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Journal articles on the topic 'Hough Transform'

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Published: 4 June 2021
Last updated: 1 June 2024

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1

UMEDA, Michio. "Hough Transform." Journal of Japan Society for Fuzzy Theory and Systems 8, no. 2 (1996): 229–33. http://dx.doi.org/10.3156/jfuzzy.8.2_229.

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2

FOKKINGA, MAARTEN. "The Hough transform." Journal of Functional Programming 21, no. 2 (February 24, 2011): 129–33. http://dx.doi.org/10.1017/s0956796810000341.

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Suppose you are given a number of points in a plane and want to have those lines that each contain a large number of the given points. The Hough transform is a computerized procedure for that task. It was invented by Paul Hough (1962), originally to find the trajectories of subatomic particles in a bubble chamber, and it has even been patented. Nowadays, adaptations of the Hough transform are used, among others, for identification of transformed instances of a predefined figure, instead of just a line, in a digital picture. There are plenty of explanations on the Internet (use search key “Hough transform” and “generalized Hough transform”), some with nice applets to demonstrate the working (add search key “applet” or “demo”). Recently, Hart (2009) has looked back at the invention. We show how the original procedure could have been derived.
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3

Du, S. "Rotation Hough Transform." SAIEE Africa Research Journal 105, no. 3 (September 2014): 127–30. http://dx.doi.org/10.23919/saiee.2014.8531534.

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4

Budak, Ümit, Yanhui Guo, Abdulkadir Şengür, and Florentin Smarandache. "Neutrosophic Hough Transform." Axioms 6, no. 4 (December 18, 2017): 35. http://dx.doi.org/10.3390/axioms6040035.

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5

Steier, William H., and Raj K. Shori. "Optical Hough transform." Applied Optics 25, no. 16 (August 15, 1986): 2734. http://dx.doi.org/10.1364/ao.25.002734.

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6

PICTON, P. D. "HOUGH TRANSFORM REFERENCES." International Journal of Pattern Recognition and Artificial Intelligence 01, no. 03n04 (December 1987): 413–25. http://dx.doi.org/10.1142/s021800148700028x.

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7

Dahyot, R. "Statistical Hough Transform." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 8 (August 2009): 1502–9. http://dx.doi.org/10.1109/tpami.2008.288.

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8

Shiu Yin K Yuen, Tze Shan L Lam, and Nang Kwok D Leung. "Connective hough transform." Image and Vision Computing 11, no. 5 (June 1993): 295–301. http://dx.doi.org/10.1016/0262-8856(93)90007-4.

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9

Basak, J., and S. K. Pal. "Hough transform network." Electronics Letters 35, no. 7 (1999): 577. http://dx.doi.org/10.1049/el:19990283.

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10

Han, Joon H., LászlóT Kóczy, and Timothy Poston. "Fuzzy Hough transform." Pattern Recognition Letters 15, no. 7 (July 1994): 649–58. http://dx.doi.org/10.1016/0167-8655(94)90068-x.

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11

Leavers, V. F. "Which Hough Transform?" CVGIP: Image Understanding 58, no. 2 (September 1993): 250–64. http://dx.doi.org/10.1006/ciun.1993.1041.

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12

Leavers, V. "Which Hough Transform?" Computer Vision and Image Understanding 58, no. 2 (September 1993): 250–64. http://dx.doi.org/10.1006/cviu.1993.1043.

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13

Smereka, Marcin, and Ignacy Dulęba. "Circular Object Detection Using a Modified Hough Transform." International Journal of Applied Mathematics and Computer Science 18, no. 1 (March 1, 2008): 85–91. http://dx.doi.org/10.2478/v10006-008-0008-9.

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Circular Object Detection Using a Modified Hough TransformA practical modification of the Hough transform is proposed that improves the detection of low-contrast circular objects. The original circular Hough transform and its numerous modifications are discussed and compared in order to improve both the efficiency and computational complexity of the algorithm. Medical images are selected to verify the algorithm. In particular, the algorithm is applied to localize cell nuclei of cytological smears visualized using a phase contrast microscope.
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14

Sun, Kewen, Baoguo Yu, Mireille Elhajj, Washington Yotto Ochieng, Tengteng Zhang, and Jianlei Yang. "A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner–Hough Transform." Sensors 21, no. 13 (June 24, 2021): 4306. http://dx.doi.org/10.3390/s21134306.

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This paper develops novel Global Navigation Satellite System (GNSS) interference detection methods based on the Hough transform. These methods are realized by incorporating the Hough transform into three Time-Frequency distributions: Wigner–Ville distribution, pseudo -Wigner–Ville distribution and smoothed pseudo-Wigner–Ville distribution. This process results in the corresponding Wigner–Hough transform, pseudo-Wigner–Hough transform and smoothed pseudo-Wigner–Hough transform, which are used in GNSS interference detection to search for local Hough-transformed energy peak in a small limited area within the parameter space. The developed GNSS interference detection methods incorporate a novel concept of zero Hough-transformed energy distribution percentage to analyze the properties of energy concentration and cross-term suppression. The methods are tested with real GPS L1-C/A data collected in the presence of sweep interference. The test results show that the developed methods can deal with the cross-term problem with improved interference detection performance. In particular, the GNSS interference detection performance obtained with the smoothed pseudo-Wigner–Hough transform method is at least double that of the Wigner–Hough transform-based approach; the smoothed pseudo-Wigner–Hough transform-based GNSS interference detection method is improved at least 20% over the pseudo-Wigner–Hough transform-based technique in terms of the zero Hough-transformed energy percentage criteria. Therefore, the proposed smoothed pseudo-Wigner–Hough transform-based method is recommended in the interference detection for GNSS receivers, particularly in challenging electromagnetic environments.
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15

Turan, Jan, Zoran Bojkovic, Peter Filo, Andreja Samcovic, and L'ubos Ovsenik. "Signal processing with continuous Kernel Hough transform." Facta universitatis - series: Electronics and Energetics 18, no. 1 (2005): 113–26. http://dx.doi.org/10.2298/fuee0501113t.

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The paper deals with new modification of Hough transform - Continuous Kernel Hough transform. Definition of Continuous Kernel Hough transform, image processing, system identification and basics of parameter estimation are presented.
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16

Illingworth, J., and J. Kittler. "The Adaptive Hough Transform." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, no. 5 (September 1987): 690–98. http://dx.doi.org/10.1109/tpami.1987.4767964.

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17

Ben-Tzvi, D., and M. B. Sandler. "Counter-based Hough transform." Electronics Letters 26, no. 11 (May 24, 1990): 751–53. http://dx.doi.org/10.1049/el:19900491.

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18

Kiryati, N., Y. Eldar, and A. M. Bruckstein. "A probabilistic Hough transform." Pattern Recognition 24, no. 4 (January 1991): 303–16. http://dx.doi.org/10.1016/0031-3203(91)90073-e.

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19

Zeevi, Assaf, and Alexander Goldenshluger. "The Hough transform estimator." Annals of Statistics 32, no. 5 (October 2004): 1908–32. http://dx.doi.org/10.1214/009053604000000760.

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20

Ben-Tzvi, D., and M. B. Sandler. "A combinatorial Hough transform." Pattern Recognition Letters 11, no. 3 (March 1990): 167–74. http://dx.doi.org/10.1016/0167-8655(90)90002-j.

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21

Jeng, Sheng-Ching, and Wen-Hsiang Tsai. "Fast generalized Hough transform." Pattern Recognition Letters 11, no. 11 (November 1990): 725–33. http://dx.doi.org/10.1016/0167-8655(90)90091-f.

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22

Kiryati, N., and A. M. Bruckstein. "Antialiasing the Hough transform." CVGIP: Graphical Models and Image Processing 53, no. 3 (May 1991): 213–22. http://dx.doi.org/10.1016/1049-9652(91)90043-j.

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23

Chai, Yu, Su Jing Wei, and Xin Chun Li. "The Multi-Scale Hough Transform Lane Detection Method Based on the Algorithm of Otsu and Canny." Advanced Materials Research 1042 (October 2014): 126–30. http://dx.doi.org/10.4028/www.scientific.net/amr.1042.126.

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In order to improve the accuracy of detecting lane for automatic vehicle driving, a method for detecting the straight part of Lane is proposed, which is the Multi-Scale Hough transform method for lane detection based on the algorithm of Otsu and Canny. First of all, by the methods of Otsu to segment image and use the morphology operation of erode and dilate to wipe off the information of roadside trees and fences to strengthen the road boundary characteristics.Then the lane edge and feature is gained by the canny operator. At last, using Standard Hough Transform, Progressiveness Probabilities Hough Transform and Multi-Scale Hough Transform complete the detection of lane’s straight part. The experimental results show that, Multi-Scale Hough Transform method can accurately detect the lane line and provide the reliable basis for the path planning, automatic follow-up vehicle driving and lane departure warning.
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24

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 (June 1, 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 straight lines during rotation, translation, and isotropic scaling transformations. The problem of rotation assessment then becomes a one-dimensional optimization problem. The suggested algorithm inherits the Hough method’s robustness to noise.
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25

Tao, Xiaodong, and Alwyn Eades. "Anomalies in the Hough Transform of Kikuchi Bands in EBSD." Microscopy and Microanalysis 7, S2 (August 2001): 364–65. http://dx.doi.org/10.1017/s1431927600027896.

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The Hough Transform is widely used to detect linear features in image processing techniques. However, the features sought in the EBSD patterns are typically Kikuchi bands from low-index crystal planes, which are not lines but are bands of above average intensity bordered by dark bands. This gives rise to a characteristic shaped peak in the Hough transform that has been called the “butterfly” shape. in most cases, to reduce noise, the Hough Transform is convoluted with a mask having a matching “butterfly” shape. Unfortunately, this method sacrifices resolution through averaging the intensity of neighboring pixels. We have been concerned to use the Hough transform to locate the position of the Kikuchi bands with the highest possible precision. in order to achieve this goal we have looked into the way the Hough transform works on Kikuchi bands in detail, and found some anomalies that must be considered if accuracy is to be achieved.
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26

Donchenko, Vladimir S., and Nikolay Fedorovich Kirichenko. "Fast Hough Transform and Pseudoinversion." Journal of Automation and Information Sciences 34, no. 4 (2002): 10. http://dx.doi.org/10.1615/jautomatinfscien.v34.i4.50.

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27

Cucchiara, R., and F. Filicori. "The vector-gradient Hough transform." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 7 (July 1998): 746–50. http://dx.doi.org/10.1109/34.689304.

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28

Kesidis, A. L., and N. Papamarkos. "On the inverse Hough transform." IEEE Transactions on Pattern Analysis and Machine Intelligence 21, no. 12 (1999): 1329–43. http://dx.doi.org/10.1109/34.817411.

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29

Timmermakn, D., H. Hahn, and B. J. Hosticka. "Hough transform using Cordic method." Electronics Letters 25, no. 3 (1989): 205. http://dx.doi.org/10.1049/el:19890147.

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30

Ben-Tzvi, D., and M. Sandler. "Analogue implementation of Hough transform." Electronics Letters 25, no. 18 (1989): 1216. http://dx.doi.org/10.1049/el:19890815.

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31

Mukhopadhyay, Priyanka, and Bidyut B. Chaudhuri. "A survey of Hough Transform." Pattern Recognition 48, no. 3 (March 2015): 993–1010. http://dx.doi.org/10.1016/j.patcog.2014.08.027.

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32

Thazhuthaveetil, Matthew J., and Anish V. Shah. "Parallel hough transform algorithm performance." Image and Vision Computing 9, no. 2 (April 1991): 88–92. http://dx.doi.org/10.1016/0262-8856(91)90017-j.

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33

Hare, A. R., and M. B. Sandler. "Improved-performance ‘randomised’ Hough transform." Electronics Letters 28, no. 18 (1992): 1678. http://dx.doi.org/10.1049/el:19921067.

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34

Satzoda, R. K., S. Suchitra, and T. Srikanthan. "Parallelizing the Hough Transform Computation." IEEE Signal Processing Letters 15 (2008): 297–300. http://dx.doi.org/10.1109/lsp.2008.917804.

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35

Wolfson, Haim J. "Generalizing the generalized hough transform." Pattern Recognition Letters 12, no. 9 (September 1991): 565–73. http://dx.doi.org/10.1016/0167-8655(91)90157-h.

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36

Kiryati, N., M. Lindenbaum, and A. M. Bruckstein. "Digital or analog Hough transform?" Pattern Recognition Letters 12, no. 5 (May 1991): 291–97. http://dx.doi.org/10.1016/0167-8655(91)90412-f.

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37

Chung, Kuo-Liang, and Horn-Yi Lin. "Hough Transform on Reconfigurable Meshes." Computer Vision and Image Understanding 61, no. 2 (March 1995): 278–84. http://dx.doi.org/10.1006/cviu.1995.1020.

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38

Kowalski, Paweł. "Improved Hardware Hough Transform implementation." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 3 (March 10, 2023): 207–10. http://dx.doi.org/10.15199/48.2023.03.36.

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39

Faruq, Md Omar, Md Almash Alam, and Md Muktar Hossain. "A Comparisonal Study on Circle Detection for Real-World Images." Bangladesh Journal of Multidisciplinary Scientific Research 1, no. 2 (July 28, 2019): 19–25. http://dx.doi.org/10.46281/bjmsr.v1i2.364.

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Real-life objects have different characteristics such as form characteristics, texture characteristics, and color characteristics and so on. The circular objects are the most common shape in our day to day lives and industrial production. So circle detection algorithm is ever ending research today. The most common algorithm is Circular Hough Transform which is used to detect a circle in an image. It is not very robust to noise so a simple approach to modified Circular Hough Transform algorithm is applied to detect the circle from an image. The image is pre-processed by edge detection. A comparison between Circular Hough Transform and modified Circular Hough Transform algorithm is presented in this research.
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40

TONG, FRANK, and ZE-NIAN LI. "ON IMPROVING THE ACCURACY OF LINE EXTRACTION IN HOUGH SPACE." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 05 (December 1992): 831–47. http://dx.doi.org/10.1142/s0218001492000424.

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This paper presents an accurate line extraction technique — the Hierarchical Peak Compaction Hough Transform (HPCHT). Vote scattering in the parameter space is a problem when the Hough transform is used for line extraction. This paper investigates the effects of image size and edge data errors on the severity of vote scattering. The HPCHT uses the Hough procedure on small subimages initially, and a recursive Hough merging scheme on the extracted line segments afterwards. A bound on vote scattering has been derived which guides the image subdivision and the adaptive quantization of the parameter space. As a result, an accurate Hough transform of low ρ-scattering and high θ-precision has been achieved. The HPCHT is suitable for fast parallel implementation on pyramid computers.
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41

Ismail, Raneem, and Szilvia Nagy. "A Novel Gradient-Weighted Voting Approach for Classical and Fuzzy Circular Hough Transforms and Their Application in Medical Image Analysis—Case Study: Colonoscopy." Applied Sciences 13, no. 16 (August 8, 2023): 9066. http://dx.doi.org/10.3390/app13169066.

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Classical circular Hough transform was proven to be effective for some types of colorectal polyps. However, the polyps are very rarely perfectly circular, so some tolerance is needed, that can be ensured by applying fuzzy Hough transform instead of the classical one. In addition, the edge detection method, which is used as a preprocessing step of the Hough transforms, was changed from the generally used Canny method to Prewitt that detects fewer edge points outside of the polyp contours and also a smaller number of points to be transformed based on statistical data from three colonoscopy databases. According to the statistical study we performed, in the colonoscopy images the polyp contours usually belong to gradient domain of neither too large, nor too small gradients, though they can also have stronger or weaker segments. In order to prioritize the gradient domain typical for the polyps, a relative gradient-based thresholding as well as a gradient-weighted voting was introduced in this paper. For evaluating the improvement of the shape deviation tolerance of the classical and fuzzy Hough transforms, the maximum radial displacement and the average radius were used to characterize the roundness of the objects to be detected. The gradient thresholding proved to decrease the calculation time to less than 50% of the full Hough transforms, and the number of the resulting circles outside the polyp’s environment also decreased, especially for low resolution images.
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42

Hansen, Klaus, and Jens Damgaard Andersen. "Understanding the Hough transform: Hough cell support and its utilisation." Image and Vision Computing 15, no. 3 (March 1997): 205–18. http://dx.doi.org/10.1016/s0262-8856(96)01128-6.

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43

Gulyaev, P. V. "Application of the Hough Transform to Dispersion Control of Overlapping Particles and Their Agglomerates." Devices and Methods of Measurements 14, no. 3 (October 6, 2023): 199–206. http://dx.doi.org/10.21122/2220-9506-2023-14-3-199-206.

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The dispersion control of micro- and nanoparticles by their images is of great importance for ensuring the specified properties of the particles themselves and materials based on them. The aim of this article was to consider the possibilities of using the Hough transform for dispersion control of overlapping particles and their agglomerates. Analysis of the application of the Hough transform for overlapping particles and their agglomerates showed the following. The particularities of the conventional implementation lead to the preferred registration of large particles, the shift of the centers of overlapping particles, and the distortion of the size values. To use the Hough transform correctly, fine-tuning of all its parameters is required. To automate this process, the dependences of the number and size of particles recorded in the image on the parameters of the Hough transform was investigated. The studies were carried out on test images with a known number and size of particles. The results showed that when the threshold parameters of the Hough transform change, the number of detected particles stabilizes near their optimal values. When the size range of particles detected by the Hough transform changes, the histogram of the particle size distribution changes. In this case, the optimal width of the range is determined by the most stable extremes of the histogram. The maximum center-to-center distance is set at least half of the optimal range. The configuration algorithm is described and implemented. It implies repeatedly running the Hough transform with different combinations of parameters. The algorithm includes stages of coarse and fine-tuning, which allows to getting closer to the optimal parameters. The efficiency of the algorithm has been confirmed on test and real images. Tests have shown that the errors in determining the size and number of particles of the multi-pass Hough transform are on the same level or exceed these indicators for analog methods.
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44

Li, Jing, Huai Yu Liu, and Liu Rong Hong. "Detecting Object by Affine Transform Using Line." Advanced Materials Research 490-495 (March 2012): 1306–10. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1306.

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Hough transform is an effective way in object recognition and applied to many industrial processes. Based on the principle of Hough transform, a new algorithm which can detect objects through an affine transform was proposed in this paper. First, application of Hough transform to extract straight lines in a model image and a scene image, got these coordinates of the lines, sorted according to the direction angle. Because of affine transform and the periodic direction angle, the direction order of the lines on scene image were different from those on the model image, these lines on scene image were expanse a cycle. Finally affine transform parameters were applied to objects detection. The results showed the effectiveness of the algorithm.
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45

Chen, Jian Jun, Yi Jun Gao, and Zhao Ju Deng. "Counting of Microscopic Cells Based on Grads Hough Transform." Advanced Materials Research 383-390 (November 2011): 7607–12. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.7607.

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In order to improve the accuracy and efficiency of automatic counting of microscopic cells, the method based on the Hough transform has been proposed. And the standard Hough transform has been improved using image gradient information. Compared with the traditional counting methods based on mathematical morphology and boundary tracking tags, the accuracy of the counting accuracy has been greatly improved. The results show the accuracy and efficiency of counting of the microscopic cells based on grads Hough transform is improved.
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46

Cai, Jian-Hua, and Wei-Wen Hu. "Feature Extraction of Gear Fault Signal Based on Sobel Operator and WHT." Shock and Vibration 20, no. 3 (2013): 551–59. http://dx.doi.org/10.1155/2013/367045.

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Taking Wigner-Ville distribution of gear fault signal as a picture,Sobeloperator was applied for edge detection of picture and then Hough transform was used to extract signal feature. Some simulated and measured signals have been processed to demonstrate the effectiveness of new method, which was compared with traditional Wigner-Hough transform and SPWD-Hough transform. The results show that the proposed method can suppress cross term which is produced from using Wigner-Ville distribution to analyze multi-component signal, especially under the condition of low signal to noise ratio. The improved Wigner-Hough transform can effectively suppress the influence of noise and has a good real-time performance because its algorithm is fast. The proposed method provides an effective method to determine the state of gear accurately.
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47

da Fontoura Costa, Luciano, and Harry Wechsler. "Guest Editorial: Special Issue on the Hough Transform. Has the Hough Transform Come of Age?" Real-Time Imaging 6, no. 2 (April 2000): 77–78. http://dx.doi.org/10.1006/rtim.1999.0205.

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48

Ketcham, Mahasak, and Thittaporn Ganokratanaa. "The analysis of lane detection algorithms using histogram shapes and Hough transform." International Journal of Intelligent Computing and Cybernetics 8, no. 3 (August 10, 2015): 262–78. http://dx.doi.org/10.1108/ijicc-05-2014-0024.

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Purpose – The purpose of this paper is to develop a lane detection analysis algorithm by Hough transform and histogram shapes, which can effectively detect the lane markers in various lane road conditions, in driving system for drivers. Design/methodology/approach – Step 1: receiving image: the developed system is able to acquire images from video files. Step 2: splitting image: the system analyzes the splitting process of video file. Step 3: cropping image: specifying the area of interest using crop tool. Step 4: image enhancement: the system conducts the frame to convert RGB color image into grayscale image. Step 5: converting grayscale image to binary image. Step 6: segmenting and removing objects: using the opening morphological operations. Step 7: defining the analyzed area within the image using the Hough transform. Step 8: computing Houghline transform: the system operates the defined segment to analyze the Houghline transform. Findings – This paper presents the useful solution for lane detection by analyzing histogram shapes and Hough transform algorithms through digital image processing. The method has tested on video sequences filmed by using a webcam camera to record the road as a video file in a form of avi. The experimental results show the combination of two algorithms to compare the similarities and differences between histogram and Hough transform algorithm for better lane detection results. The performance of the Hough transform is better than the histogram shapes. Originality/value – This paper proposed two algorithms by comparing the similarities and differences between histogram shapes and Hough transform algorithm. The concept of this paper is to analyze between algorithms, provide a process of lane detection and search for the algorithm that has the better lane detection results.
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49

Patil, Vijaya, Vaishali Kumbhakarna, and Dr Seema Kawathekar. "Detection of Optic Disc in Retina Using Hough Transform." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 3 (January 23, 2016): 6613–17. http://dx.doi.org/10.24297/ijct.v15i3.1676.

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We propose a method to automatically locate the Optic Disc (OD) in fundus images of the retina. Based on the properties of the OD, our proposed method includes edge detection using the Canny method, and detection of circles using the Hough transform. The Hough transform assists in the detection of the center and radius of a circle that approximates the margin of the OD. Based on the feature that the OD is one of the brightest areas in fundus image, the potential circles can be detected by Hough transform.
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50

Ren, Long, Jia Wen Liao, Jian Zhong Cao, Hua Wang, Xiao Dong Zhao, and Han Meng. "An Improved Hough Transform Algorithm Based on Pyramid Method." Applied Mechanics and Materials 543-547 (March 2014): 1917–21. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1917.

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Hough Transform[has become a common method in the usage of line detection because of its robustness. It is important in computer vision and image analysis. Usually, the standard Hough transform method (SHT) transform the points in image space into parameter space and vote for all the possible patterns passing through that point. But, there are two serious problems in the standard method of line detection. The first is the high computation complexity and the second is the large storage requirements .In order to solve the two problems, this paper raise a fast-Hough transform algorithm base on pyramid algorithm. First of all we need to desample the primitive binary image with n times; and execute the Hough transform in the nth level image to get the parameter of straight line in this image, which is used in the n-1 level image. Finally we can get the parameter of lines in the primitive image. Experiments show that this method can extremely reduces the computational time.
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