF is applied to all components simultaneously. Like the SMF, the VMF intends to suppress random noise while preserving discontinuities in the vector fields. Preserving such discontinuities is essential for exploration geophysics because disc

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3

Lin, Tzu-Chao, and Pao-Ta Yu. "Adaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration." Neural Computation 16, no. 2 (2004): 333–54. http://dx.doi.org/10.1162/neco.2004.16.2.333.

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In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality o

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4

Wang, Yanghua, Xiwu Liu, Fengxia Gao, and Ying Rao. "Robust vector median filtering with a structure-adaptive implementation." GEOPHYSICS 85, no. 5 (2020): V407—V414. http://dx.doi.org/10.1190/geo2020-0012.1.

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The 3D seismic data in the prestack domain are contaminated by impulse noise. We have adopted a robust vector median filter (VMF) for attenuating the impulse noise from 3D seismic data cubes. The proposed filter has two attractive features. First, it is robust; the vector median that is the output of the filter not only has a minimum distance to all input data vectors, but it also has a high similarity to the original data vector. Second, it is structure adaptive; the filter is implemented following the local structure of coherent seismic events. The application of the robust and structure-ada

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5

Matsuoka, Jyohei, Takanori Koga, Noriaki Suetake, and Eiji Uchino. "Switching non-local vector median filter." Optical Review 23, no. 2 (2016): 195–207. http://dx.doi.org/10.1007/s10043-016-0184-z.

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6

Khriji, L. "Vector Directional Distance Rational Hybrid Filters for Color Image Restoration." Journal of Engineering Research [TJER] 2, no. 1 (2005): 1. http://dx.doi.org/10.24200/tjer.vol2iss1pp1-12.

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A new class of nonlinear filters, called vector-directional distance rational hybrid filters (VDDRHF) for multispectral image processing, is introduced and applied to color image-filtering problems. These filters are based on rational functions (RF). The VDDRHF filter is a two-stage filter, which exploits the features of the vector directional distance filter (VDDF), the center weighted vector directional distance filter (CWVDDF) and those of the rational operator. The filter output is a result of vector rational function (VRF) operating on the output of three sub-functions. Two vector directi

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7

Lukac, Rastislav, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos, and Bogdan Smolka. "A Statistically-Switched Adaptive Vector Median Filter." Journal of Intelligent and Robotic Systems 42, no. 4 (2005): 361–91. http://dx.doi.org/10.1007/s10846-005-1730-2.

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8

Yang, Sung-Chul, and Ki-Yun Yu. "Vector Median Filter for Alignment with Road Vector Data to Aerial Image." Korean Journal of Geomatics 29, no. 1 (2011): 63–69. http://dx.doi.org/10.7848/ksgpc.2011.29.1.63.

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9

Argenti, F., M. Barni, V. Cappellini, and A. Mecocci. "Vector median deblurring filter for colour image restoration." Electronics Letters 27, no. 21 (1991): 1899. http://dx.doi.org/10.1049/el:19911179.

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10

Huo, Shoudong, Yi Luo, and Panos G. Kelamis. "Simultaneous sources separation via multidirectional vector-median filtering." GEOPHYSICS 77, no. 4 (2012): V123—V131. http://dx.doi.org/10.1190/geo2011-0254.1.

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Simultaneous source acquisition technology, also referred to as “blended acquisition,” involves recording two or more shots simultaneously. Despite the fact that the recorded data has crosstalk from different shots, conventional processing procedures can still produce acceptable images for interpretation. This is due to the power of the stacking process using blended data with its increased data redundancy and inherent time delays between various shots. It is still desirable to separate the blended data into single shot gathers and reduce the crosstalk noise to achieve the highest seismic imag

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11

Arnal, Josep, and Luis Súcar. "Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images." Applied Sciences 10, no. 1 (2019): 243. http://dx.doi.org/10.3390/app10010243.

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To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse an

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12

Gopatoti, Anandbabu, Veeranjaneyulu Ganipisetty, and Chandra Naik Merajothu. "Impulse Noise Removal in Digital Images by using Image Fusion Technique." Journal of Advanced Research in Dynamical and Control Systems 10, no. 6S (2018): 566–77. https://doi.org/10.5281/zenodo.11037845.

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This technique is implemented in reducing impulse noise from the digital images and to acquire noise free image. Image fusion technique means that fusing or combination of two or more images with different or similar constraints to form a single image with all the information in each image not being strayed. Normally to reduce noise from an image we use different filtering algorithms and the outputs of those algorithms are fused together to form a perfect image without noise. In this paper we intend to use five different filtering algorithms individually to and image captured by a sensor. The

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13

Rashid, Nasr, Kamel Berriri, Mohammed Albekairi, et al. "New Real-Time Impulse Noise Removal Method Applied to Chest X-ray Images." Diagnostics 12, no. 11 (2022): 2738. http://dx.doi.org/10.3390/diagnostics12112738.

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In this paper, we propose a new Modified Laplacian Vector Median Filter (MLVMF) for real-time denoising complex images corrupted by “salt and pepper” impulsive noise. The method consists of two rounds with three steps each: the first round starts with the identification of pixels that may be contaminated by noise using a Modified Laplacian Filter. Then, corrupted pixels pass a neighborhood-based validation test. Finally, the Vector Median Filter is used to replace noisy pixels. The MLVMF uses a 5 × 5 window to observe the intensity variations around each pixel of the image with a rotation step

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14

Aslandogan, Yuksel Alp. "Robust switching vector median filter for impulsive noise removal." Journal of Electronic Imaging 17, no. 4 (2008): 043006. http://dx.doi.org/10.1117/1.2991415.

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15

Vardavoulia, M. I., I. Andreadis, and Ph Tsalides. "A new vector median filter for colour image processing." Pattern Recognition Letters 22, no. 6-7 (2001): 675–89. http://dx.doi.org/10.1016/s0167-8655(00)00141-0.

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16

Barni, M., V. Cappellini, and A. Mecocci. "Fast vector median filter based on Euclidean norm approximation." IEEE Signal Processing Letters 1, no. 6 (1994): 92–94. http://dx.doi.org/10.1109/97.295343.

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17

Rhee, Kang Hyeon. "Improvement Feature Vector: Autoregressive Model of Median Filter Residual." IEEE Access 7 (2019): 77524–40. http://dx.doi.org/10.1109/access.2019.2921573.

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18

Qur'ana, Tri Wahyu. "PERBAIKAN CITRA MENGGUNAKAN MEDIAN FILTER UNTUK MENINGKATKAN AKURASI PADA KLASIFIKASI MOTIF SASIRANGAN." Technologia: Jurnal Ilmiah 9, no. 4 (2018): 270. http://dx.doi.org/10.31602/tji.v9i4.1543.

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Kain Sasirangan memiliki beragam motif yang telah diakui oleh pemerintah. Untuk membantu upaya pendokumentasian, dibutuhkan sistem klasifikasi yang cukup handal dalam mengklasifikasi dan mengidentifikasi citra motif kain Sasirangan. Pengolahan citra digital merupakan salah satu teknologi yang dapat dimanfaatkan untuk merancang sebuah model klasifikasi motif kain sasirangan melalui proses akuisisi citra. Pada tahap pre-processing digunakan metode Median Filter untuk meningkatkan mutu citra. Pada tahap ekstraksi fitur warna dan tekstur menggunakan metode Local Binary Pattern Variance (LBPV), sel

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19

Roy, Amarjit, Joyeeta Singha, and Rabul Hussain Laskar. "Removal of Impulse Noise from Gray Images Using Fuzzy SVM Based Histogram Fuzzy Filter." Journal of Circuits, Systems and Computers 27, no. 09 (2018): 1850139. http://dx.doi.org/10.1142/s0218126618501396.

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Impulse noise is an image noise that degrades the quality of the image drastically. In this paper, k-means clustering has been incorporated with fuzzy-support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from gray images. Here, local binary pattern (LBP) has been incorporated with previously used feature vector prediction error of the processing pixel, absolute difference between median value and processing pixel, median pixel, pixel under operation and mean value around the processing kernel. In this work, [Formula: see text]-me

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20

Liu, C., W. Pei, Z. Y. Xia, S. Niyokindi, J. C. Song, and Li Ding Wang. "Wavelet Transform Based 3D Scattered Data Processing in Binocular Micro Stereovision System." Key Engineering Materials 291-292 (August 2005): 673–0. http://dx.doi.org/10.4028/www.scientific.net/kem.291-292.673.

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In micro stereovision system, false matching due to the randomicity of image signal makes 3D reconstruction data contain a large amount of abnormal data in the form of noise (pulse noise, Gauss noise). In order to obtain a more accurate 3D reconstruction shape, a novel method of 3D scattered data processing, which is a combination of wavelet transform and extended Vector Median Filters, is proposed to use wavelet transform to realize primary denosing, and use extended Vector Median Filters to further denoise thereby achieving higher precision 3D measurement. In wavelet based denosing, wavelet

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21

Zhu, Zhiliang, Lianghai Jin, Enmin Song, and Chih-Cheng Hung. "Quaternion Switching Vector Median Filter Based on Local Reachability Density." IEEE Signal Processing Letters 25, no. 6 (2018): 843–47. http://dx.doi.org/10.1109/lsp.2018.2808343.

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22

Xu, Jiangtao, Lei Wang, and Zaifeng Shi. "A switching weighted vector median filter based on edge detection." Signal Processing 98 (May 2014): 359–69. http://dx.doi.org/10.1016/j.sigpro.2013.11.035.

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23

Wu, Shaojiang, Yibo Wang, Zhixin Di, and Xu Chang. "Random noise attenuation by 3D Multi-directional vector median filter." Journal of Applied Geophysics 159 (December 2018): 277–84. http://dx.doi.org/10.1016/j.jappgeo.2018.09.021.

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24

Gómez-Moreno, Hilario, Pedro Gil-Jiménez, Sergio Lafuente-Arroyo, Roberto López-Sastre, and Saturnino Maldonado-Bascón. "A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression." Scientific World Journal 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/826405.

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We present a new impulse noise removal technique based on Support Vector Machines (SVM). Both classification and regression were used to reduce the “salt and pepper” noise found in digital images. Classification enables identification of noisy pixels, while regression provides a means to determine reconstruction values. The training vectors necessary for the SVM were generated synthetically in order to maintain control over quality and complexity. A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known stat

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25

PARK, Ju Hyun, Young-Chul KIM, and Hong-Sung HOON. "Edge-Based Motion Vector Processing for Frame Interpolation Based on Weighted Vector Median Filter." IEICE Transactions on Information and Systems E93-D, no. 11 (2010): 3132–35. http://dx.doi.org/10.1587/transinf.e93.d.3132.

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26

Pulipaka, Krishna Prasad, K. Sathish Kumar, K. Geethali Apoorva, Rohith Rao, and K. Radha Krishna. "Noise Removal from Images Using Adaptive Neuro/Network-Fuzzy Interface Systems." International Journal of Engineering and Applied Technologies 22 (December 13, 2022): 15–32. http://dx.doi.org/10.56431/p-t615v7.

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Any Information signal is best desirable without any external noise/ disturbances. Noise in any signal is the undesirable quantity present which deteriorates the signal's quality, thus compromising the information. Any signal, be it an image signal (2-D) or else a video signal (3-D) in the field of communication, if not always but most number of times prone to noise. In this paper, we would be dealing with removing types of noise on an image, using various filter techniques such as vector median filter, vector directional filter. Using the image processing tools in MATLAB, we could achieve thi

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27

Fard, L. A., K. Jaseb, and S. M. Mehdi Safi. "Motor-Imagery EEG Signal Classification using Optimized Support Vector Machine by Differential Evolution Algorithm." NAMJ 17 (2023), no. 2, 17 (2023) (2023): 78–86. http://dx.doi.org/10.56936/18290825-2023.17.2-78.

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Background: Motor-Imagery (MI) is a mental or cognitive stimulation without actual sensory input that enables the mind to represent perceptual information. This study aims to use the optimized support vector machine (OSVM) by differential evolution algorithm for motor-Imagery EEG signal classification. Methods: A total of three filters were applied to each signal during the preprocessing phase. The bandstop filter was used to remove urban noise and signal recorders, the median filter to remove random sudden peaks in the signal, and finally, the signal was normalized using the mapminmax filter.

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28

Liu, Ji Ping, and Bo Wang. "An Improved Error Concealment Algorithm for Wireless Video Applications." Advanced Materials Research 171-172 (December 2010): 531–35. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.531.

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This paper develops a new error concealment algorithm for the whole-frame losses in wireless video transmission based on H.264/AVC.At first,we propose a forward motion vector extrapolation algorithm based on variable-size block motion vector estimation to obtain the motion vector field of the loss frame without yielding the hole problems. Then, we partition the missing frame into several object regions by clustering the motion vectors, furthermore, we apply median filter to reconstruct the motion vectors for hole areas based on the reliabilities of neighboring motion vectors. Finally, all macr

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29

A., Afreen Habiba, and Dr.B.Raghu2. "IMAGE DENOISING IN MRI IMAGES USING CONTOURLET TRANSFORM AND COMPARISON OF FILTERING METHODS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 7 (2017): 150–60. https://doi.org/10.5281/zenodo.835762.

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In diagnosis of medical images, operations such as feature extraction and object recognition plays the key role.. These operations will become difficult if the images are corrupted with noises. Several types of noise were introduced in the images during image acquisition, transfer & storage. The main objective is to remove the noise from the input image. Image Denoising is an utmost challenge for Researchers, developing Image denoising algorithms is a difficult task, since fine details in a medical image should not be destroyed during noise removal during the diagnosis of information. Medi

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30

Roji Chanu, P., and Kh Manglem Singh. "Two-Stage Quaternion Vector Median Filter for Removing Impulse Noise in Color Images." Journal of Engineering and Applied Sciences 15, no. 2 (2019): 350–64. http://dx.doi.org/10.36478/jeasci.2020.350.364.

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31

Muharir, Muharir. "PENGENALAN CITRA SASIRANGAN BERBASIS FITUR GLCM DAN MEDIAN FILTER MENGGUNAKAN LEARNING VECTOR QUANTITATION." Technologia: Jurnal Ilmiah 9, no. 4 (2018): 255. http://dx.doi.org/10.31602/tji.v9i4.1541.

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Sasirangan adalah kain khas suku Banjar provinsi Kalimantan Selatan, kain sasirangan merupakan salah satu budaya yang dimiliki bangsa Indonesia yang harus dijaga dan dilestarikan. Sasirangan saat ini memiliki beragam motif dan sebagian motif-motif yang ada belum dikenal masyarakat. Pengenalan citra saat ini sudah banyak dilakukan namun akurasi yang dihasilkan masih rendah dan belum diketahui algoritma apa yang menghasilkan akurasi terbaik untuk mengenali citra sasirangan. Pada penelitian ini, teknik yang digunakan untuk ekstraksi fitur adalah metode Grey Level Co-occurrence Matriec. Untuk peng

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32

Lin, Tzu-Chao, and Pao-Ta Yu. "Adaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration." Neural Computation 16, no. 2 (2004): 332–53. http://dx.doi.org/10.1162/089976604322742056.

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33

Lee, Dae-Geun, Min-Jae Park, Jeong-Ok Kim, Do-Yoon Kim, Dong-Wook Kim, and Dong-Hoon Lim. "Adaptive Switching Median Filter for Impulse Noise Removal Based on Support Vector Machines." Communications for Statistical Applications and Methods 18, no. 6 (2011): 871–86. http://dx.doi.org/10.5351/ckss.2011.18.6.871.

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34

Wang, Pei, and Jing Wang. "Particle Swarm Clustering-Based Moving Object Segmentation in the H.264 Compressed Domain." Advanced Materials Research 433-440 (January 2012): 4841–44. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4841.

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An approach of the moving object segmentation is proposed in this paper. Firstly the motion fields are extracted from the compressed stream, where the noise and the unreal motion blocks are removed by vector median filter. Then the motion vectors are accumulated by motion estimation, in order to get denser and prominent motion vectors. Finally the moving objects are segmented adaptively by particle swarm clustering algorithm. It is demonstrated by the experimental results that the moving objects in the compressed domain can be segmented effectively.

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35

Kavinkumar, K., and T. Meeradevi. "Classification of Tumor of MRI Brain Image Using Hybrid Feature Extraction Method and Support Vector Machine Classifier." Journal of Medical Imaging and Health Informatics 11, no. 10 (2021): 2558–65. http://dx.doi.org/10.1166/jmihi.2021.3842.

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Brain tumors Analysis is problematic somewhat due to varied size, shape, location of tumor and the appearance and presence of brain tumor. Clinicians and radiologist have difficulty in identifying the tumor type. An efficient hybrid feature extraction method to classify the type of tumor accurately as meningioma, gliomas and pituitary tumor using SVM (support vector machine) classifier is proposed. The modified Non-Local Means (NLM) filter may be effectively used to get the pure image. The NLM filter is compared with common filters like median and wiener. From the denoised image the classifica

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36

Fajardo-Delgado, Daniel, Ansel Y. Rodríguez-González, Sergio Sandoval-Pérez, Jesús Ezequiel Molinar-Solís, and María Guadalupe Sánchez-Cervantes. "Genetic Programming to Remove Impulse Noise in Color Images." Applied Sciences 14, no. 1 (2023): 126. http://dx.doi.org/10.3390/app14010126.

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This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image chan

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37

Wang, Chang You, and Zhao Long Gao. "Image Denoising Method Based on ^v-Support Vector Regression and Noise Detection." Advanced Materials Research 756-759 (September 2013): 4126–32. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4126.

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Aimed at the correlation between noise pixels and neighboring pixels, a new method based on the-support vector regression (-SVR) is proposed to remove the salt & pepper noise in corrupted images. The new algorithm first takes a decision whether the pixel under test is noise or not by comparing the block uniformity of the 3x3 window with one of the entire image, secondly adjusts adaptively the size of filtering window which is used to determine the training set according to the number of noise points in the window, thirdly determines the decision function that is used to predict the gray va

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38

Khaidem, Sally. "Detection and Removal of Impulse Noise from Colour Image using Lagrange Interpolation and Centre Weighted Vector Median Filter." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (2020): 50–56. http://dx.doi.org/10.5373/jardcs/v12i3/20201166.

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39

Roy, Amarjit, Joyeeta Singha, Lalit Manam, and Rabul Hussain Laskar. "Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images." IET Image Processing 11, no. 6 (2017): 352–61. http://dx.doi.org/10.1049/iet-ipr.2016.0320.

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40

Jin, Lianghai, and Dehua Li. "A switching vector median filter based on the CIELAB color space for color image restoration." Signal Processing 87, no. 6 (2007): 1345–54. http://dx.doi.org/10.1016/j.sigpro.2006.11.008.

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Muhtadan, Risanuri Hidayat, Widyawan, and Fahmi Amhar. "Weld Defect Classification in Radiographic Film Using Statistical Texture and Support Vector Machine." Advanced Materials Research 896 (February 2014): 695–700. http://dx.doi.org/10.4028/www.scientific.net/amr.896.695.

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Weld defect identification requires radiographic operator experience, so the interpretation of weld defect type could potentially bring subjectivity and human error factor. This paper proposes Statistical Texture and Support Vector Machine method for weld defect type classification in radiographic film. Digital image processing technique applied in this paper implements noise reduction using median filter, contrast stretching, and image sharpening using Laplacian filter. Statistical method feature extraction based on image histogram was proposed for describing weld defects texture characterist

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42

Kesrarat, Darun, and Vorapoj Patanavijit. "Experimental Study in Error Vector Magnitude of Bidirectional Confidential with Median Filter on Spatial Domain Optical Flow under Non Gaussian Noise Contamination." ECTI Transactions on Electrical Engineering, Electronics, and Communications 14, no. 2 (2016): 1–10. http://dx.doi.org/10.37936/ecti-eec.2016142.171135.

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In this paper, we focus on the robustness in noise tolerance of spatial domain optical flow and we present a performance study of bidirectional confidential with median filter on spatial domain optical flow (spatial correlation, local based, and global based) under non Gaussian noise where several noise tolerance models on spatial domain optical flow are used in comparison. The experiment results are investigated on robustness under noisy condition by using non Gaussian noise (Poisson Noise, Salt & Pepper noise, and Speckle Noise) in contamination with several standard sequences. The exper

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43

Singh, Khumanthem Manglem. "Vector median filter based on non-causal linear prediction for detection of impulse noise from images." International Journal of Computational Science and Engineering 7, no. 4 (2012): 345. http://dx.doi.org/10.1504/ijcse.2012.049729.

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Selvaraj, Lokesh, and Balakrishnan Ganesan. "Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/270576.

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Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum

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Jöhl, Alexander, Yannick Berdou, Matthias Guckenberger, et al. "Performance behavior of prediction filters for respiratory motion compensation in radiotherapy." Current Directions in Biomedical Engineering 3, no. 2 (2017): 429–32. http://dx.doi.org/10.1515/cdbme-2017-0090.

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AbstractIntroduction: In radiotherapy, tumors may move due to the patient’s respiration, which decreases treatment accuracy. Some motion mitigation methods require measuring the tumor position during treatment. Current available sensors often suffer from time delays, which degrade the motion mitigation performance. However, the tumor motion is often periodic and continuous, which allows predicting the motion ahead. Method and Materials: A couch tracking system was simulated in MATLAB and five prediction filters selected from literature were implemented and tested on 51 respiration signals (med

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Chanu, P. Roji, and Kh Manglem Singh. "A two-stage switching vector median filter based on quaternion for removing impulse noise in color images." Multimedia Tools and Applications 78, no. 11 (2018): 15375–401. http://dx.doi.org/10.1007/s11042-018-6925-1.

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Chen, Yan Ting, and Wan Zhou Ye. "Fast Algorithm for Order Statistics Filters." Applied Mechanics and Materials 651-653 (September 2014): 2154–58. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2154.

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Nonlinear processing techniques are very important tools in signal and image processing. One of the most significant classes is based on order statistics, which depends on data sorting algorithm. The sorting algorithm is very time consuming and intractable for vector data. Therefore, it is extremely urgent to put forward a fast algorithm to compute the r-th order statistics without sorting operation. The ITM and ITTM filter proposed an iteration algorithm to approach the median, from the mean. It is reasonab to generalize to the other order statistics by building a new data set Xp which is com

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Subbiah, Stalin, and Suresh Subramanian. "Biomedical Arrhythmia Heart Diseases Classification Based on Artificial Neural Network and Machine Learning Approach." International Journal of Engineering & Technology 7, no. 3.27 (2018): 10. http://dx.doi.org/10.14419/ijet.v7i3.27.17642.

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In present day, several types of developments are carried toward the medical application. There has been increased improvement in the processing of ECG signals. The accurate detection of ECG signals with the help of detection of P, Q, R and S waveform. However these waveforms are suffered from some disturbances like noise. Initially denoising the ECG signal using filters and detect the PQRS waveforms. Four filters are carried out to remove the ECG noises that are Median, Gaussian, FIR and Butterworth filter. ECG signal is analyzed or classify using Extreme Learning Machine (ELM) and it compare

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Atitallah, Ahmed Ben. "An Optimized HW/SW Implementation of the Vector Median Rational Hybrid Filter for Real-Time Color Image Denoising." Traitement du Signal 41, no. 4 (2024): 2135–42. http://dx.doi.org/10.18280/ts.410441.

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Wiryadinata, Romi, Muhammad Rofiki Adli, Rian Fahrizal, and Rocky Alfanz. "Klasifikasi 12 Motif Batik Banten Menggunakan Support Vector Machine." Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) 13, no. 1 (2019): 60–64. https://doi.org/10.21776/jeeccis.v13i1.570.

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Batik adalah kain bergambar yang ditulis atau dicap dengan canting yang terbuat dari tembaga atau plat seng, agar dapat menghasilkan seni keindahan yang artistik dan klasik. Hingga saat ini masih banyak masyarakat Indonesia yang belum mengetahui dengan baik nama-nama aneka ragam motif batik yang menjadi kekayaan intelektual yang telah diakui oleh UNESCO (United Nations Educational, Scientific, and Cultural Organization) pada 2 Oktober 2009 sebagai salah satu warisan kebudayaan dunia yang berasal dari Indonesia. SVM (support vector machine) adalah metode learning machine yang bekerja dengan tuj

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