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Journal articles on the topic 'Hough algorithm'

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Published: 5 June 2025

Last updated: 16 July 2025

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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 (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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Zhao, Changfu, Hongchang Ding, Guohua Cao, and Ying Zhang. "A New Method for Detecting Compensation Hole Parameters of Automobile Brake Master Cylinder Based on Machine Vision." Journal of Advanced Transportation 2021 (April 8, 2021): 1–14. http://dx.doi.org/10.1155/2021/8864679.

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The machining accuracy of the compensation hole of the automobile brake master cylinder directly determines the safety of the automobile and the reliability of parking. How to detect the parameters of the compensation hole with high precision becomes a crucial issue. In this paper, by analyzing the principle of Hough transform detection technology and several optimization algorithms, a new method combining Zernike moment and improved gradient Hough transform is proposed to detect the circular hole parameters. The simulation experiment shows that the proposed algorithm satisfies 0.1 pixels in the coordinate detection of the center position, and the radius detection accuracy is 0.05 pixels, with fast detection speed and good robustness. Compared with the random Hough transform algorithm and the gradient Hough transform algorithm, the algorithm proposed in this paper has higher detection accuracy, faster detection speed, and better robustness, which meets the online detection accuracy requirements of the brake master cylinder compensation hole.

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WANG, QIANG, HONGBO CHEN, XIAORONG XU, and HAIYAN LIU. "A NEWLY MODIFIED ALGORITHM OF HOUGH TRANSFORM FOR LINE DETECTION." International Journal of Image and Graphics 05, no. 04 (2005): 715–27. http://dx.doi.org/10.1142/s0219467805001975.

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The heavy burden of computational complexity and massive storage requirement is the drawback of the standard Hough transform (SHT). To overcome the weakness of SHT, many modified approaches, for example, the probabilistic Hough transform (PHT), have been presented. However, a very important fact, which is that a line has its own width in a real digital image and the width of the line is uniform, was ignored by all of these modified algorithms of Hough transform. This phenomenon influenced the result of line detection. In this paper a new modified algorithm of Hough transform for line detection is proposed. In our algorithm, the fact mentioned above is fully considered and a strip-shaped area corresponding to the accumulate cells of HT is proposed. Experimental results have shown that our approach is efficient and promising, and the effect of detection is far better than the popular modified approaches.

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

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Smereka, Marcin, and Ignacy Dulęba. "Circular Object Detection Using a Modified Hough Transform." International Journal of Applied Mathematics and Computer Science 18, no. 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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Zhou, Mingkuan, Weiwei Wang, Shenqing Shi, Zhen Huang, and Tao Wang. "Research on Global Navigation Operations for Rotary Burying of Stubbles Based on Machine Vision." Agriculture 15, no. 1 (2025): 114. https://doi.org/10.3390/agriculture15010114.

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In order to plan suitable navigation operation paths for the characteristics of rice fields in the middle and lower reaches of the Yangtze River and the operational requirements of straw rotary burying, this paper proposes a combination of the Hough matrix and RANSAC algorithms to extract the starting routes of straw boundaries; the algorithm adopts the Hough matrix to extract the characteristic points of the straw boundaries and remove the redundancies, and then reduces the influence of noise points caused by different straw shapes using the RANSAC algorithm to improve the accuracy of the starting route extraction. The algorithm extracts the starting routes of straw boundaries and the characteristic points of the straw boundaries and removes the redundancies, so as to improve the accuracy of the starting route extraction. The extraction test shows that under different scenes, the recognition accuracy of the path extraction method combining the Hough matrix and RANSAC algorithm is above 90%, and the algorithm takes no more than 0.51 s. Finally, the road test shows that the method meets the characteristics of tractor operation with a large turning radius and without reversing and satisfies the unmanned operation requirements of straw rotary burying in the field.

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CHANG, KUAN-TSUNG, and TIAN-YUAN SHIH. "LINEAR FEATURES EXTRACTION WITH AN ORIENTATION CONSTRAINED PROBABILISTIC HOUGH TRANSFORM." International Journal of Image and Graphics 08, no. 01 (2008): 157–68. http://dx.doi.org/10.1142/s0219467808003027.

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This study proposed a Hough Transform algorithm based on the probabilistic scheme termed as the Orientation Constrained Probabilistic Hough Transform. The orientation constraints for segmentation are applied to form compact and reliable sampling subsets. This process is subsequently followed by constrained searching. Numerical experiments are performed with both synthetic and real datasets indicate that the proposed method performs better than other algorithms in terms of correctness and omission rate. Moreover, computational time deemed necessary is much less than that for other algorithms.

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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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Li, Qiang, and Qi Yuan Sun. "Research on Matching Pair Purification Methods of Image Based on SIFT Algorithm." Applied Mechanics and Materials 713-715 (January 2015): 1851–54. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1851.

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In view of the SIFT algorithm in image matching will produce a lot of mismatches, the paper has applied a method which is based on Hough Transform will remove the SIFT matching error effectively. Firstly, to use the SIFT algorithm finish the image matching roughly. And then, using the Hough Transform to form the equal division hough units. And according to the matching parameter to distribute all the match into the hough units. The match in the units which has least matching-pair will be deleted. Experimental results show that the method can effectively improve the matching accuracy of feature matching and it lays a foundation for the following robot vision navigation.

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Dineva, Tatyana, Nely Ruseva, and Mariya Georgieva-Nikolova. "A COMPARATIVE ANALYSIS OF ALGORITHMS FOR COUNTING OBJECTS IN IMAGES." International Conference on Technics, Technologies and Education, no. 1 (2018): 310–14. http://dx.doi.org/10.15547/ictte.2018.08.002.

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The report presents a comparative analysis of algorithms for counting objects in images. They are used in counting eggs. From the three algorithms compared Threshold, Circular Hough and Wateshed, with high performance and small error values is the algorithm Circular Hough. When recognizing the eggs, it is necessary to make a selection of the color model, by which to separate the eggs from the background according to their color and the breed of birds. More research is needed on the impact of the image capturing conditions on the accuracy of algorithms.

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Tagzout, Samir, Karim Achour, and Oualid Djekoune. "Hough transform algorithm for FPGA implementation." Signal Processing 81, no. 6 (2001): 1295–301. http://dx.doi.org/10.1016/s0165-1684(00)00248-6.

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Nair, P. S., and A. T. Saunders. "Hough transform based ellipse detection algorithm." Pattern Recognition Letters 17, no. 7 (1996): 777–84. http://dx.doi.org/10.1016/0167-8655(96)00014-1.

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KAVIANPOUR, A., and N. BAGHERZADEH. "PARALLEL ALGORITHMS FOR LINE DETECTION ON A PYRAMID ARCHITECTURE." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (1994): 337–49. http://dx.doi.org/10.1142/s0218001494000164.

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This paper considers the problem of detecting lines in images using a pyramid architecture. The approach is based on the Hough Transform calculation. A pyramid architecture of size n is a fine-grain architecture with a mesh base of size [Formula: see text] processors each holding a single pixel of the image. The pyramid operates in an SIMD mode. Two algorithms for computing the Hough Transform are explained. The first algorithm initially uses different angles, θj’s, and its complexity is O(k+log n) with O(m) storage requirement. The second algorithm computes the Hough Transform in a pipeline fashion for each angle θj at a time. This method produces results in O(k * log n) time with O(1) storage, where k is the number of θj angles, m is the number of ρi normal distances from the origin, and n is the number of pixels. A simulation program is also described.

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Shao, Shuai. "Research on Lane Line Detection Algorithm based on Improved Hough Transform." Frontiers in Science and Engineering 2, no. 6 (2022): 1–7. http://dx.doi.org/10.54691/fse.v2i6.964.

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In order to solve the problems existing in the traditional lane line detection algorithm, such as weak robustness and low accuracy, an improved Hough transform lane line detection algorithm is proposed. Firstly, the method preprocesses the road image, including gray transformation, histogram equalization, Gaussian filtering, image binarization and so on. Then the canny edge detection operator is used to detect the edge of the image to be processed, and an improved Hough transform algorithm is proposed. Simulation results show that the algorithm has stronger robustness and higher accuracy than the traditional algorithm.

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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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ZENG, J., J. ZHANG, L. XIANG, Z. DONG, and S. YUAN. "AN IMPROVED HOUGH TRANSFORM ALGORITHM FOR RADAR DETECTION." Journal of Circuits, Systems and Computers 19, no. 03 (2010): 549–55. http://dx.doi.org/10.1142/s0218126610006281.

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In this paper, we describe a new algorithm for radar detection based on the Hough transform which employs the slope-intercept parameter space. Unlike the conventional Hough transform, we shift the parameter space cells to perform the transform. The computation burden is reduced. Another advantage is that those straight lines whose intercept are bigger than the radar maximum range can also be detected. In addition, we also investigate the performance of the algorithm we present and show its efficiency with some simulations.

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Bieniecki, Wojciech, and Sebastian Stoliński. "Identification and Assessment of Selected Handwritten Function Graphs Using Least Square Approximation Combined with General Hough Transform." Image Processing & Communications 22, no. 4 (2017): 23–42. http://dx.doi.org/10.1515/ipc-2017-0019.

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Abstract The paper provides a comparison of three variants of algorithms for automatic assessment of some examination tasks involving sketching a function graph based on image processing. Three types of functions have been considered: linear, quadratic, and trigonometric. The assumption adopted in the design of the algorithm is to map the way the examiner assesses the solutions and to achieve the evaluation quality close to the one obtained in manual evaluation. In particular, the algorithm should not reject a partly correct solution and also extract the correct solution from other lines, deletions and corrections made by a student. Essential subproblems to solve in our scheme concern image segmentation, object identification and automatic understanding. We consider several techniques based on Hough Transform, least square fitting and nearest neighbor based classification. The most reliable solution is an algorithm combining least square fitting and Hough Transform.

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Calero-Castro, Francisco José, Sheila Pereira, Imán Laga, et al. "Quantification and Characterization of CTCs and Clusters in Pancreatic Cancer by Means of the Hough Transform Algorithm." International Journal of Molecular Sciences 24, no. 5 (2023): 4278. http://dx.doi.org/10.3390/ijms24054278.

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Circulating Tumor Cells (CTCs) are considered a prognostic marker in pancreatic cancer. In this study we present a new approach for counting CTCs and CTC clusters in patients with pancreatic cancer using the IsofluxTM System with the Hough transform algorithm (Hough-IsofluxTM). The Hough-IsofluxTM approach is based on the counting of an array of pixels with a nucleus and cytokeratin expression excluding the CD45 signal. Total CTCs including free and CTC clusters were evaluated in healthy donor samples mixed with pancreatic cancer cells (PCCs) and in samples from patients with pancreatic ductal adenocarcinoma (PDAC). The IsofluxTM System with manual counting was used in a blinded manner by three technicians who used Manual-IsofluxTM as a reference. The accuracy of the Hough-IsofluxTM approach for detecting PCC based on counted events was 91.00% [84.50, 93.50] with a PCC recovery rate of 80.75 ± 16.41%. A high correlation between the Hough-IsofluxTM and Manual-IsofluxTM was observed for both free CTCs and for clusters in experimental PCC (R2 = 0.993 and R2 = 0.902 respectively). However, the correlation rate was better for free CTCs than for clusters in PDAC patient samples (R2 = 0.974 and R2 = 0.790 respectively). In conclusion, the Hough-IsofluxTM approach showed high accuracy for the detection of circulating pancreatic cancer cells. A better correlation rate was observed between Hough-IsofluxTM approach and with the Manual-IsofluxTM for isolated CTCs than for clusters in PDAC patient samples.

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Xu, Hai Rong, and Wen Hua Lu. "The Research on Fastener Image Skew Detection." Applied Mechanics and Materials 121-126 (October 2011): 4224–28. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4224.

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When image digitalization device translate fastener into fastener image, the fastener image is always skewed to some extent, which will result in failure of the subsequent processing. The correction of the fastener image is the important step in its automatic recognition. In order to overcome the heavy computing burdens of hough transform, a new usage of hough transform is introduced in this paper. The algorithm works by first doing certain process on input image, the close edge of the image is gotten. Then a two-stage Hough transform algorithm is applied to the image to calculate the angle of the main edge line. This angle is thought as the declining angle of the fastener image. Lastly, the orientated image is rectified using rotation method. This algorithm is validated through experimental results.

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Lee, Jong Pyo, Qian Qian Wu, Min Ho Park, Cheol Kyun Park, and Ill Soo Kim. "A Study on Modified Hough Algorithm for Image Processing in Weld Seam Tracking System." Advanced Materials Research 1088 (February 2015): 824–28. http://dx.doi.org/10.4028/www.scientific.net/amr.1088.824.

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In modern market, achieving mechanical and automatic arc welding process is the key issue to be solved in welding industries. Because of the high complexity of the welding environment, manual detection of the weld line information is hard to be successful and time consuming. Therefore, this study aim at developing a new image processing algorithm for seam tracking system in Gas Metal Arc (GMA) welding by modified Hough algorithm based on the laser vision system. Firstly, noises in the captured weld seam images by CCD camera were effectively removed by noise filtering algorithm and then weld joint position were detected by the modified Hough algorithm to realize the automatic weld seam tracking. To verify the efficiency of the developed image processing model, a common image processing method was employed and the processed results were compared with the proposed algorithm. Statistical results proved that the modified Hough algorithm was able to acquire the weld information precisely with less computing time and memory cost, which also capable for industrial application.

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Tian, Yifei, Wei Song, Long Chen, Yunsick Sung, Jeonghoon Kwak, and Su Sun. "Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds." Applied Sciences 10, no. 5 (2020): 1744. http://dx.doi.org/10.3390/app10051744.

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Plane extraction is regarded as a necessary function that supports judgment basis in many applications, including semantic digital map reconstruction and path planning for unmanned ground vehicles. Owing to the heterogeneous density and unstructured spatial distribution of three-dimensional (3D) point clouds collected by light detection and ranging (LiDAR), plane extraction from it is recently a significant challenge. This paper proposed a parallel 3D Hough transform algorithm to realize rapid and precise plane detection from 3D LiDAR point clouds. After transforming all the 3D points from a Cartesian coordinate system to a pre-defined 3D Hough space, the generated Hough space is rasterised into a series of arranged cells to store the resided point counts into individual cells. A 3D connected component labeling algorithm is developed to cluster the cells with high values in Hough space into several clusters. The peaks from these clusters are extracted so that the targeting planar surfaces are obtained in polar coordinates. Because the laser beams emitted by LiDAR sensor holds several fixed angles, the collected 3D point clouds distribute as several horizontal and parallel circles in plane surfaces. This kind of horizontal and parallel circles mislead plane detecting results from horizontal wall surfaces to parallel planes. For detecting accurate plane parameters, this paper adopts a fraction-to-fraction method to gradually transform raw point clouds into a series of sub Hough space buffers. In our proposed planar detection algorithm, a graphic processing unit (GPU) programming technology is applied to speed up the calculation of 3D Hough space updating and peaks searching.

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Munawir, Munawir, Amelia Wandini, and Ahmad Ihsan. "LINE PATH DETECTION ON HIGHWAYS USING THE HOUGH TRANSFORM METHOD." Jurnal Teknik Informatika (Jutif) 6, no. 1 (2025): 221–28. https://doi.org/10.52436/1.jutif.2025.6.1.1955.

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Lane line detection on highways is an important problem in the development of intelligent transportation technology or autonomous vehicles. One commonly used method is the Hough Transform method, which is known for its excellent level of accuracy and effectiveness. Line lane detection aims to identify and monitor line lanes on highways, which helps direct and limit vehicle traffic and ensures the safety and efficiency of vehicle movement. This research uses video images from cellphone cameras that have been taken previously. The image is then processed using the Hough Transform algorithm to detect line paths on the highway. The aim of this research is to create a line lane detection system on highways that is able to identify line lanes in various road conditions by utilizing the Hough Transform Algorithm. Apart from that, it also aims to test the ability of the Hough Transform algorithm in the lane line detection system which can provide a warning if the driver is too close to the line lane, increasing safety on the road. Even though there are several obstacles such as poor road conditions, unclear or faded line paths, and busy traffic situations, the results of this research show that the Hough Transform method can be used to detect line paths on highways well, and the level of accuracy is sufficient high namely 83%.

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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 (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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Zhou, Bing, and Yang He. "Fast Circle Detection Using Spatial Decomposition of Hough Transform." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 03 (2017): 1755006. http://dx.doi.org/10.1142/s0218001417550060.

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Circles are important patterns in many automatic image inspection applications. The Hough Transform (HT) is a popular method for extracting shapes from original images. It was first introduced for the recognition of straight lines, and later extended to circles. The drawbacks of standard Hough Transform (SHT) for circle detection are the large computational and storage requirements. In this paper, we propose a modified HT called Vector Quantization of Hough Transform (VQHT) to detect circles more efficiently. The basic idea is to first decompose the edge image into many subimages by using Vector Quantization (VQ) algorithm based on their natural spatial relationships. The edge points resided in each subimage are considered as one circle candidate group. Then the VQHT algorithm is applied for fast circle detection. A new paradigm to store potential curve parameters is also proposed, which can exponentially reduce the storage space for HT algorithm. Experimental results show that the proposed algorithm can quickly and accurately detect multiple circles from the noisy background.

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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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Bergen, James R., and Haim Shvaytser (Schweitzer). "A probabilistic algorithm for computing Hough transforms." Journal of Algorithms 12, no. 4 (1991): 639–56. http://dx.doi.org/10.1016/0196-6774(91)90037-y.

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Vuckovic, Vladan, and Boban Arizanovic. "Automatic optimized document skew pre-processor for character segmentation algorithm." Facta universitatis - series: Electronics and Energetics 30, no. 4 (2017): 611–25. http://dx.doi.org/10.2298/fuee1704611v.

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In this paper, as a part of character segmentation algorithm, an automatic optimized document skew correction approach based on Hough transform is presented. The importance of skew correction in document image analysis lies in the fact that further processing is impossible if the document image is skewed. The proposed approach is based on fast implementation of the standard Hough transform which is followed by highly optimized low-level machine code implementation of the image rotation. In order to achieve high computational results, linear image representation is used. The proposed approach results from the aspect of time complexity and skew estimation accuracy which are analyzed and compared with the already existing skew correction approaches. The proposed approach gives better results compared with analogous approach used in related work, but it gives worse results compared with optimized version which exploits a BAG algorithm. Provided results show significant improvement of the standard Hough transform implementation.

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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 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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Li, Jing, and Tao Yang. "Efficient and Robust Feature Matching via Local Descriptor Generalized Hough Transform." Applied Mechanics and Materials 373-375 (August 2013): 536–40. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.536.

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Robust and efficient indistinctive feature matching and outliers removal is an essential problem in many computer vision applications. In this paper we present a simple and fast algorithm named as LDGTH (Local Descriptor Generalized Hough Transform) to handle this problem. The main characteristics of the proposed method include: (1) A novel local descriptor generalized hough transform framework is presented in which the local geometric characteristics of invariant feature descriptors are fused together as a global constraint for feature correspondence verification. (2) Different from standard generalized hough transform, our approach greatly reduces the computational and storage requirements of parameter space through taking advantage of the invariant feature correspondences. (3) The proposed algorithm can be seamlessly embedded into the existing image matching framework, and significantly improve the image matching performance both in speed and robustness in challenge conditions. In the experiment we use both synthetic image data and real world data with high outliers ratio and severe changes in view point, scale, illumination, image blur, compression and noises to evaluate the proposed method, and the results demonstrate that our approach achieves achieves faster and better matching performance compared to the traditional algorithms.

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Zhang, Liting, Hao Huan, Tao Ran, Shangyu Zhang, Yushu Zhang, and Hao Ding. "A Spaceborne Passive Localization Algorithm Based on MSD-HOUGH for Multiple Signal Sources." Remote Sensing 16, no. 22 (2024): 4303. http://dx.doi.org/10.3390/rs16224303.

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Recently, the passive synthetic aperture (PSA) technique has been used in passive localization to improve the position accuracy of single source by estimating the Doppler parameter of the received signal. However, in the presence of multiple sources, time-frequency aliasing will lead to serious cross-term interference during Doppler signal extraction, resulting in low localization performance. To solve this problem, a spaceborne passive synthetic aperture localization algorithm based on the multiple-stay detector HOUGH transform (MSD-HOUGH) is proposed in this paper. Firstly, an improved convolutional neural network based on the adaptive histogram equalization method (AHE-CNN) is proposed to achieve source number estimation. Then, the PSA Doppler equations are established in the HOUGH domain, which can suppress the cross-term interference of the multiple emitters. Meanwhile, a multiple-stay detector (MSD) is designed to reduce the pseudo-peaks in HOUGH domain. The estimated source number determines when the MSD will be terminated. Finally, a PSA cost function is established based on the estimated Doppler parameter to achieve signal source localization. Experimental results show that compared with other localization methods, the proposed algorithm has a significant improvement for multiple signal source localization.

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Song, Wei, Dechao Li, Su Sun, et al. "2D&3DHNet for 3D Object Classification in LiDAR Point Cloud." Remote Sensing 14, no. 13 (2022): 3146. http://dx.doi.org/10.3390/rs14133146.

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Accurate semantic analysis of LiDAR point clouds enables the interaction between intelligent vehicles and the real environment. This paper proposes a hybrid 2D and 3D Hough Net by combining 3D global Hough features and 2D local Hough features with a classification deep learning network. Firstly, the 3D object point clouds are mapped into the 3D Hough space to extract the global Hough features. The generated global Hough features are input into the 3D convolutional neural network for training global features. Furthermore, a multi-scale critical point sampling method is designed to extract critical points in the 2D views projected from the point clouds to reduce the computation of redundant points. To extract local features, a grid-based dynamic nearest neighbors algorithm is designed by searching the neighbors of the critical points. Finally, the two networks are connected to the full connection layer, which is input into fully connected layers for object classification.

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Ma, Peng Ge, and Rong Xing Guo. "Research on the Algorithm of Automatic Detection of Bus Dashboard Pointer Based on CM-Hough Transforms." Key Engineering Materials 621 (August 2014): 663–68. http://dx.doi.org/10.4028/www.scientific.net/kem.621.663.

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The pointer position detection is an important part of implementing the bus dashboard functional test using machine vision. This paper introduces the composition and working principle of the dashboard automatic detection system on machine vision. Then, combining with image processing and Hough transform, we get the image analysis algorithm of the dashboard pointer detection. By analyzing a large amount of computation resulted from the fact that traditional Hough transform uses divergent mapping methods, paper puts forward the methods of improving the convergence of the mapping and conducts parameter space mapping, which effectively reduces the amount of computation. After that, combining with the actual picture of a bus dashboard, automatic detection experiment was carried out for the proposed algorithm. Experiments show that algorithm for dashboard pointer position machine visual based on CM-Hough transform can obtain the angle of the pointer, and effectively shorten the time for dashboard functionality test, and improve the efficiency of the production line for passenger bus dashboard.

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Wang, Hui, Meng Wang, and Peng Zhao. "Sports Video Augmented Reality Real-Time Image Analysis of Mobile Devices." Mathematical Problems in Engineering 2021 (June 8, 2021): 1–13. http://dx.doi.org/10.1155/2021/9963524.

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Sports video is loved by the audience because of its unique charm, so it has high research value and application value to analyze and study the video data of competition. Based on the background of football match, this paper studies the football detection and tracking algorithm in football game video and analyzes the real-time image of real-time mobile devices in sports video augmented reality. Firstly, the image is preprocessed by image graying, image denoising, image binarization, and so on. Secondly, Hough transform is used to locate and detect football, and according to the characteristics of football, Hough transform is improved. Based on the good performance of SIFT algorithm in feature matching, a football tracking algorithm based on SIFT feature matching is proposed, which matches the detected football with the sample football. The simulation results show that the improved Hough transform can effectively detect football and has good antijamming performance. And the designed football tracking algorithm based on SIFT feature matching can accurately track the football trajectory; therefore, the football detection and tracking algorithm designed in this paper is suitable for real-time football monitoring and tracking.

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Zhou, Zhi Heng, and Wenting Zhang. "Lane Detection and Predicting Algorithm Based on Randomized Hough Transform." Applied Mechanics and Materials 284-287 (January 2013): 3199–202. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3199.

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Lane detection is the key technology of the intelligent vehicle based on machine vision. In order to improve the detection of real-time, a lane detection and prediction algorithm based on Randomized Hough Transform is developed in this paper. The algorithm includes lane detection algorithm and prediction algorithm. First of all at identification stages, scan the pretreated image in order to search lanes candidate points, and combine with the lanes angle range of constraints, fit the candidate boundary points by Randomized Hough Transform for the improvement of real-time and robustness. The driveway line prediction algorithm is also proposed. With the dynamic searching window, weights of prediction are adaptive and they can make the line prediction more accurate. Test results show that algorithm has good real-time and robust performance.

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Gabrielli, Alessandro, Fabrizio Alfonsi, and Francesca Del Corso. "Hough Transform Proposal and Simulations for Particle Track Recognition for LHC Phase-II Upgrade." Sensors 22, no. 5 (2022): 1768. http://dx.doi.org/10.3390/s22051768.

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In the near future, LHC experiments will continue future upgrades by overcoming the technological obsolescence of the detectors and the readout capabilities. Therefore, after the conclusion of a data collection period, CERN will have to face a long shutdown to improve overall performance, by updating the experiments, and implementing more advanced technologies and infrastructures. In particular, the largest LHC experiment, i.e., ATLAS, will upgrade parts of the detector, the trigger, and the data acquisition system. In addition, the ATLAS experiment will complete the implementation of new strategies, algorithms for data handling, and transmission to the final storage apparatus. This paper presents an overview of an upgrade planned for the second half of this decade for the ATLAS experiment. In particular, we show a study of a novel pattern recognition algorithm used in the trigger system, which is a device designed to provide the information needed to select physical events from unnecessary background data. The idea is to use a well known mathematical transform, the Hough transform, as the algorithm for the detection of particle trajectories. The effectiveness of the algorithm has already been validated in the past, regardless of particle physics applications, to recognize generic shapes within images. On the contrary, here, we first propose a software emulation tool, and a subsequent hardware implementation of the Hough transform, for particle physics applications. Until now, the Hough transform has never been implemented on electronics in particle physics experiments, and since a hardware implementation would provide benefits in terms of overall Latency, we complete the studies by comparing the simulated data with a physical system implemented on a Xilinx hardware accelerator (FELIX-II card). In more detail, we have implemented a low-abstraction RTL design of the Hough transform on Xilinx UltraScale+ FPGAs as target devices for filtering applications.

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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 detection method can extract the lane marking parameters accurately even for which are badly broken, and covered by shadow or rainwater completely, and the algorithm has good robustness.

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Altameemi, Hayder G. A., Ahmed Abdul Azeez Ismael, and Raddam Sami Mehsen. "Hough Transform for Distinctive Edge Detection to Images in Fingerprint Recognition Matching Transformation." Webology 18, no. 2 (2021): 999–1010. http://dx.doi.org/10.14704/web/v18i2/web18369.

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Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.

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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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Fourie, Jaco. "Robust Circle Detection Using Harmony Search." Journal of Optimization 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9710719.

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Automatic circle detection is an important element of many image processing algorithms. Traditionally the Hough transform has been used to find circular objects in images but more modern approaches that make use of heuristic optimisation techniques have been developed. These are often used in large complex images where the presence of noise or limited computational resources make the Hough transform impractical. Previous research on the use of the Harmony Search (HS) in circle detection showed that HS is an attractive alternative to many of the modern circle detectors based on heuristic optimisers like genetic algorithms and simulated annealing. We propose improvements to this work that enables our algorithm to robustly find multiple circles in larger data sets and still work on realistic images that are heavily corrupted by noisy edges.

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Chen, Yu Feng, Zhi Zhong Yang, Gao Jie Yan, and Fei Fei Li. "Image Classification Algorithm Based on Structural Information." Applied Mechanics and Materials 263-266 (December 2012): 2627–30. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2627.

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Bag of words algorithm is to integrate visual words, described the local features of the object, together to form a bag model. It does not consider the structural information of the object, considering only the local features. This paper presents an image classification algorithm based on structural information. Algorithm adds the structural information of the object according to the ideas of generalized Hough transform. We use the structural information on the local features of the test image with generalized Hough transform and get the possible position of the center of the object. Then, we have to analyze the template size on the basis of the dispersion degree of the voting points, and finally optimize the voting results. Experimental results demonstrate that our proposed method is superior to the method which does not consider the structural information and uses the low-level features to classification.

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Nikolaev, Dmitry, Egor Ershov, Alexey Kroshnin, Elena Limonova, Arseniy Mukovozov, and Igor Faradzhev. "On a Fast Hough/Radon Transform as a Compact Summation Scheme over Digital Straight Line Segments." Mathematics 11, no. 15 (2023): 3336. http://dx.doi.org/10.3390/math11153336.

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The Hough transform, interpreted as the discretization of the Radon transform, is a widely used tool in image processing and machine vision. The primary way to speed it up is to employ the Brady–Yong algorithm. However, the accuracy of the straight line discretization utilized in this algorithm is limited. In this study, we propose a novel algorithm called ASD2 that offers fast computation of the Hough transform for images of arbitrary sizes. Our approach adopts a computation scheme similar to the Brady–Yong algorithm but incorporates the best possible line discretization for improved accuracy. By employing the Method of Four Russians, we demonstrate that for an image of size n×n where n=8q and q∈N, the computational complexity of the ASD2 algorithm is O(n8/3) when summing over O(n2) digital straight line segments.

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Li, Gui Qin, Lin Xin Zhang, Zhi Pin Yu, Yan Wang, Xiao Yuan, and Hong Bo Li. "Research of Edge Detection Algorithm Based on Canny Algorithm and Hough Transform." Advanced Materials Research 1039 (October 2014): 262–65. http://dx.doi.org/10.4028/www.scientific.net/amr.1039.262.

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In order to solve the difficulty of low contrast feature extracting between labeled features and background, a new method combined the Hough transform and Canny algorithm is put forward for tensile test using the video extensometer, and a new algorithm edge detection is also obtained based on Canny an feature region tracking as well. The algorithm has proved to be a high positioning accuracy and computational efficiency in the test.

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Xu, Pengfei, Yinjie Jia, and Xinnian Guo. "Single channel convolutive blind source separation for LFM radar signals." Journal of Electrical Engineering 73, no. 6 (2022): 378–86. http://dx.doi.org/10.2478/jee-2022-0052.

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Abstract We propose a single channel blind source separation algorithm for convolutively mixed linear frequency modulation (LFM) signals based on smoothed Wigner-Ville distribution (SWVD) time-frequency analysis, Canny edge detection, and Hough transform detection. First, the SWVD time-frequency analysis diagram is obtained as an image based on the LFM time-frequency characteristics. Second, Canny edge detection is performed on the image. Then, Hough transform is used to detect the characteristic parameters of the linear signal. Finally, the source signal is recovered. The simulation results show that the algorithm is effective for single channel detection and extraction of convolutively mixed LFM signals.

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Kato, Kunihito, Toshio Endo, Kazuhito Murakami, Takashi Toriu, and Hiroyasu Koshimizu. "Randomized Voting Hough Transform Algorithm and Its Application." IEEJ Transactions on Electronics, Information and Systems 120, no. 12 (2000): 1978–87. http://dx.doi.org/10.1541/ieejeiss1987.120.12_1978.

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YE Feng, 叶峰, 陈灿杰 CHEN Can-jie, 赖乙宗 LAI Yi-zong, and 陈剑东 CHEN Jian-dong. "Fast circle detection algorithm using sequenced Hough transform." Optics and Precision Engineering 22, no. 4 (2014): 1104–11. http://dx.doi.org/10.3788/ope.20142204.1104.

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Kao, Tzong-Wann, Shi-Jinn Horng, Yue-Li Wang, and Kuo-Liang Chung. "A constant time algorithm for computing hough transform." Pattern Recognition 26, no. 2 (1993): 277–86. http://dx.doi.org/10.1016/0031-3203(93)90036-v.

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Guo, Hongwei, and Beiting Lü. "Phase-shifting algorithm by use of Hough transform." Optics Express 20, no. 23 (2012): 26037. http://dx.doi.org/10.1364/oe.20.026037.

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Ozbek, Fevzi O., and Meghanad D. Wagh. "A parallel Hough transform algorithm for nonuniform images." Pattern Recognition Letters 15, no. 3 (1994): 253–59. http://dx.doi.org/10.1016/0167-8655(94)90057-4.

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Li, P. Y., Y. Li, Jian Ming Zheng, D. Zhang, and C. Y. Hao. "Tool Cutting Edge Line Detection Based on Improved Hough Transform." Key Engineering Materials 455 (December 2010): 59–65. http://dx.doi.org/10.4028/www.scientific.net/kem.455.59.

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In view of the inherent limitations of classical Hough transform in the detection of line, an improved algorithm of randomized Hough transform is offered in this paper. The feature point sets are segmented based on the connected component labels in this algorithm. The elements of various point sets are stored in sequence so as to reducing the invalid samples. By using a valid line detection area and a dynamic storage management, the temporary storage units are gradually reducing with the iterative process. The speed of the line detection and the accuracy of identification can be improved, the storage space can be reduced, the computational complexity can be cut down and some unnecessary calculations can be avoided by using this algorithm. However, it still has the characteristics of the classical Hough transform that it is not sensitive to noise. The line edge where the cutting tool is on can be pinpointed. To achieve the vision-based mode of tool wear state, it can accurately detect rake and flank face of the cutting tool, and can effectively remove the influence to the tool wear detection from the BUILT-UP EDGE in cutting process. By the validation of testing examples, the algorithm has high speed, low memory occupation and high accuracy.

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