Relevant bibliographies by topics / Hough Transform

Academic literature on the topic 'Hough Transform'

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Published: 4 June 2021
Last updated: 1 June 2024

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Journal articles on the topic "Hough Transform"

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

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

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Suppose you are given a number of points in a plane and want to have those lines that each contain a large number of the given points. The Hough transform is a computerized procedure for that task. It was invented by Paul Hough (1962), originally to find the trajectories of subatomic particles in a bubble chamber, and it has even been patented. Nowadays, adaptations of the Hough transform are used, among others, for identification of transformed instances of a predefined figure, instead of just a line, in a digital picture. There are plenty of explanations on the Internet (use search key “Hough transform” and “generalized Hough transform”), some with nice applets to demonstrate the working (add search key “applet” or “demo”). Recently, Hart (2009) has looked back at the invention. We show how the original procedure could have been derived.
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Du, S. "Rotation Hough Transform." SAIEE Africa Research Journal 105, no. 3 (September 2014): 127–30. http://dx.doi.org/10.23919/saiee.2014.8531534.

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

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

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

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

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

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

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

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Dissertations / Theses on the topic "Hough Transform"

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Galambos, Charles. "The Progressive Probabilistic Hough Transform." Thesis, University of Surrey, 2000. http://epubs.surrey.ac.uk/842944/.

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This thesis presents the Progressive Probabilistic Hough Transform (PPHT). Unlike the Probabilistic HT [46] where the Standard HT is performed on a pre-selected fraction of input points, the PPHT minimises the amount of computation needed to detect lines by exploiting the difference in the fraction of votes needed to reliably detect lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the probabilistic HT; it is a function of the inherent complexity of data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved. The most salient features are likely to be detected first. While retaining its robustness, experiments show PPHT has, in many circumstances, advantages over the Standard HT.
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Stephens, Richard Sturge. "The Hough Transform : a probabilistic approach." Thesis, University of Cambridge, 1990. https://www.repository.cam.ac.uk/handle/1810/251579.

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Nordstrand, Lindgren Emelie, and Johan Sandmark. "Hough transform vid identifiering av hudförändringar." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166607.

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Att med bildigenkänning identifiera hudförändringar (ofta kallade födelsemärken) kan leda till snabbare upptäckt och diagnos jämfört med dagens manuella metoder. Hough transform är en känd algoritm inom bildigenkänning men har ännu inte applicerats direkt på hudförändringar. Uppsatsen undersöker om Hough transform kan användas med tillräckligt hög pålitlighet för att identifiera hudförändringar hos patienter. Den valda metoden är att med bildbehandling förbereda testbilder för att minimera brus och störningar, för att sedan med hjälp av MATLAB tillämpa algoritmen. Resultatet från uppsatsen med den valda metoden är att cirka 30% av hudförändringarna kunde identifieras. Slutsatsen är därmed att algoritmen, i denna implementation, inte ger ett tillräckligt korrekt resultat för att vara ett verktyg inom sjukvården. En annan slutsats är att mer undersökning behövs för att eliminera brus och störningar i testbilderna från bland annat kroppsbehåring.
Using image recognition to identify skin moles could lead to faster detection and diagnosis of skin cancer compared to the manual workflow used in health care today. Hough transform is a well known algorithm for image recognition but have not yet been applied directly to skin moles. This report examines if Hough transform can be used with sufficient reliability to identify skin moles on dermatology patients. The method is to prepare the test images to minimize noise and distrubence and then apply a MATLAB implementation of the algorithm. The results shows that about 30% of the skin moles could be identified. The conclusion from the report is that the algorithm, with the choosen method, does not provide a sufficiently accurate result to be used as a reliable tool in health care. Another conclusion is that more research is needed to eliminate noice and disturbance in the test images derived from e.g. bodyhair.
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Rodriguez, Artolazabal Jose Antonio. "Exploiting invariance in Hough transform algorithms." Thesis, University of Surrey, 2007. http://epubs.surrey.ac.uk/972/.

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Eriksson, Edvin. "Coordinate conversion for the Hough transform." Thesis, Uppsala universitet, Högenergifysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448782.

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This thesis attempts to develop a conversion algorithm between local coordinates in constituent detector modules and global coordinates encompassing the whole detector structure in a generic detector. The thesis is a part of preparatory work for studying the Hough Transform as a means of track reconstruction in the level-1 hardware trigger in the upgraded trigger and data acquisition (TDAQ) system in the phase 2 upgrade of the ATLAS detector at CERN. The upgrades being made are to withstand much more extreme conditions that come with the high-Luminosity Large Hadron Collider (HL-LHC). Two algorithms have been made and then implemented in Python scripts to test their feasibility and to compare them against each-other. The Rotation algorithm uses several rotations to correctly place the local coordinates in the global system. The second, the Shear algorithm, simplifies the process into two shears and one rotation, using the small angle approximation. Both algorithms need to be extended to work with more parts of the detector to be considered complete. Despite having lower maximum precision the second algorithm is considered the most promising attempt, since it is much less sensitive to the truncation error that results from working in an integer environment, which is a requirement for use in FPGAs.
I denna uppsats görs ett försök att skapa en omvandlingsalgoritm mellan lokala koordinater i konstituerande detektormoduler och globala koordinater i hela detektorstrukturen för en generisk detektor. Uppsatsen är en del i förberedande arbete för att undersöka hur Houghtransformen kan användas för spårrekonstruktion i den hårdvarubaserade level-1 triggern i det uppgraderade trigger- och datainsamlingssystemet (TDAQ) i fas två-uppgraderingen av ATLAS detektorn vid CERN. Uppgraderingarna som görs är för att kunna utstå de mycket mer extrema förhållanden som medförs av högluminositetsuppgraderingen av Large Hadron Collider (HL-LHC). Två algoritmer har skapats och implementerats i Pythonskript för att testa genomförbarhet och för att jämföra med varandra. Rotationsalgoritmen använder ett antal rotationer för att korrekt placera ut de lokala koordinaterna i det globala systemet. Den andra, Skjuvalgortimen, förenklar processen till två skjuvningar och en rotation med hjälp av liten vinkel-approximationen. Båda algoritmerna behöver utökas för att fungera för fler delar av detektorn för att anses kompletta. Trots lägre maximal precision bedöms den andra algoritmen vara det mest lovande försöket, eftersom den är mycket mindre känslig för trunkeringsfelet som kommer av att arbeta i en heltalsmiljö, som är ett krav för FPGA-implementationen.
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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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Kim, Jongwoo. "A robust hough transform based on validity /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842545.

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Zou, Rucong, and Hong Sun. "Building Extraction in 2D Imagery Using Hough Transform." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-17597.

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The purpose of this paper is to find out whether Hough transform if it is helpful to building extraction or not. This paper is written with the intention to come up with a building extraction algorithm that captures building areas in images as accurately as possible and eliminates background interference information, allowing the extracted contour area to be slightly larger than the building area itself. The core algorithm in this paper is based on the linear feature of the building edge and it removes interference information from the background. Through the test with ZuBuD database in Matlab, we can detect images successfully.  So according to this study, the Hough transform works for extracting building in 2D images.
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Tyler, Jonathan. "Muon identification with Veritas using the Hough Transform." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107695.

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Imaging atmospheric Cherenkov telescope (IACT) arrays such as VERITAS are used to perform very high-energy gamma-ray astronomy. This is accomplished by detecting and analyzing the Cherenkov light produced by gamma-ray initiated atmospheric air showers. IACTs also detect the Cherenkov light produced by individual muons. The Cherenkov light produced by muons is well understood, and can be used as a calibrated light source for the telescopes. Muons create characteristic annular patterns in the cameras of IACTs, which may be identified using parametrization algorithms. One such algorithm, the Hough transform, has been used to identify muons in VERITAS data. The details of the Hough transform and its implementation on VERITAS data will be described, as well as the use of parameters derived from the Hough transform for muon identification. In addition, the selection of muon rings appropriate for calibration purposes will be described. Finally, the Hough transform-based muon selection technique will be compared to the standard VERITAS muon selection technique.
Les systèmes de télescopes par imagerie Cherenkov tel que VERITAS sont utilisés pour l'astronomie à rayons gammas de très hautes énergies. Ceci est accompli par la détection et l'analyse de la lumière Cherenkov produite par les gerbes de particules causées par l'interaction des rayons gammas avec l'atmosphère. Ces télescopes détectent aussi la lumière Cherenkov produite par les muons. La lumière Cherenkov produite par les muons est bien comprise, et peut être utilisée comme source de calibration pour les télescopes. Les muons forment un anneau dans leur caméra, et peuvent être identifiés en utilisant des algorithmes de paramétrisation. La transformée de Hough est un de ces algorithmes, et a été utilisé afin d'identifier les muons dans les données de VERITAS. Les détails de la transformée de Hough et son application avec VERITAS seront présentés, ainsi que l'utilisation des paramêtres en découlant pour l'identification de muons. De plus, la sélection d'anneaux de muons appropriés pour des besoins de calibration sera décrite. Finalement, la technique de sélection de muons basé sur les transformées de Hough sera comparée à la technique de sélection de muons standard de VERITAS.
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Slininger, Timothy. "Robust hough transform for noisy and cluttered images." Thesis, Southern Connecticut State University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1525183.

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Finding arbitrary shapes within image data is a problem with applications ranging from Internet searching to intelligence data processing to analyzing zoning maps. This task is further complicated when the images are not pristine but contain noise. The effect of noise on the Generalized Hough Transform is analyzed using idealized images with variable amounts of noise. The ability of the algorithm to detect the desired shapes is reported as a function of the amount of noise. Time performance degradation is considered as are methods for increasing the ability of the algorithm to detect objects under various scale and rotation variations. As part of this thesis, a modular software platform was developed to support custom image processing algorithms including filtering, gradient transformations, edge detection, and implementations of the Hough Transform.

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Books on the topic "Hough Transform"

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Kälviäinen, Heikki. Randomized Hough transform: New extensions. Lappeenranta: Lappeenrannan teknillinen korkeakoulu, 1994.

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

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C, Martin Robert. Interpolating beyond the quantization of the Hough transform. Toronto: University of Toronto, Dept. of Computer Science, 1990.

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

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

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Zorski, Witold. Pattern recognition of irregular shapes using the Hough transform. Leicester: De Montfort University, 2001.

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

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Latt, Khine. Sonar-based localization of mobile robots using the Hough transform. Monterey, Calif: Naval Postgraduate School, 1997.

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P, Banks Stephen. Simple object recognition by neural networks: Application of the Hough Transform. Sheffield: University of Sheffield, Dept. of Control Engineering, 1990.

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Book chapters on the topic "Hough Transform"

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Ranka, Sanjay, and Sartaj Sahni. "Hough Transform." In Bilkent University Lecture Series, 145–66. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4613-9692-5_6.

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Bräunl, Thomas, Stefan Feyrer, Wolfgang Rapf, and Michael Reinhardt. "Hough Transform." In Parallel Image Processing, 83–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04327-1_9.

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Cantoni, Virginio, and Elio Mattia. "Hough Transform." In Encyclopedia of Systems Biology, 917–18. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1310.

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James, Thomas Owen. "The Hough Transform." In Springer Theses, 39–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31934-2_4.

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Yuen, Shiu Yin K. "Connective Hough Transform." In BMVC91, 127–35. London: Springer London, 1991. http://dx.doi.org/10.1007/978-1-4471-1921-0_17.

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Flores-Mendez, Alejandro, and Angeles Suarez-Cervantes. "Circular Degree Hough Transform." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 287–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_34.

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Leavers, V. F. "Which Hough?" In Shape Detection in Computer Vision Using the Hough Transform, 113–35. London: Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-1940-1_6.

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Lin, Yancong, Silvia L. Pintea, and Jan C. van Gemert. "Deep Hough-Transform Line Priors." In Computer Vision – ECCV 2020, 323–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58542-6_20.

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Leavers, V. F. "The dynamic generalized hough transform." In Computer Vision — ECCV 90, 592–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/bfb0014916.

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Guil, N., and E. L. Zapata. "A parallel pipelined Hough Transform." In Lecture Notes in Computer Science, 131–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0024694.

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Conference papers on the topic "Hough Transform"

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Yuen, Shiu Yin K. "Connective Hough Transform." In British Machine Vision Conference 1991. Springer-Verlag London Limited, 1991. http://dx.doi.org/10.5244/c.5.17.

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Ibrahim, Mohammad K., E. C. L. Ngau, and Mohammad F. Daemi. "Weighted Hough transform." In Robotics - DL tentative, edited by David P. Casasent. SPIE, 1992. http://dx.doi.org/10.1117/12.57063.

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Cyganski, David, William F. Noel, and John A. Orr. "Analytic Hough transform." In SC - DL tentative, edited by Bernd Girod. SPIE, 1990. http://dx.doi.org/10.1117/12.20013.

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Matas, J., C. Galambos, and J. Kittler. "Progressive Probabilistic Hough Transform." In British Machine Vision Conference 1998. British Machine Vision Association, 1998. http://dx.doi.org/10.5244/c.12.26.

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Leavers, V. F. "Dynamic generalized Hough transform." In SC - DL tentative, edited by Leonard A. Ferrari and Rui J. P. de Figueiredo. SPIE, 1990. http://dx.doi.org/10.1117/12.19754.

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Lei Zhu and Zhaoqi Chen. "Probabilistic Convergent Hough Transform." In 2008 International Conference on Information and Automation (ICIA). IEEE, 2008. http://dx.doi.org/10.1109/icinfa.2008.4608271.

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Huang, Xinhan, Wei Li, and Min Wang. "New fast Hough transform." In International Symposium on Multispectral Image Processing, edited by Ji Zhou, Anil K. Jain, Tianxu Zhang, Yaoting Zhu, Mingyue Ding, and Jianguo Liu. SPIE, 1998. http://dx.doi.org/10.1117/12.323654.

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Tu, Chunling, Karim Djouani, Barend Jacobus van Wyk, Yskandar Hamam, and Shengzhi Du. "Good resolutions for Hough Transform." In 2012 10th World Congress on Intelligent Control and Automation (WCICA 2012). IEEE, 2012. http://dx.doi.org/10.1109/wcica.2012.6359409.

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Kiryati, N., M. Lindenbaum, and A. M. Brucksiein. "Digital or analog Hough Transform?" In British Machine Vision Conference 1990. British Machine Vision Association, 1990. http://dx.doi.org/10.5244/c.4.59.

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Atherton, T. J., and D. J. Kerbyson. "The Coherent Circle Hough Transform." In British Machine Vision Conference 1993. British Machine Vision Association, 1993. http://dx.doi.org/10.5244/c.7.27.

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Reports on the topic "Hough Transform"

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Li, Duwang. Invariant pattern recognition algorithm using the Hough Transform. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5783.

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Nasrabadi, Nasser M. Application of Multi-Channel Hough Transform to Stereo Vision. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada207937.

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Grimson, W. E., and Daniel P. Huttenlocher. On the Sensitivity of the Hough Transform for Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada202372.

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Rerkngamsanga, Pornrerk. Generalized Hough Transform for Object Classification in the Maritime Domain. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1009203.

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

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Aganj, Iman, Christophe Lenglet, Neda Jahanshad, Essa Yacoub, Noam Harel, Paul M. Thompson, and Guillermo Sapiro. A Hough Transform Global Probabilistic Approach to Multiple-Subject Diffusion MRI Tractography. Fort Belvoir, VA: Defense Technical Information Center, April 2010. http://dx.doi.org/10.21236/ada540720.

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Shyu, Haw-Jye, and Yung P. Lee. Application of the Bearing Trace, Hough Transform (BTHT) to Passive Shipping Lane Monitoring. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada337380.

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DiDonato, Armido. Target Location and ID From a Passive Multistatic Sensor Network Using Time Differences of Arrival (TDOAs) and the Hough Transform. Fort Belvoir, VA: Defense Technical Information Center, November 2008. http://dx.doi.org/10.21236/ada509798.

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