Journal articles: 'Hough circle detection transform'

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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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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 (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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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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Bała, Justyna, Maciej Dwornik, and Anna Franczyk. "Automatic Subsidence Troughs Detection in SAR Interferograms Using Circlet Transform." Sensors 21, no. 5 (2021): 1706. http://dx.doi.org/10.3390/s21051706.

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This article presents the results of automatic detection of subsidence troughs in synthetic aperture radar (SAR) interferograms. The detection of subsidence troughs is based on the circlet transform, which is able to detect features with circular shapes. Compared to other methods of detecting circles, the circular transform takes into account the finite data frequency. Moreover, the search shape is not limited to a circle but identified on the basis of a certain width. This is especially important in the case of detection of subsidence troughs whose shapes may not be similar to circles or ellipses but to their fragments. The transformation works directly on the image gradient; it does not require further binary segmentation or edge detection as in the case of other methods, e.g., the Hough transform. The entire processing process can be automated to save time and increase reliability compared to traditional methods. The proposed automatic detection method was tested on a differential interferogram that was generated based on Sentinel-1A SAR images of the Upper Silesian Coal Basin area. The test carried out showed that the proposed method is 20% more effective in detecting troughs that than the method using Hough transform.

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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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Fu, Hu Dai, Hua Wang, and Jin Gang Gao. "Circles Detection in Images by Using of Coarse-to-Fine Search Technique." Applied Mechanics and Materials 220-223 (November 2012): 1385–88. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1385.

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In the automatic production, the application of machine vision technology on image pattern recognition is an important task. Realistic image is composed of these basic units such as straight lines, circles and ellipses. But the actual images usually contain noise and other interferences. So the images will have some discontinuous modes. Hough transform is commonly used to detect straight lines, circles or other parametric patterns in noisy images. In practical application, Hough transform requires a large amount of storage space and computation. In the paper, it proposed an efficient coarse-to-fine search technique to reduce the storage and computing time of circle detection in image. Variable size image and accumulated matrix are used to reduce the required amount of computation and storage for Hough transform. It shows the parameter convergence speed and precision by using different iterative algorithms. The experimental results show that the coarse-to-fine search technique is very suitable for circle detection having time constraints.

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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. Hough line transform is a technique most commonly used to detect curved objects such as lines, circles, ellipses and parabolas. The detection accuracy of the pupil is influenced by the accuracy of the extraction of the edges of the eye. If the outer circle identification is not detected, Hough Transform will be identified. The segmentation step carried out can identify the pupil circle region with a detection success of 80-85%.

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Yao, Zhenjie, and Weidong Yi. "Curvature aided Hough transform for circle detection." Expert Systems with Applications 51 (June 2016): 26–33. http://dx.doi.org/10.1016/j.eswa.2015.12.019.

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Luo, Daisheng, Xiaohai He, Qizhi Teng, and Qingchuan Tao. "Triplet circular Hough transform for circle detection." Journal of Electronics (China) 19, no. 4 (2002): 356–62. http://dx.doi.org/10.1007/s11767-002-0065-4.

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Cao, Jianan, Yue Gao, and Chuanyang Wang. "A Novel Four-Step Algorithm for Detecting a Single Circle in Complex Images." Sensors 23, no. 22 (2023): 9030. http://dx.doi.org/10.3390/s23229030.

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Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, and the least squares method, lead to low detection accuracy, low efficiency, and poor stability in circle detection. To improve the accuracy, efficiency, and stability of circle detection, this paper proposes a single-circle detection algorithm by combining Canny edge detection, a clustering algorithm, and the improved least squares method. To verify the superiority of the algorithm, the performance of the algorithm is compared using the self-captured image samples and the GH dataset. The proposed algorithm detects the circle with an average error of two pixels and has a higher detection accuracy, efficiency, and stability than random sample consensus and random Hough transform.

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Djekoune, A. Oualid, Khadidja Messaoudi, and Kahina Amara. "Incremental circle hough transform: An improved method for circle detection." Optik 133 (March 2017): 17–31. http://dx.doi.org/10.1016/j.ijleo.2016.12.064.

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CHIU, SHIH-HSUAN, JIUN-JIAN LIAW, and KUO-HUNG LIN. "A FAST RANDOMIZED HOUGH TRANSFORM FOR CIRCLE/CIRCULAR ARC RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 03 (2010): 457–74. http://dx.doi.org/10.1142/s0218001410007956.

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The main drawbacks of the Hough transform (HT) are the heavy requirement of computation and storage. To improve the drawbacks of the HT, the randomized Hough transform (RHT) was proposed. But the RHT is not suitable for detecting the pattern with the complex image because the probability is too low. In this paper, we propose a fast randomized Hough transform for circle/circular arc detection. We pick one point at random to be the seed point. Then, we propose a checking rule to confirm if the seed point is on the true circle. Compared with the previous techniques, the proposed method requires less computational time and is more suitable for complex images. In the experiments, synthetic and real images are used to show the effect of the proposed method.

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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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Xie, Yu Chan. "A New Kind of Beer Bottle Mouth Defect Recognition Method Based on Vision." Applied Mechanics and Materials 490-491 (January 2014): 1465–69. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1465.

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According to the beer bottle mouth defect recognition problem on modern production line, a new recognition method based on the combination the Hough Transform and the Midpoint circle algorithm was put forward. Firstly, extract edge pixels on beer bottle mouth mage and transform them into Hough space, which was to draw circles at each pixel location with bottle mouth radius. According to the circular symmetry, only 1/8 circle pixels were needed to draw circles, which were worked out by the Midpoint Circle Algorithm. The circles there overlapped each other to vote. Secondly, took the position with the highest votes as the center of bottle mouth and determined the bottle circular area. Divided the area into regions. Finally, count out the number of image pixels in each region and recognition beer bottle defect. In this paper detailed implementation steps with detection results were given. Experiments and its analysis shows: the algorithm can recognition beer bottle mouth defect correctly and quickly.

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Hazim, G. Daway, H. kareem Hana, and Rafid Hashim Ahmed. "Pupil Detection Based on Color Difference and Circular Hough Transform." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3278–84. https://doi.org/10.11591/ijece.v8i5.pp3278-3284.

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Human pupil eye detection is a significant stage in iris segmentation which is representing one of the most important steps in iris recognition. In this paper, we present a new method of highly accurate pupil detection. This method is consisting of many steps to detect the boundary of the pupil. First, the read eye image (R, G, B), then determine the work area which is consist of many steps to detect the boundary of the pupil. The determination of the work area contains many circles which are larger than pupil region. The work area is necessary to determine pupil region and neighborhood regions afterward the difference in color and intensity between pupil region and surrounding area is utilized, where the pupil region has color and intensity less than surrounding area. After the process of detecting pupil region many steps on the resulting image is applied in order to concentrate the pupil region and delete the others regions by using many methods such as dilation, erosion, canny filter, circle hough transforms to detect pupil region as well as apply optimization to choose the best circle that represents the pupil area. The proposed method is applied for images from palacky university, it achieves to 100 % accuracy.

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Huang, Yonglin, Yutang Ye, Zhenlong Chen, and Naosheng Qiao. "New method of fast Hough transform for circle detection." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 24, no. 9 (2010): 837–41. http://dx.doi.org/10.3724/sp.j.1187.2010.00837.

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Jiang, Lian-yuan. "Fast detection of multi-circle with randomized Hough transform." Optoelectronics Letters 5, no. 5 (2009): 397–400. http://dx.doi.org/10.1007/s11801-009-9071-1.

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KANATANI, KENICHI, and NAOYA OHTA. "AUTOMATIC DETECTION OF CIRCULAR OBJECTS BY ELLIPSE GROWING." International Journal of Image and Graphics 04, no. 01 (2004): 35–50. http://dx.doi.org/10.1142/s0219467804001282.

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We present a new method for the automatic detection of circular objects in images: we detect an osculating circle to an elliptic arc using a Hough transform, iteratively deforming it into an ellipse, removing outlier pixels, and searching for a separate edge. The voting space for the Hough transform is restricted to one and two dimensions for efficiency, and special weighting schemes are introduced to enhance the accuracy. We demonstrate the effectiveness of our method using real images. Finally, we apply our method to the calibration of a turntable for 3D object shape reconstruction.

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Hollitt, C., and M. Johnston-Hollitt. "Feature Detection in Radio Astronomy using the Circle Hough Transform." Publications of the Astronomical Society of Australia 29, no. 3 (2012): 309–17. http://dx.doi.org/10.1071/as11051.

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AbstractWhile automatic detection of point sources in astronomical images has experienced a great degree of success, less effort has been directed towards the detection of extended and low-surface-brightness features. At present, existing telescopes still rely on human expertise to reduce the raw data to usable images and then to analyse the images for non-pointlike objects. However, the next generation of radio telescopes will generate unprecedented volumes of data making manual data reduction and object extraction infeasible. Without developing new methods of automatic detection for extended and diffuse objects such as supernova remnants, bent-tailed galaxies, radio relics and halos, a wealth of scientifically important results will not be uncovered. In this paper we explore the response of the Circle Hough Transform to a representative sample of different extended circular or arc-like astronomical objects. We also examine the response of the Circle Hough Transform to input images containing noise alone and inputs including point sources.

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Wu, Xue Feng, and Yu Fan. "Measurement of Positioning Barrel Eccentric Hole Based on Machine Vision Technology." Applied Mechanics and Materials 143-144 (December 2011): 726–30. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.726.

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A measurement based on image process is proposed for detecting the hole on a barrel. The barrel image is separated from background with the image edge detection technology. The different detection operators are compared and the sobel operator is used for edge detection. The actual space position of the hole on barrel is computed by circle detecting method. Gradient hough transform is used for detecting circle position information, including circle radius and two dimension coordinates. The stepping motor drive system is consists of a control card, stepping motor drive units and two two-phase hybrid stepping motors. Automatic positioning is achieved through stepping motor drive system.

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Shang, Ce, and Guo Liang Tao. "An Automatic Assembly Technology for Perforated Discs Based on Contour Optimized Hough Circle Transform." Applied Mechanics and Materials 459 (October 2013): 297–303. http://dx.doi.org/10.4028/www.scientific.net/amm.459.297.

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Aiming at the low efficiency of handwork and high labor intensity, an automatic solution for perforated discs assembly is developed based on computer vision. This technology consists of the processes of material feeding, parts handling, circle detecting and assembling. The mechatronic structure includes the pneumatic elements and electric actuators that controlled by PLC and stepper motor drivers. This method has solved the problem of the large cost of human force since this product has a big industrial production. Meanwhile, a contour optimized Hough circle transform (CHCT) is proposed. It can overcome the standard Hough circle transform (HCT) s disadvantages, such as redundant calculation and probability of failures. It enhances the reliability in order to satisfy the demand of industrial automatic production. The image processing takes only about 60ms and reaches 100% success rate with a small detection error. This method also has the generality for the similar assembly system based on machine vision.

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Yue, Gong He, Chang Hua Lu, Liu Qing Sheng, and Yu Na Liu. "A Combined Method for Concentric Circles Detection in Image of O-Shape Rubber Ring." Advanced Materials Research 488-489 (March 2012): 1619–23. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1619.

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Traditional Hough transform (HT) methods based on parameters decomposition do not give good result for concentric circle detection, for the reasons of its large amount of calculations, high demand of storage space and low efficiency in real-time application. To compensate the weaknesses, this paper employs a kind of improved Hough transform method which aims at reducing the parameters space. In this paper, the detection method is further improved through adding an edge detection procedure based on global threshold. The experimental results show that this algorithm meets the on-line detection of high accuracy requirement, with superior real-time performance and stronger anti-interference ability.

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Qiao, Naosheng, Kang Xiao, and Chao Wei. "PCB Photoelectric Image Circle Detection Based on Improved Hough Transform." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 11, no. 4 (2018): 457–59. http://dx.doi.org/10.2174/2352096511666180213115737.

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WANG, Lei, and Lin-qiang CHEN. "Concentric circle detection based on chord midpoint Hough transform." Journal of Computer Applications 29, no. 7 (2009): 1937–39. http://dx.doi.org/10.3724/sp.j.1087.2009.01937.

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Aswar, Dipti Nilesh. "Measuring the Dimensions of Mechanical Component using Image Processing Techniques." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 9 (2017): 65. http://dx.doi.org/10.23956/ijarcsse.v7i9.363.

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This paper introduces automated process for measuring the dimensions of mechanical component. The proposed method includes image pre-processing techniques, edge detection technique, hough transform technique for circle detection and stereo vision concept is used for hole depth measurement of mechanical component. In practical, there are many factors which affects the measurement result. Noise may play key role. In order to eliminate noise effect on measurement Gaussian filtering algorithm is used. Then canny edge detection technique is used for edge detection, which helps to improve the accuracy of the result. For hole diameter measurement first we have to find out the circular shape and for circle identification we are using Hough transform technique. We are going to calculate the depth of hole by using the elevation by parallax technique. This proposed method is used for specific type of component. But in future this method can be applied for many type of real time application.

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Wu, Xue Feng, and Yu Fan. "A Method of Detecting Circle by Improved Hough Transform." Advanced Materials Research 542-543 (June 2012): 639–42. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.639.

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A new algorithms for parameters of an image irregular boundary circle parameters is presented, which is based on “Curve-Approximate Method” .For a set of an image circle boundary points by image pre-processing, firstly this paper introduces a substitute variant curve approximate reputably while picking out the irregular boundary points in all points, until to fit the terminate condition. Finally, it succeeds to get the optimal estimation of parameters of a circle. Example show that the algorithms runs more quickly and automatically than traditional generalized hough transform, and a good result is obtained if the irregular boundary points is small proportion in all points of a circle.

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Cui, Ji Wen, and Jiu Bin Tan. "A Circle Contour Measurement Technique Based on Randomized Hough Transform Using Gradient Information." Key Engineering Materials 295-296 (October 2005): 277–82. http://dx.doi.org/10.4028/www.scientific.net/kem.295-296.277.

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Hough Transform (HT) is an image edge detection technique which is widely used in pattern recognition and computer vision. In this paper the fundamental principle of HT is analyzed and the defect of HT and Randomized Hough Transform (RHT) is indicated. An algorithm based on RHT and the information of grayscale and gradient in image is proposed. It uses the property of the pattern and is mainly used for detection of circle and arc contour measurement. This algorithm can decrease memory usage in computer by a multi to one mapping, accelerate the calculation speed by parallel algorithm, improve the edge detection accuracy by subpixel division, obtain the parameters of object by applying least square fitting algorithm. Based on the principle, a measurement system with high accuracy and efficiency in image capturing and processing is developed. Experiments are carried out in the system. The result of experiment has certified the feasibility and validity of the algorithm.

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Li, Shaodong, Zhijiang Du, Hongjian Yu, and Jiafu Yi. "A Robust Multi-Circle Detector Based on Horizontal and Vertical Search Analysis Fitting with Tangent Direction." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 04 (2019): 1954013. http://dx.doi.org/10.1142/s0218001419540132.

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In this paper, we propose an efficient Multi-Circle detector which follows the fixed search order. The method makes use of horizontal and vertical search to realize circle detection, which is named as HVCD. First, this method computes edge areas in a given image. The edge areas could be divided into some regions by means of region growing. Each of regions could be efficiently searched to achieve not only one-pixel wide edges but edge segments as well. Next, the candidate circles can be extracted from every edge segment. Finally, the circle candidates could be validated with the help of Helmholtz principle. Experimental results demonstrate that HVCD could effectively detect circles on synthetic and natural images on the one hand; on the other hand, HVCD here could solve the weakness in the process of circle Hough transform implementation and EDcircles implementation.

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Lam, W. C. Y., and S. Y. Yuen. "Efficient technique for circle detection using hypothesis filtering and Hough transform." IEE Proceedings - Vision, Image, and Signal Processing 143, no. 5 (1996): 292. http://dx.doi.org/10.1049/ip-vis:19960794.

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Li, Q., and Y. Xie. "Randomised hough transform with error propagation for line and circle detection." Pattern Analysis & Applications 6, no. 1 (2003): 55–64. http://dx.doi.org/10.1007/s10044-002-0178-2.

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Huang, An Chi, Chun Yuan, Sheng Hui Meng, and Tian Jiun Huang. "Design of Fatigue Driving Behavior Detection Based on Circle Hough Transform." Big Data 11, no. 1 (2023): 1–17. http://dx.doi.org/10.1089/big.2021.0166.

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Duraiswamy, Umamaheswari, and Geetha Shanmugam. "Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes." Indian Journal of Science and Technology 13, no. 45 (2020): 4541–54. https://doi.org/10.17485/IJST/v13i45.328.

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Abstract Background/Objectives: To present an accurate quantitative approach based two-phase algorithm to count both the leukocytes and erythrocytes for identifying the severity of leukaemia in the human body. Methods/Statistical analysis: The algorithm is having two-phases with the first phase meant for recognizing and counting the leukocytes using the thresholding based segmentation technique that focuses on the intensity values of pixels of the greyscale blood smear images; whereas the second phase recognizes the erythrocytes by their circular shape using Circular Hough Transform (CHT) method. The system experiments with 26 stained blood smear images from the ALL-IDB1 benchmark dataset. Findings: The first phase of the algorithm achieves 99.41 per cent overall accuracy in leukocytes detection and in the second phase 99.76 per cent overall accuracy is attained in erythrocytes detection. Novelty/Applications: This proposal applies Circular Hough Transform in detecting the erythrocytes by adjusting the radius of the circle according to the magnification rate of the sample image. Keywords: Circular Hough transform; cell count; image processing; Leukaemia; Leukocytes; Erythrocytes

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Shi, Chun Lei, Guang Yuan Jiang, Haiyan Qi, and Fei Han. "An Efficient and Fast Iris Location Algorithm." Applied Mechanics and Materials 263-266 (December 2012): 2549–52. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2549.

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The edge information of pupil was extracted by the least-square method, and the iris outer circle was extracted by the improved Canny Operator plus Hough Transform. The segmental-secondary linear localization method adopting edge detection and Radon Transform was proposed to remove the noise from eyelid on the eyelid localization, the eyelash noise and eyelid shadows were removed by threshold method.

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Daway, Hazim G., Hana H. Kareem, and Ahmed Rafid Hashim. "Pupil Detection Based on Color Difference and Circular Hough Transfor." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3278. http://dx.doi.org/10.11591/ijece.v8i5.pp3278-3284.

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Human pupil eye detection is a significant stage in iris segmentation which is representing one of the most important steps in iris recognition. In this paper, we present a new method of highly accurate pupil detection. This method is consisting of many steps to detect the boundary of the pupil. First, the read eye image (R, G, B), then determine the work area which is consist of many steps to detect the boundary of the pupil. The determination of the work area contains many circles which are larger than pupil region. The work area is necessary to determine pupil region and neighborhood regions afterward the difference in color and intensity between pupil region and surrounding area is utilized, where the pupil region has color and intensity less than surrounding area. After the process of detecting pupil region many steps on the resulting image is applied in order to concentrate the pupil region and delete the others regions by using many methods such as dilation, erosion, canny filter, circle hough transforms to detect pupil region as well as apply optimization to choose the best circle that represents the pupil area. The proposed method is applied for images from palacky university, it achieves to 100 % accurac

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Torii, Akihiko, and Atsushi Imiya. "The randomized-Hough-transform-based method for great-circle detection on sphere." Pattern Recognition Letters 28, no. 10 (2007): 1186–92. http://dx.doi.org/10.1016/j.patrec.2007.02.002.

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Li, Yao, and George Eichmann. "Hough-transform-based circle detection using an array of multimode optical fibers." Optics Communications 61, no. 4 (1987): 248–51. http://dx.doi.org/10.1016/0030-4018(87)90100-3.

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Zięba, Przemysław, Adam Gąska, Wiktor Harmatys, and Marcin Krawczyk. "Adaptive Hough Transform Circle Detection Application in Developed 2D Coordinate Measuring System." Advances in Science and Technology Research Journal 17, no. 6 (2023): 185–91. http://dx.doi.org/10.12913/22998624/174111.

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Xu, Xiaoyan, and Li Zhang. "Detection Method of Tourist Flow in Scenic Spots based on Kalman Filter Prediction." Scalable Computing: Practice and Experience 25, no. 3 (2024): 2048–61. http://dx.doi.org/10.12694/scpe.v25i3.2708.

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The tourism industry has developed rapidly, but it is always limited by the environmental carrying capacity and cannot receive too many tourists at the same time. Therefore, it is very necessary to limit the number of tourists visiting at the same time based on traffic detection. To this end, the tourist scenic spot (TSS) traffic statistics system was designed. The system performed graying, binarization, image denoising, and morphological processing on the image. The pre-processed image used the background difference method based on mixed Gaussian background modeling to detect moving objects. The improved Hough transform circle detection method was used to identify the head target, and the Kalman filter (KF) was used to complete the target tracking. KF could predict the target trajectory accurately, and the improved Hough transform circle detection method could recognize the head under occlusion. The maximum missed detection rate of the statistical system was 3.2\%, the minimum is 0, and the overall detection accuracy was the highest. The error rate of inbound passenger flow was 4.10\%, and the error rate of outbound passenger flow was 3.0\%. Using this system can control the tourist flow (TF) in the scenic spot and avoid safety accidents due to excessive passenger flow. And it is conducive to the sustainable development of the scenic spot.

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Chu, Guang Li, and Yan Jie Wang. "Shape Features Detection Based on Hough Transform in Images." Applied Mechanics and Materials 644-650 (September 2014): 1104–6. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1104.

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Hough transform as an effective graphics target detection method can detect straight lines, circles, ellipses, parabolas and many other analytical graphics. The discretization of space, as well as the calculation of the process make Hough transform have some limitations, such as poor detection results because of high-intensity noise, a large amount of calculation, large demand of storage resources and so on. This paper analyzes the Hough Transform voting process and points out that the accumulation with 1 in the method is unreasonable. The paper proposed a Hough transform based on template matching via the modification of the definition of the traditional method. In this method, each parameter unit identifies a template in image space. The feature points according with the conditions can be searched by the template actively. The method takes the number of feature points as the value of parameter unit and takes the record of the coordinates of line segment endpoints. So line segments can be detected and storage resources can be saved.

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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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Cao, Binfang, Jianqi Li, Yincong Liang, Xuan Sun, and Weihao Li. "Real-Time Detection of Nickel Plated Punched Steel Strip Parameters Based on Improved Circle Fitting Algorithm." Electronics 12, no. 8 (2023): 1865. http://dx.doi.org/10.3390/electronics12081865.

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Nickel-plated punched steel strip is a product obtained by punching holes on the surface of cold-rolled white sheet steel strip and then electrochemical nickel plating. It is necessary to make accurate and fast detection of punching circle parameters, since it is of crucial importance to ensuring the quality of nickel-plated punched steel strips. Accordingly, in this article, an improved circle fitting algorithm of nickel-plated punched steel strip is proposed. Firstly, the least squares fitting is performed to obtain the circle center and radius dataset by iterative algorithm with different values for the initial point positions and intervals. Then, the mean shift algorithm is used to optimize the results after iteration, and the segmented fitted circle centers are all concentrated around the true circle center to obtain the best radius and center coordinates. Finally, comparison experiments with different numbers of circular holes and verification experiments with nickel-plated punched steel strips are carried out. As the results show, the algorithm proposed in this article is more robust than the least squares algorithm in detecting multiple circles and has better real-time performance than the Hough transform. Therefore, it can meet the industrial production needs with high accuracy and real-time requirements, such as nickel-plated punched steel strips.

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Muharom, S., A. Rizkiawan, I. Masfufiah, R. A. Firmansyah, and Y. A. Prabowo. "Detection and Erasing Scribble Blackboard System Based on Hough-Transform Method Using Camera." Journal of Physics: Conference Series 2117, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1742-6596/2117/1/012010.

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Abstract Teachers of a secondary school in Semarang city are often exposed to blackboard chalk dust. This will give a significant impact if it occurs at a fairly frequent intensity and a long period of time. An automatic scratch detector and designing a device that can erase the blackboard is a preventive step that can reduce the long-term impact of chalk dust. The scratch detector uses a circle shape parameter as a mark of dirty position that needs to be erased. Light can affect the system performance. The system works properly at light intensities ranging from ± 160 to ± 200 lux. Testing the threshold value proves that the system can detect circles in the range of 40 - 55. The pixel size which is detected by the camera was 640x480 will allow the system to divide the blackboard into 9 mapping areas. The mapping area is differentiated into 9 sections so that the x and y coordinate positions of the blackboard dirty spot can be determined. A mechanical execution will erase the top and bottom areas according to the position of the detected mapping area. The success of scratch detector reaches 81.8%..

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Ji, Jin Jun, and C. Ye. "The Automatic Detection Technology for the Surface Defects of Automobile Engine Cylinder." Key Engineering Materials 693 (May 2016): 1458–65. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1458.

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In view of the casting defects of the automobile engine cylinder, the automatic detection technology for the surface defects of cylinder was investigated in this study. Moreover, a system was designed to automatically detect the surface defects of engine cylinder bore basing on machine vision technology. The pixels of the bottom circle and top circle of cylinder bore were effectively extracted using a Hough transform-based fast detection circle algorithm; Aiming to solve the inconvenience in observation and measurement as well as the obvious geometric distortion presented in annular image, an algorithm, in which annulus was extended into rectangular, was put forward. Experiment results proved that this algorithm was fast and efficient and showed lower mean error in calculating annular defect area.

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Priambodo, Bagus, and Yuwan Jumaryadi. "Sistem Pakar Deteksi Covid-19 Dengan Lie Detection Menggunakan Metode Circle Hough Transform." Journal of Computer System and Informatics (JoSYC) 4, no. 1 (2022): 238–44. http://dx.doi.org/10.47065/josyc.v4i1.2528.

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The number of health workers infected with Covid-19 in Indonesia continues to grow amid the coronavirus pandemic. Not only doctors, nurses and other medical support staff have been exposed to Covid-19. To date, IDI has recorded more than 180 health workers who have died from Corona. Health workers are dying because of a lack of Personal Protective Equipment (PPE) and fatigue. Prevention of direct contact between asymptomatic patients and health workers is a way to prevent health workers from contracting COVID-19. An expert system for diagnosing COVID-19 with lie detection is proposed to be used for patients who wish to seek treatment at a health center before they meet face-to-face with health workers. Several previous studies have proven that the certainty method can be used to diagnose COVID-19 with an accuracy of up to 90%, provided that the patient answers questions honestly. In this study, control questions and pupil detection were added using the circle hard transform to find out whether patients who wanted treatment did not lie when answering questions about symptoms of exposure to Covid, travel history and family history of exposure to Covid. The combination of an expert system and lie detection is expected to be the first protective alternative for health workers from asymptomatic patients. Based on the results of the application testing carried out, it can be seen that the movement of the patient's pupils when answering questions.

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Chiu, Shih‐Hsuan, and Jiun‐Jian Liaw. "A proposed circle/circular arc detection method using the modified randomized hough transform." Journal of the Chinese Institute of Engineers 29, no. 3 (2006): 533–38. http://dx.doi.org/10.1080/02533839.2006.9671148.

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Li, Xiao Mei, Xi Lin Zhu, Yong Yu, Xiang Zou, and Chen Jun Huang. "The Image Positioning and Segmentation Techniques about Gauge Visual Detection System between High Signals and Contact Net Based on Target Feature." Key Engineering Materials 522 (August 2012): 351–54. http://dx.doi.org/10.4028/www.scientific.net/kem.522.351.

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In the design process of gauge visual detection system between high signals and contact net, it is need to do target positioning and segmentation between high signals and contact line. The paper first analyzes the structural characteristics of high signals and contact line, and then uses the Sobel operator for edge detection, and uses first close and then open of image morphological operations for edge treatment, finally uses Hough transform for line and circle detection to extract the object's edge. This positioning and segmentation of target object would be achieved.

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Zhou, Zhi Feng. "An Approach for Detecting Regular Hexagon." Advanced Materials Research 187 (February 2011): 780–85. http://dx.doi.org/10.4028/www.scientific.net/amr.187.780.

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Regular hexagon is a kind of common geometric figure. This paper proposes a vision detection method based on geometric characteristics to detect regular hexagon. First, line segments are detected in the image by hough transform. The line equations are constructed according to the detected line segments in the image space. Second, the line equations are utilized to compute the intersection points of adjacent two lines. The lengths of every side of regular hexagon are gotten by computing the distances between adjacent two intersection points. The angles between adjacent lines are calculated. Then the circle detection method based hough transform is employed to inspect whether all intersection points locate at the same circumference. Finally, the detection experiment of hexagon bolt is constructed to illustrate the performance of the presented method. The measurement errors of angles between adjacent two sides and lengths of sides of regular hexagon are 0.38% and 0.85% respectively. The result shows that the presented method is effective and can precisely detect regular hexagon in the image.

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Qadir, Tara Othman, and N. S. A. M, Taujuddin. "Iris Segmentation Based on Black Hole Algorithm for Biometric System." International Journal of Engineering and Advanced Technology 10, no. 2 (2020): 281–87. http://dx.doi.org/10.35940/ijeat.b2111.1210220.

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Current iris recognition schemes such as IntegroDifferential method, Hough Transform, Watershed Transform Circle Fitting, and Circular Hough Transformation (CHT) are used to find circular parameters between pupil and iris. Segmentation process of an eye image using the circular parameters toextracts the iris region still can be further improved. In this paper, we introduced an optimization method of circular parameters detection for iris segmentation based on Black Hole Algorithm (BHA). The proposed segmentation algorithm utilizes a computational model of the pixels’ value to detect the iris boundary. The BHA searches for center radius of both pupil and iris. The system tests the CASIA Iris Interval V3 database by on MATLAB. The segmented images show an accuracy of 98.3%. In short, the segmentation-based on BHA is efficient to identify the iris for any future access control applications.

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Tara, Othman Qadir, and Taujuddin N.S.A.M. "Iris Segmentation Based on Black Hole Algorithm for Biometric System." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 2 (2020): 281–87. https://doi.org/10.35940/ijeat.B2111.1210220.

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Current iris recognition schemes such as IntegroDifferential method, Hough Transform, Watershed Transform Circle Fitting, and Circular Hough Transformation (CHT) are used to find circular parameters between pupil and iris. Segmentation process of an eye image using the circular parameters toextracts the iris region still can be further improved. In this paper, we introduced an optimization method of circular parameters detection for iris segmentation based on Black Hole Algorithm (BHA). The proposed segmentation algorithm utilizes a computational model of the pixels’ value to detect the iris boundary. The BHA searches for center radius of both pupil and iris. The system tests the CASIA Iris Interval V3 database by on MATLAB. The segmented images show an accuracy of 98.3%. In short, the segmentation-based on BHA is efficient to identify the iris for any future access control applications.

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Zhang, Ming Jun, Yuan Yuan Wan, and Zhen Zhong Chu. "Research on Underwater Target Tracking Based on Contour Detection." Advanced Materials Research 317-319 (August 2011): 890–96. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.890.

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The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.

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Journal articles on the topic 'Hough circle detection transform'

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