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Journal articles on the topic 'Hough's transformation'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

DUNG, Le, and Makoto MIZUKAWA. "2P1-G03 Hand feature extraction based on distance transformation and Hough transformation." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2009 (2009): _2P1—G03_1—_2P1—G03_4. http://dx.doi.org/10.1299/jsmermd.2009._2p1-g03_1.

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2

Колякин, Владислав, Vladislav Kolyakin, Владимир Аверченков, Vladimir Averchenkov, Максим Терехов, and Maksim Terekhov. "METHODS FOR MODELS PARAMETERS ESTIMATE TO SOLVE PROBLEMS ON 3D RECONSTRUCTION OF REAL OBJECTS COMPLEX MODELS." Bulletin of Bryansk state technical university 2016, no. 3 (2016): 233–42. http://dx.doi.org/10.12737/22120.

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Virtual threedimensional (3 D) models of complex objects are used in many fields of science and engineering, such as architecture, industry, medicine, robotics. Besides, 3D models are used in geoinformation systems, computer games, virtual and supplemented reality and so on. Three dimensional models can be formed in dif-ferent ways, one of which consists in 3 D reconstruc-tion. One of the stages of the 3 D reconstruction of complex models of real objects is a definition of the mathematical models of geometric primitives emphasized on the image. One of the ways for the estimate of model paramet

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3

DAI Qin, 代勤, 王延杰 WANG Yan-jie, and 韩广良 HAN Guang-liang. "Perspective Image Rectification Based on Improved Hough Transformation and Perspective Transformation." Chinese Journal of Liquid Crystals and Displays 27, no. 4 (2012): 552–56. http://dx.doi.org/10.3788/yjyxs20122704.0552.

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4

Kang, Chee-Woo, Rae-Hong Park, and Kwae-Hi Lee. "Extraction of straight line segments using rotation transformation: generalized hough transformation." Pattern Recognition 24, no. 7 (1991): 633–41. http://dx.doi.org/10.1016/0031-3203(91)90030-9.

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5

Liu, Fei, Wataru Ohyama, Tetsushi Wakabayashi, and Fumitaka Kimura. "Cadastral Map Assembling Using Generalized Hough Transformation." IEEJ Transactions on Electronics, Information and Systems 127, no. 11 (2007): 1894–901. http://dx.doi.org/10.1541/ieejeiss.127.1894.

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6

Sreedevi, M., and P. Jeno Paul. "An Efficient Image Segmentation Using Hough Transformation." Asian Journal of Information Technology 10, no. 3 (2011): 108–12. http://dx.doi.org/10.3923/ajit.2011.108.112.

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7

Jinxue Sui, Li Yang, and Zhen Hua. "Shape Detection based on Improved Hough Transformation." International Journal of Digital Content Technology and its Applications 5, no. 3 (2011): 245–54. http://dx.doi.org/10.4156/jdcta.vol5.issue3.25.

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8

Junji, Ikuo, Hideo, and Toshinori. "Highly accurate segment detection using hough transformation." Systems and Computers in Japan 21, no. 1 (1990): 68–77. http://dx.doi.org/10.1002/scj.4690210107.

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9

Novović, Lena, Vladimir Ostojić, Đorđe Starčević, and Vladimir Petrović. "Radiography calibration marker detection using Hough transformation." Telfor Journal 11, no. 1 (2019): 30–34. http://dx.doi.org/10.5937/telfor1901030n.

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10

Thomae, Nico, Kai-Uwe Plagwitz, Peter Husar, and Günter Henning. "HOUGH-TRANSFORMATION ZUR BILDVERARBEITUNG BEI DER BLICKRICHTUNGSBESTIMMUNG." Biomedizinische Technik/Biomedical Engineering 47, s1b (2002): 636–38. http://dx.doi.org/10.1515/bmte.2002.47.s1b.636.

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11

Shiba, Hisashi. "Underwater vehicle attitude estimation using Hough transformation." Journal of the Acoustical Society of America 123, no. 5 (2008): 3748. http://dx.doi.org/10.1121/1.2935304.

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12

Li, Zhao Yu, Xiang Dong Gao, Xing Huan Zhu, and Jian Wen Lin. "Visual Technique for Detecting Weld Position Based on Hough Transformation." Advanced Materials Research 139-141 (October 2010): 2059–62. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.2059.

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Detecting the weld position is the precondition of seam tracking during an arc welding process, and is crucial to achieve high-quality welds. Visual sensing is one of the most attractive approaches to detect the weld position, which provides valuable information to control the arc following the expected path. The weld edge is main characteristics of the weld position image, here the visual technique for detecting the weld position based on Hough transformation is proposed. The weld position can be detected and recognized through Hough transformation, while the weld edge image is detected using

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13

YOSHIOKA, Hitoshi, Shigeru MURATA, Tadashi KUSHIYAMA, and Hiroshi KISE. "Separation of particle pathlines by Hough transformations." JOURNAL OF THE FLOW VISUALIZATION SOCIETY OF JAPAN 7, Supplement (1987): 3–6. http://dx.doi.org/10.3154/jvs1981.7.supplement_3.

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14

Deng, Ming Hui, Qing Shuang Zeng, and Lan Ying Zhang. "An Information Fusion Algorithm Based on Dopplerlet-Hough Transformation." Advanced Materials Research 605-607 (December 2012): 2211–16. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2211.

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In this paper, a robust image watermarking method with S-Hough transformation is proposed which is robust against geometric distortion. This watermarking is detected by a linear frequency change. The chirp signals are used as watermarks and this type of signals is resistant to all stationary filtering methods and exhibits geometrical symmetry. In the two-dimensional Radon-Wigner transformation domain, the chirp signals used as watermarks change only its position in space/spatial-frequency distribution, after applying linear geometrical attack. But the two-dimensional Radon-Wigner transformatio

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15

Huang, K. Y., K. S. Fu, S. W. Cheng, and Z. S. Lin. "Syntactic pattern recognition and Hough transformation for reconstruction of seismic patterns." GEOPHYSICS 52, no. 12 (1987): 1612–20. http://dx.doi.org/10.1190/1.1442278.

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Hierarchical syntactic pattern recognition and the Hough transformation are proposed for automatic recognition and reconstruction of seismic patterns in seismograms. In the first step, the patterns are hierarchically decomposed or recognized into single patterns, straight‐line patterns, or hyperbolic patterns, using syntactic pattern recognition. In the second step, the Hough transformation technique is used for reconstruction, pattern by pattern. The system of syntactic seismic pattern recognition includes envelope generation, a linking process in the seismogram, segmentation, primitive recog

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16

Song, Zhengxi, Qi Wu, Xue Wang, and Qing Wang. "3D Reconstruction with circular light field by using 3D hough transformation." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 1 (2021): 135–40. http://dx.doi.org/10.1051/jnwpu/20213910135.

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Aiming at the issue of incomplete trajectories in the 2D epipolar image of circular light field, this paper proposes a 3D reconstruction method by using 3D Hough transformation. This method computes 3D point clouds by computing the parameters of feature trajectories in 3D image volume. By analyzing the 3D distribution of circular light field trajectories, binary curves in image volume are extracted, and their local orientation are further estimated by the 3D structure tensor. The 3D Hough space generation and the parameter selection method are proposed to the 3D curves detection. The parameter

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17

Qiu, Zhi Wei, and Cai Li Zhang. "Extraction Method of Path Based on Hough Transformation." Applied Mechanics and Materials 239-240 (December 2012): 1074–78. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1074.

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This paper uses Matlab7.0 software development tool, using Hough transformation and other technical means in the pictures are linear structure information extraction, for future research and deployment to provide reference. But there are still some limitations, such as image segmentation dependence, from nonroad factors affecting such. firstly using the road seed point spectrum information is way of regional growth, extracted from the spectral information consistent with the road area, get a contains road information two value image, and then this two value image filtering, in the extracted ro

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18

Cui Yan, 崔岩, 周鑫昌 Zhou Xinchang, 刘亚飞 Liu Yafei, 刘康 Liu Kang, and 褚金奎 Chu Jinkui. "Solar Meridian Extraction Method Based on Hough Transformation." Acta Optica Sinica 40, no. 17 (2020): 1701002. http://dx.doi.org/10.3788/aos202040.1701002.

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19

Ehrenfried, Klaus. "Processing calibration-grid images using the Hough transformation." Measurement Science and Technology 13, no. 7 (2002): 975–83. http://dx.doi.org/10.1088/0957-0233/13/7/303.

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20

Shiba, Hisashi. "Geometrical distortion correction in attitude estimating Hough transformation." Journal of the Acoustical Society of America 124, no. 4 (2008): 2521. http://dx.doi.org/10.1121/1.4782957.

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21

Bahri, Zaiful. "Metode Pusat dan Circular Hough Transformation untuk Mendeteksi Lingkaran pada Sebuah Citra." Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 11, no. 2 (2020): 301–10. http://dx.doi.org/10.31849/digitalzone.v11i2.5086.

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Tulisan ini membahas tentang penerapan metode pusat dan Circular Hough Transformation(CHT) untuk mendeteksi semua lingkaran yang terkandung dalam citra baik lingkaran tunggal maupun lingkaran yang tumpang tindih. Metode pusat dan CHT memainkan peran penting dalam mendeteksi lingkaran yang terkandung dalam citra melalui array akumulator dua dimensi A(a, b) yang memiliki memori berurutan dengan titik pusat dari lingkaran yang tumpang tindih sehingga dapat dihitung untuk setiap titik pada kurva menggunakan nilai parameter jari-jari yang dipilih untuk mendapatkan nilai triplet (a, b, r) pada Circu

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22

Casasent, David, and Raghuram Krishnapuram. "Curved object location by Hough transformations and inversions." Pattern Recognition 20, no. 2 (1987): 181–88. http://dx.doi.org/10.1016/0031-3203(87)90052-5.

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23

Krishnapuram, Raghuram, and David Casasent. "Hough space transformations for discrimination and distortion estimation." Computer Vision, Graphics, and Image Processing 38, no. 3 (1987): 299–316. http://dx.doi.org/10.1016/0734-189x(87)90115-0.

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24

Fan, Chao, Li Long Hou, Shuai Di, and Jing Bo Xu. "Research on the Lane Detection Algorithm Based on Zoning Hough Transformation." Advanced Materials Research 490-495 (March 2012): 1862–66. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1862.

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In order to improve the adaptability of the lane detection algorithm under complex conditions such as damaged lane lines, covered shadow, insufficient light, the rainy day etc. Lane detection algorithm based on Zoning Hough Transform is proposed in this paper. The road images are processed by the improved ±45° Sobel operators and the two-dimension Otsu algorithm. To eliminate the interference of ambient noise, highlight the dominant position of the lane, the Zoning Hough Transform is used, which can obtain the parameters and identify the lane accurately. The experiment results show the lane de

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25

Kaur, Manpreet. "A REVIEW OF HOUGH TRANSFORMATION BASED LANE DETECTION TECHNIQUES." International Journal of Advanced Research in Computer Science 8, no. 8 (2017): 719–22. http://dx.doi.org/10.26483/ijarcs.v8i8.4843.

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26

Wang, Chun Fang. "Fast Line Extraction Algorithm Based on Improved Hough Transformation." Advanced Materials Research 926-930 (May 2014): 3612–15. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3612.

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Automatic recognition of the line in the image is an important work in the field of machine vision and image processing. Focusing on the problem of the computational cost and large invalid sampling in the line extraction algorithm using standard Hough transform (HT). An improved HT algorithm is proposed to solve these problems. The parameters of the improved algorithm can be reduced to one and the accumulator is operated by setting the tolerance. Then the existence of linear is determined by seting the threshold. The experimental results show that the algorithm not only can effectively solve t

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27

Deng Minghui, Zeng Qingshuang, and Zhang Lanying. "An Information Fusion Algorithm Based on Chirplet-Hough Transformation." Journal of Convergence Information Technology 7, no. 19 (2012): 426–33. http://dx.doi.org/10.4156/jcit.vol7.issue19.50.

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28

YAMAGUCHI, Tomoyuki, Hiroki SAKANIWA, Hiroyuki HAYAMI, and Fujio YAMAGUCHI. "Reconstruction of 3-D Point Using Omnidirectional Hough Transformation." Journal of the Japan Society for Precision Engineering, Contributed Papers 71, no. 2 (2005): 205–10. http://dx.doi.org/10.2493/jspe.71.205.

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29

Ji, Ronghua, and Lijun Qi. "Crop-row detection algorithm based on Random Hough Transformation." Mathematical and Computer Modelling 54, no. 3-4 (2011): 1016–20. http://dx.doi.org/10.1016/j.mcm.2010.11.030.

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30

Xu Chao, 徐超, and 平雪良 Ping Xueliang. "Line Detection Algorithm Based on Improved Random Hough Transformation." Laser & Optoelectronics Progress 56, no. 5 (2019): 051001. http://dx.doi.org/10.3788/lop56.051001.

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31

Flint, A. D. "A prognostic maintenance system based on the Hough Transformation." Transactions of the Institute of Measurement and Control 16, no. 2 (1994): 59–65. http://dx.doi.org/10.1177/014233129401600202.

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32

Montiel, Eugenia, Alberto S. Aguado, and Mark S. Nixon. "Improving the Hough Transform gathering process for affine transformations." Pattern Recognition Letters 22, no. 9 (2001): 959–69. http://dx.doi.org/10.1016/s0167-8655(01)00039-3.

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33

Xu, Bingliang, Dianwen Liu, Guoyin Xu, XiaoLin Zhang, and Lingcheng Bi. "A measurement method for contact angle based on Hough Transformation." Measurement 46, no. 3 (2013): 1109–14. http://dx.doi.org/10.1016/j.measurement.2012.10.023.

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34

Xu, Xiaohong, Zhihui Wu, Yu Chen, Guanlin Huang, and Tonglin Zhu. "Plant root spatial distribution measurements based on the hough transformation." Neurocomputing 145 (December 2014): 209–20. http://dx.doi.org/10.1016/j.neucom.2014.05.041.

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35

Liu, Yuhang, and Shiyu Zhou. "Detecting Point Pattern of Multiple Line Segments Using Hough Transformation." IEEE Transactions on Semiconductor Manufacturing 28, no. 1 (2015): 13–24. http://dx.doi.org/10.1109/tsm.2014.2385600.

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36

Hermann, Eduard, Stephanie Bleicken, Yamunadevi Subburaj, and Ana J. García-Sáez. "Automated analysis of giant unilamellar vesicles using circular Hough transformation." Bioinformatics 30, no. 12 (2014): 1747–54. http://dx.doi.org/10.1093/bioinformatics/btu102.

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37

Chen, Yu Ting, Xiao Lu Pan, Wei Yu, Ling Li, and Qi Biao Ni. "Multiple Planar Intersects Recognition by Hough Transform." Applied Mechanics and Materials 347-350 (August 2013): 3190–93. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3190.

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Detect the intersect lines of multiple planes is one way to recognize and reconstruct the 3D space. First, analyzed the depth data grabbed by SR3000, sampled the depth data into gray bitmap and detect the depth gradient edge. By using the improved Hough Transformation method, get the parameters of spatial intersection lines.

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38

Alicke, Anna, Tobias Stockmanns, and James Ritman. "Track Finding for the PANDA Detector Based on Hough Transformations." EPJ Web of Conferences 251 (2021): 04002. http://dx.doi.org/10.1051/epjconf/202125104002.

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The PANDA experiment at FAIR (Facility for Antiproton and Ion Research) in Darmstadt is currently under construction. In order to reduce the amount of data collected during operation, it is essential to find as many true tracks as possible and to be able to distinguish them from false tracks. Part of the preparation for the experiment is the development of a fast online track finder. This work presents an online track finding algorithm based on Hough transformations, which is comparable in quality and performance to the currently best offline track finder in PANDA. In contrast to most track fi

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39

DEGUCHI, Koichiro, Takashi OKADA, and Iwao MORISHITA. "Analysis of Under-Ground Radar Image Using Generalized Hough Transformation Technique." Transactions of the Society of Instrument and Control Engineers 25, no. 7 (1989): 800–807. http://dx.doi.org/10.9746/sicetr1965.25.800.

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40

Xu, Zhiniu. "A high accurate static contact angle algorithm based on Hough transformation." Measurement 55 (September 2014): 97–100. http://dx.doi.org/10.1016/j.measurement.2014.04.030.

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41

Soshin, K. V., D. P. Nikolaev, S. A. Gladilin, and E. I. Ershov. "Acceleration of Summation Over Segments Using the Fast Hough Transformation Pyramid." Bulletin of the South Ural State University. Series "Mathematical Modelling, Programming and Computer Software" 13, no. 1 (2020): 129–40. http://dx.doi.org/10.14529/mmp200110.

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42

Yao, L., M. P. Moody, J. M. Cairney, et al. "Crystallographic structural analysis in atom probe microscopy via 3D Hough transformation." Ultramicroscopy 111, no. 6 (2011): 458–63. http://dx.doi.org/10.1016/j.ultramic.2010.11.018.

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43

White, K. Preston, Bijoy Kundu, and Christina M. Mastrangelo. "Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transformation." IEEE Transactions on Semiconductor Manufacturing 21, no. 2 (2008): 272–78. http://dx.doi.org/10.1109/tsm.2008.2000269.

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44

Aldoshkin, Dmitry N., and Roman Y. Tsarev. "Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform." Cybernetics and Information Technologies 18, no. 2 (2018): 112–22. http://dx.doi.org/10.2478/cait-2018-0032.

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Abstract This paper proposes an algorithm that assesses the angular orientation of a mobile robot with respect to its referential position or a map of the surrounding space. In the framework of the suggested method, the orientation problem is converted to evaluating a dimensional rotation of the object that is abstracted as a polygon (or a closed polygonal chain). The method is based on Hough transform, which transforms the measurement space to a parametric space (in this case, a two-dimensional space [θ, r] of straight-line parameters). The Hough transform preserves the angles between the str

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45

Sindar, Anita, and Arjon Samuel Sitio. "Sistem Identifikasi Biometrik Ekpresi Wajah Menggunakan Metode Transformasi Hough." Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 3, no. 3 (2020): 262–67. http://dx.doi.org/10.32672/jnkti.v3i3.2722.

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The movement of the eye ball affects the condition of the pupil to dilate to become smaller or vice versa, it indicates a person's mood changes very quickly. The image of the eyeball is not necessarily in accordance with the condition of a person's heart, so it is necessary to analyze the movement of the pupil of the eye. Facial expression using the Hough Transformation Method focuses on the movement of the pupil of the eye. The Hough transform works by looking for the neighbor relationship between pixels using straight line equations to detect lines and circular equations to detect circles. H

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46

Łukianowicz, Czesław. "Identification of machining marks on abrasive treated surfaces, using image processing techniques." Mechanik 91, no. 11 (2018): 1020–22. http://dx.doi.org/10.17814/mechanik.2018.11.181.

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The principles of selected image processing techniques based on Fourier, Radon and Hough transformations are presented. The techniques can be used to quick identification of the machined marks on surfaces after abrasive machining. Example of software that allow the identification of machined marks is shown.

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47

Dorj, Byambaa, and Deok Jin Lee. "A Precise Lane Detection Algorithm Based on Top View Image Transformation and Least-Square Approaches." Journal of Sensors 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4058093.

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The next promising key issue of the automobile development is a self-driving technique. One of the challenges for intelligent self-driving includes a lane-detecting and lane-keeping capability for advanced driver assistance systems. This paper introduces an efficient and lane detection method designed based on top view image transformation that converts an image from a front view to a top view space. After the top view image transformation, a Hough transformation technique is integrated by using a parabolic model of a curved lane in order to estimate a parametric model of the lane in the top v

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48

Sengupta, Madhumita, and J. K. Mandal. "Authentication Through Hough Transformation Generated Signature on G-Let D3 Domain (AHSG)." Procedia Technology 10 (2013): 121–30. http://dx.doi.org/10.1016/j.protcy.2013.12.344.

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49

Yu, Xuemei, and Yaojie Sun. "Research on parking detecting analysis based on projection transformation and Hough transform." Journal of Physics: Conference Series 1187, no. 4 (2019): 042068. http://dx.doi.org/10.1088/1742-6596/1187/4/042068.

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50

Wada, Toshikazu, Makito Seki, and Takashi Matsuyama. "High-precision Î³-Ï hough transformation algorithm to detect arbitrary digital lines." Systems and Computers in Japan 26, no. 3 (1995): 39–52. http://dx.doi.org/10.1002/scj.4690260304.

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