Relevant bibliographies by topics / Hough circle detection transform

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hough circle detection transform'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "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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Dissertations / Theses on the topic "Hough circle detection transform"

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Lu, Dingran. "Multi-circle Detections for an Automatic Medical Diagnosis System." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/735.

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Real-time multi-circle detection has been a challenging problem in the field of biomedical image processing, due to the variable sizes and non-ideal shapes of cells in microscopic images. In this study, two new multi-circle detection algorithms are developed to facilitate an automatic bladder cancer diagnosis system: one is a modified circular Hough Transform algorithm integrated with edge gradient information; and the other one is a stochastic search approach based on real valued artificial immune systems. Computer simulation results show both algorithms outperform traditional methods such as the Hough Transform and the geometric feature based method, in terms of both precision and speed.
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Sutherland, Fritz. "Driver traffic violation detection and driver risk calculation through real-time image processing." Diss., University of Pretoria, 2005. http://hdl.handle.net/2263/66246.

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Road safety is a serious problem in many countries and affects the lives of many people. Improving road safety starts with the drivers, and the best way to make them change their habits is to offer incentives for better, safer driving styles. This project aims to make that possible by offering a means to calculate a quantified indicator of how safe a driver's habits are. This is done by developing an on-board, visual road-sign recognition system that can be coupled with a vehicle tracking system to determine how often a driver violates the rules of the road. The system detects stop signs, red traffic lights and speed limit signs, and outputs this data in a format that can be read by a vehicle tracking system, where it can be combined with speed information and sent to a central database where the driver safety rating can be calculated. Input to the system comes from a simple, standard dashboard mounted camera within the vehicle, which generates a continuous stream of images of the scene directly in front of the vehicle. The images are subjected to a number of cascaded detection sub-systems to determine if any of the target objects (road signs) appear within that video frame. The detection system software had to be optimized for minimum false positive detections, since those will unfairly punish the driver, and it also had to be optimized for speed to run on small hardware that can be installed in the vehicle. The first stage of the cascaded system consists of an image detector that detects circles within the image, since traffic lights and speed signs are circular and a stop sign can be approximated by a circle when the image is blurred or the resolution is lowered. The second stage is a neural network that is trained to recognize the target road sign in order to determine which road sign was found, or to eliminate other circular objects found in the image frame. The output of the neural network is then sent through an iterative filter with a majority voted output to eliminate detection 'jitter' and the occasional incorrect classifier output. Object tracking is applied to the 'good' detection outputs and used as an additional input for the detection phase on the next frame. In this way the continuity and robustness of the image detector are improved, since the object tracker indicates to it where the target object is most likely to appear in the next frame, based on the track it has been following through previous frames. In the final stage the detection system output is written to the chosen pins of the hardware output port, from where the detection output can be indicated to the user and also used as an input to the vehicle tracking system. To find the best detection approach, some methods found in literature were studied and the most likely candidates compared. The scale invariant feature transform (SIFT) and speeded up robust features (SURF) algorithms are too slow compared to the cascaded approach to be used for real-time detection on an in-vehicle hardware platform. In the cascaded approach used, different detection stage algorithms are tested and compared. The Hough circle transform is measured against blob detection on stop signs and speed limit signs. On traffic light state detection two approaches are tested and compared, one based on colour information and the other on direct neural network classification. To run the software in the user's vehicle, an appropriate hardware platform is chosen. A number of promising hardware platforms were studied and their specifications compared before the best candidate was selected and purchased for the project. The developed software was tested on the selected hardware in a vehicle during real public road driving for extended periods and under various conditions.<br>Dissertation (MEng)--University of Pretoria, 2017.<br>Electrical, Electronic and Computer Engineering<br>MEng<br>Unrestricted
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Musilová, Kateřina. "Pupilometrie aplikovaná během měření defokusační křivky." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401002.

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The aim of this work is to design algorithm that will detect pupil from video. The theoretical knowledge necessary for proper pupil detection is also described in this master’s thesis. Detection is done on 24 videos that are converted to single images. The complete result the dependence of the pupil diameter on the used dioptre. The overall success rate of the algorithm is 88,13 %. The overall error is 11,87 %. For 17 out of 24 patients, it is confirmed that the greater the dioptre, the larger the pupil.
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Zutautas, Vaidutis. "Charcoal Kiln Detection from LiDAR-derived Digital Elevation Models Combining Morphometric Classification and Image Processing Techniques." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-24374.

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This paper describes a unique method for the semi-automatic detection of historic charcoal production sites in LiDAR-derived digital elevation models. Intensified iron production in the early 17th century has remarkably influenced ways of how the land in Sweden was managed. Today, the abundance of charcoal kilns embedded in the landscape survives as cultural heritage monuments that testify about the scale forest management for charcoal production has contributed to the uprising iron manufacturing industry. An arbitrary selected study area (54 km2) south west of Gävle city served as an ideal testing ground, which is known to consist of already registered as well as unsurveyed charcoal kiln sites. The proposed approach encompasses combined morphometric classification methods being subjected to analytical image processing, where an image that represents refined terrain morphology was segmented and further followed by Hough Circle transfer function applied in seeking to detect circular shapes that represent charcoal kilns. Sites that have been identified manually and using the proposed method were only verified within an additionally established smaller validation area (6 km2). The resulting outcome accuracy was measured by calculating harmonic mean of precision and recall (F1-Score). Along with indication of previously undiscovered site locations, the proposed method showed relatively high score in recognising already registered sites after post-processing filtering. In spite of required continual fine-tuning, the described method can considerably facilitate mapping and overall management of cultural resources.
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Oldham, Kevin M. "Table tennis event detection and classification." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19626.

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It is well understood that multiple video cameras and computer vision (CV) technology can be used in sport for match officiating, statistics and player performance analysis. A review of the literature reveals a number of existing solutions, both commercial and theoretical, within this domain. However, these solutions are expensive and often complex in their installation. The hypothesis for this research states that by considering only changes in ball motion, automatic event classification is achievable with low-cost monocular video recording devices, without the need for 3-dimensional (3D) positional ball data and representation. The focus of this research is a rigorous empirical study of low cost single consumer-grade video camera solutions applied to table tennis, confirming that monocular CV based detected ball location data contains sufficient information to enable key match-play events to be recognised and measured. In total a library of 276 event-based video sequences, using a range of recording hardware, were produced for this research. The research has four key considerations: i) an investigation into an effective recording environment with minimum configuration and calibration, ii) the selection and optimisation of a CV algorithm to detect the ball from the resulting single source video data, iii) validation of the accuracy of the 2-dimensional (2D) CV data for motion change detection, and iv) the data requirements and processing techniques necessary to automatically detect changes in ball motion and match those to match-play events. Throughout the thesis, table tennis has been chosen as the example sport for observational and experimental analysis since it offers a number of specific CV challenges due to the relatively high ball speed (in excess of 100kph) and small ball size (40mm in diameter). Furthermore, the inherent rules of table tennis show potential for a monocular based event classification vision system. As the initial stage, a proposed optimum location and configuration of the single camera is defined. Next, the selection of a CV algorithm is critical in obtaining usable ball motion data. It is shown in this research that segmentation processes vary in their ball detection capabilities and location out-puts, which ultimately affects the ability of automated event detection and decision making solutions. Therefore, a comparison of CV algorithms is necessary to establish confidence in the accuracy of the derived location of the ball. As part of the research, a CV software environment has been developed to allow robust, repeatable and direct comparisons between different CV algorithms. An event based method of evaluating the success of a CV algorithm is proposed. Comparison of CV algorithms is made against the novel Efficacy Metric Set (EMS), producing a measurable Relative Efficacy Index (REI). Within the context of this low cost, single camera ball trajectory and event investigation, experimental results provided show that the Horn-Schunck Optical Flow algorithm, with a REI of 163.5 is the most successful method when compared to a discrete selection of CV detection and extraction techniques gathered from the literature review. Furthermore, evidence based data from the REI also suggests switching to the Canny edge detector (a REI of 186.4) for segmentation of the ball when in close proximity to the net. In addition to and in support of the data generated from the CV software environment, a novel method is presented for producing simultaneous data from 3D marker based recordings, reduced to 2D and compared directly to the CV output to establish comparative time-resolved data for the ball location. It is proposed here that a continuous scale factor, based on the known dimensions of the ball, is incorporated at every frame. Using this method, comparison results show a mean accuracy of 3.01mm when applied to a selection of nineteen video sequences and events. This tolerance is within 10% of the diameter of the ball and accountable by the limits of image resolution. Further experimental results demonstrate the ability to identify a number of match-play events from a monocular image sequence using a combination of the suggested optimum algorithm and ball motion analysis methods. The results show a promising application of 2D based CV processing to match-play event classification with an overall success rate of 95.9%. The majority of failures occur when the ball, during returns and services, is partially occluded by either the player or racket, due to the inherent problem of using a monocular recording device. Finally, the thesis proposes further research and extensions for developing and implementing monocular based CV processing of motion based event analysis and classification in a wider range of applications.
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Segalini, Lorenzo. "Implementazione in Java dell'algoritmo "Circle Hough Transform"." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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La tesi si concentra sullo studio della tecnica denominata "Circle Hough Transform" ed ha come obiettivo principale quello di dimostrare attraverso l’implementazione in linguaggio Java l’utilità e la validità dell’algoritmo trattato, mostrandone l’efficacia e il funzionamento generale di ogni sua sezione.
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Galbavý, Juraj. "Systém vyhodnocování pro stopový detektor v pevné fázi." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-318170.

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The aim of this thesis is to design an algorithm for an automatic track counting of an image of etched track detector made of CR-39 polymer. Tracks are produced by alpha particles. Chemically etched detector is imaged using a microscope resulting in 64 images of segments on the surface of the detector. Circle shaped tracks in the images have to be detected and counted. This thesis evaluates the utilization of circle hough transform for circle detection. The final software should automate a detector track counting and should also account for defects in the image and contamination of detector surface. The software will produce a measurement report with a total track count in each segment.
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Costa, Luciano da Fontoura. "Effective detection of line segments with Hough transform." Thesis, King's College London (University of London), 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320507.

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Chaudhary, Priyanka. "SPHEROID DETECTION IN 2D IMAGES USING CIRCULAR HOUGH TRANSFORM." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_theses/9.

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Three-dimensional endothelial cell sprouting assay (3D-ECSA) exhibits differentiation of endothelial cells into sprouting structures inside a 3D matrix of collagen I. It is a screening tool to study endothelial cell behavior and identification of angiogenesis inhibitors. The shape and size of an EC spheroid (aggregation of ~ 750 cells) is important with respect to its growth performance in presence of angiogenic stimulators. Apparently, tubules formed on malformed spheroids lack homogeneity in terms of density and length. This requires segregation of well formed spheroids from malformed ones to obtain better performance metrics. We aim to develop and validate an automated imaging software analysis tool, as a part of a High-content High throughput screening (HC-HTS) assay platform, to exploit 3D-ECSA as a differential HTS assay. We present a solution using Circular Hough Transform to detect a nearly perfect spheroid as per its circular shape in a 2D image. This successfully enables us to differentiate and separate good spheroids from the malformed ones using automated test bench.
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Princen, John. "Hough transform methods for curve detection and parameter estimation." Thesis, University of Surrey, 1990. http://epubs.surrey.ac.uk/817/.

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Books on the topic "Hough circle detection transform"

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Meredith, John. Circle detection for non-gridded data utilising the Hough transform. De Montfort University, 2003.

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Goulermas, John. Hough transform techniques for circular object detection. UMIST, 1996.

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Leavers, V. F. Shape Detection in Computer Vision Using the Hough Transform. Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-1940-1.

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Leavers, V. F. Shape detection in computer vision using the Hough transform. Springer-Verlag, 1992.

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Leavers, V. F. Shape Detection in Computer Vision Using the Hough Transform. Springer London, 1992.

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Shape Detection in Computer Vision Using the Hough Transform. Springer, 2011.

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Book chapters on the topic "Hough circle detection transform"

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Djekoune, A. Oualid, Khadidja Messaoudi, and Mahmoud Belhocine. "Incremental Hough Transform: A New Method for Circle Detection." In Studies in Computational Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23392-5_1.

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Leavers, V. F. "A Case Study: Circles and Ellipses." In Shape Detection in Computer Vision Using the Hough Transform. Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-1940-1_7.

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Yadav, Virendra Kumar, Munesh Chandra Trivedi, Shyam Singh Rajput, and Saumya Batham. "Approach to Accurate Circle Detection: Multithreaded Implementation of Modified Circular Hough Transform." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0129-1_3.

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Ismail, Ismariani, Adeline Engkamat, and Abang Feizal Abang Ibrahim. "Toward Developing an Enhanced Hough Transform Technique for Circle and Semicircle Detection." In Regional Conference on Science, Technology and Social Sciences (RCSTSS 2014). Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0534-3_33.

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Lou, Yuehuan. "Detection of Laser Concentric Circles Based on Gradient Hough Transform." In Multimedia and Signal Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_22.

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Huang, Pei-Yu, Chih-Sheng Hsu, Tzung-Pei Hong, Yan-Zhih Wang, Shin-Feng Huang, and Shu-Min Li. "Automatic Parameter Setting in Hough Circle Transform." In Intelligent Information and Database Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41964-6_45.

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Leavers, V. F. "Which Hough?" In Shape Detection in Computer Vision Using the Hough Transform. Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-1940-1_6.

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Razavi, Nima, Juergen Gall, Pushmeet Kohli, and Luc van Gool. "Latent Hough Transform for Object Detection." In Computer Vision – ECCV 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33712-3_23.

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Kang, Sung Kwan, Young Chul Choung, and Jong An Park. "Image Corner Detection Using Hough Transform." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_35.

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Imiya, Atsushi, and Keisuke Iwawaki. "Subpixel Flow Detection by the Hough Transform." In Robot Vision. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44690-7_18.

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Conference papers on the topic "Hough circle detection transform"

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Purnama, Satriawan Rasyid, Adi Wibowo, Husni Fadhilah Dhiya Ul Haq, and Cahyo Adhi Hartanto. "Automated Grading of Computer Answer Sheet: Combining Hough Circle Transform and DBSCAN." In 2024 7th International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2024. http://dx.doi.org/10.1109/icicos62600.2024.10636856.

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Bachmid, Nabil, Ronny Mardiyanto, Muhammad Attamimi, and Kai-Yi Wong. "River Detection Using Fast Fourier Transform, Contour Detection, and Hough Line Transform." In 2024 FORTEI-International Conference on Electrical Engineering (FORTEI-ICEE). IEEE, 2024. https://doi.org/10.1109/fortei-icee64706.2024.10824328.

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Kerbyson, D. J. "Circle detection using Hough transform filters." In Fifth International Conference on Image Processing and its Applications. IEE, 1995. http://dx.doi.org/10.1049/cp:19950683.

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Seo, Sang-Woo, and Myunggyu Kim. "Efficient architecture for circle detection using Hough transform." In 2015 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2015. http://dx.doi.org/10.1109/ictc.2015.7354612.

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Sarika, K., and S. Veni. "Hardware Implementation of Hough Transform for Circle Detection." In the 2014 International Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2660859.2660958.

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Wei Yi. "Circle detection using improved dynamic generalized Hough transform (IDGHT)." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.699714.

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Shi, Dongchen, Bo Zhang, and Ning Wang. "Fast circle detection based on improved randomized Hough transform." In 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT 2014), edited by Xiangang Luo and Harald Giessen. SPIE, 2014. http://dx.doi.org/10.1117/12.2068644.

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Zhou, Bing. "Using Vector Quantization of Hough Transform for Circle Detection." In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.94.

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"A New Modified Hough Transform Method for Circle Detection." In International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004424600050012.

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Kurnia, Rahmadi, Tesi D. Aufia, and Fitrilina. "Comparison of Random Circle Detection and Hough Transform Method in Detecting Obstructed Circle Object." In the 6th International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3284516.3284536.

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Reports on the topic "Hough circle detection transform"

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Walsh, Daniel, and Adrian E. Raftery. Accurate and Efficient Curve Detection in Images: The Importance Sampling Hough Transform. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada458108.

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