Literatura académica sobre el tema "FUZZY EDGE DETECTION"
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Artículos de revistas sobre el tema "FUZZY EDGE DETECTION"
Nayak, Dillip Ranjan. "Edge Detection Using Fuzzy Double Gradient Morphology". Bonfring International Journal of Advances in Image Processing 04, n.º 01 (15 de diciembre de 2014): 01–04. http://dx.doi.org/10.9756/bijaip.10357.
Texto completoLing, Xian Qing, Jun Lu y Lei Wang. "Image Edge Detection Based on Direction Fuzzy Entropy". Advanced Materials Research 268-270 (julio de 2011): 1234–38. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1234.
Texto completoPeric, Nebojsa. "Fuzzy logic and fuzzy set theory based edge detection algorithm". Serbian Journal of Electrical Engineering 12, n.º 1 (2015): 109–16. http://dx.doi.org/10.2298/sjee1501109p.
Texto completoLiang, Lily Rui y Carl G. Looney. "Competitive fuzzy edge detection". Applied Soft Computing 3, n.º 2 (septiembre de 2003): 123–37. http://dx.doi.org/10.1016/s1568-4946(03)00008-5.
Texto completoRanjan, Rakesh y Dr Vinay Avasthi. "Enhanced Edge Detection Technique in Digital Images Using Optimised Fuzzy Operation". Webology 19, n.º 1 (20 de enero de 2022): 5402–16. http://dx.doi.org/10.14704/web/v19i1/web19362.
Texto completoAbdul Khalid, Noor Elaiza, Mazani Manaf y Mohd Ezane Aziz. "Efficient Edge Detection Using Fuzzy Heuristic Particle Swarm Optimization". Scientific Research Journal 6, n.º 1 (30 de junio de 2009): 43. http://dx.doi.org/10.24191/srj.v6i1.5637.
Texto completoPaika, Er Vishal y Er Pankaj Bhambri. "Edge Detection Fuzzy Inference System". INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 4, n.º 1 (30 de junio de 2013): 148–55. http://dx.doi.org/10.24297/ijmit.v4i1.811.
Texto completoSheikh Akbari, A. y J. J. Soraghan. "Fuzzy-based multiscale edge detection". Electronics Letters 39, n.º 1 (2003): 30. http://dx.doi.org/10.1049/el:20030074.
Texto completoSeethalakshmi, K., S. Valli, T. Veeramakali, K. V. Kanimozhi, S. Hemalatha y M. Sambath. "An efficient fuzzy deep learning approach to recognize 2D faces using FADF and ResNet-164 architecture". Journal of Intelligent & Fuzzy Systems 42, n.º 4 (4 de marzo de 2022): 3241–50. http://dx.doi.org/10.3233/jifs-211114.
Texto completoDhivya, R. y R. Prakash. "Edge Detection of Images Using Improved Fuzzy C-Means and Artificial Neural Network Technique". Journal of Medical Imaging and Health Informatics 9, n.º 6 (1 de agosto de 2019): 1284–93. http://dx.doi.org/10.1166/jmihi.2019.2719.
Texto completoTesis sobre el tema "FUZZY EDGE DETECTION"
Wang, Ziqing. "Fuzzy neural network for edge detection and Hopfield network for edge enhancement". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0005/MQ42458.pdf.
Texto completoZhao, Zhenchun. "Design of a computer human face recognition system using fuzzy logic". Thesis, University of Huddersfield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323781.
Texto completoBueno, Regis Cortez. "Detecção de contornos em imagens de padrões de escoamento bifásico com alta fração de vazio em experimentos de circulação natural com o uso de processamento inteligente". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/85/85133/tde-22042016-130130/.
Texto completoThis work has developed a new method for digital image contour detection which can be successfully applied to images presenting objects of interest with high proximity and presenting complexities related with background abrupt intensity fluctuations. The developed method makes use of fuzzy logic and declivity standard deviation (Fuzzy Declivity Standard Deviation FuzDec) to image processing and contour detection. Contour detection is an important task to estimate two-phase flow features through bubble segmentation in order to obtain parameters as void fraction and bubble diameter. FuzDec was applied to natural circulation instability images which were experimentally acquired. Image acquisition was done at the Natural Circulation Circuit (CCN) of the Instituto de Pesquisas Energéticas e Nucleares (IPEN) in Brazil. This facility is all made up with glass tubes allowing easy visualization and imaging of one-phase and two-phase flow patterns during natural circulation cycles under low pressures. Results confirm that the proposed detector can improve contour identification when compared to classical contour detector algorithms, without using smoothing algorithms or human intervention.
BUENO, REGIS C. "Detecção de contornos em imagens de padrões de escoamento bifásico com alta fração de vazio em experimentos de circulação natural com o uso de processamento inteligente". reponame:Repositório Institucional do IPEN, 2016. http://repositorio.ipen.br:8080/xmlui/handle/123456789/26817.
Texto completoMade available in DSpace on 2016-11-11T13:03:47Z (GMT). No. of bitstreams: 0
Este trabalho desenvolveu um novo método para a detecção de contornos em imagens digitais que apresentam objetos de interesse muito próximos e que contêm complexidades associadas ao fundo da imagem como variação abrupta de intensidade e oscilação de iluminação. O método desenvolvido utiliza lógicafuzzy e desvio padrão da declividade (Desvio padrão da declividade fuzzy - FuzDec) para o processamento de imagens e detecção de contorno. A detecção de contornos é uma tarefa importante para estimar características de escoamento bifásico através da segmentação da imagem das bolhas para obtenção de parâmetros como a fração de vazio e diâmetro de bolhas. FuzDec foi aplicado em imagens de instabilidades de circulação natural adquiridas experimentalmente. A aquisição das imagens foi feita utilizando o Circuito de Circulação Natural (CCN) do Instituto de Pesquisas Energéticas e Nucleares (IPEN). Este circuito é completamente constituído de tubos de vidro, o que permite a visualização e imageamento do escoamento monofásico e bifásico nos ciclos de circulação natural sob baixa pressão.Os resultados mostraram que o detector proposto conseguiu melhorar a identificação do contorno eficientemente em comparação aos detectores de contorno clássicos, sem a necessidade de fazer uso de algoritmos de suavização e sem intervenção humana.
t
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP
SINGH, ISHA. "SOME STUDIES ON IMAGE ENHANCEMENT AND FILTERING". Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18449.
Texto completoBoaventura, Inês Aparecida Gasparotto. "Números fuzzy em processamento de imagens digitais e suas aplicações na detecção de bordas". Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-06052010-154227/.
Texto completoThe purpose of this work is to introduce a new approach, based on fuzzy numbers, for edge detection in gray level images. The proposed approach is called FUNED (Fuzzy Number Edge Detector). The edge detection technique, implemented by FUNED, considers a local neighborhood of image pixels, defined by the user and, based on fuzzy numbers concept, it is verified whether a pixel belongs to that image region, according to the gray level intensity in the region. The pixel that does not belong to the region is then classified as a possible edge pixel. Therefore, through a membership function, the proposed technique provides a membership matrix in gray levels and, through the choice of a threshold, the image edges are segmented. For the modeling of the problem, the gray levels are considered fuzzy numbers and, for each pixel gi,j of the image, it is computed its membership regarding to a specific region, considering the neighbors presenting gray levels near gi,j. When considering gray-values as fuzzy numbers, the inherent variability of the image gray values are incorporated, thus promoting a more powerful approach for the treatment of digital images as compares with the classic treatment based on analytical formulation. For the assessment of the performance of the technique, it was used gray-level synthetics and real images, obtained from the literature, and qualitative and quantitative tests were carried out. To achieve the quantitative tests, it was developed a new methodology for evaluating edge detectors based on ROC analysis. The evaluation process developed considers various measures, that are taken by comparing the edges obtained with the ideal edges. The results of the assessment showed that the FUNED is more computationally efficient when compared to the results obtained by Canny and Sobel detectors and, also to other fuzzy approaches. The technique allows the user to adjust several parameters. The adjustment of these parameters provide several image edge visualization possibilities, which allow the choice of details in the image. The computational implementation of FUNED is intuitive and with good performance both for obtaining edges as in processing time, being suitable for real time applications with hardware implementation.
Ruiz, Aguilera Daniel. "Contribució a l'estudi de les uninormes en el marc de les equacions funcionals. Aplicacions a la morfologia matemàtica". Doctoral thesis, Universitat de les Illes Balears, 2007. http://hdl.handle.net/10803/9411.
Texto completoLas uninormas son unos operadores de agregación que, por su definición se pueden considerar como conjunciones o disjunciones y que han sido aplicados a campos muy diversos. En este trabajo se estudian algunas ecuaciones funcionales que tienen como incógnitas las uninormas, o operadores definidos a partir de ellas.
Una de ellas es la distributividad, que se resuelve para las classes de uninormas conocidas, solucionando, en particular, un problema abierto en la teoría del análisis no estándar. También se estudian las implicaciones residuales y fuertes definidas a partir de uninormas, encontrando solución a la distributividad de estas implicaciones sobre uninormas. Como aplicación de estos estudios, se revisa y amplía la morfología matemática borrosa basada en uninormas, que proporciona un marco inicial favorable para un nuevo enfoque en el análisis de imágenes, que tendrá que ser estudiado en más profundidad.
Uninorms are aggregation operators that, due to its definition, can be considered as conjunctions or disjunctions, and they have been applied to very different fields. In this work, some functional equations are studied, involving uninorms, or operators defined from them as unknowns. One of them is the distributivity equation, that is solved for all the known classes of uninorms, finding solution, in particular, to one open problem in the non-standard analysis theory. Residual implications, as well as strong ones defined from uninorms are studied, obtaining solution to the distributivity equation of this implications over uninorms. As an application of all these studies, the fuzzy mathematical morphology based on uninorms is revised and deeply studied, getting a new framework in image processing, that it will have to be studied in more detail.
Karabagli, Bilal. "Vérification automatique des montages d'usinage par vision : application à la sécurisation de l'usinage". Phd thesis, Université Toulouse le Mirail - Toulouse II, 2013. http://tel.archives-ouvertes.fr/tel-01018079.
Texto completoWang, Chyi-Cheng y 王麒程. "Edge detection of noisy blurred image using fuzzy-edge-operator". Thesis, 1994. http://ndltd.ncl.edu.tw/handle/20397846252447662560.
Texto completo中原大學
電子工程學系
82
In this paper, we propose a new edge operator for edge detection of noisy blurred images.Our new operator "fuzzy-edge- operator" based on the theory of fuzzy sets performs edge detection of noisy blurred images faster and more perfectly than other existed operators in both speed and resolution. In the edge detection of a noisy blurred image, the conventional edge operators may require noise elimination and noise suppressing processes which may lose some original important information of edge. On the other hand, edge detection systems using our fuzzy-edge-operator can detect edge directly without preprocessing noise. Also, the results of edge detection using our fuzzy-edge-operator in both speed and cost are much better because the edge detection system using fuzzy-edge-operator is composed of coincidence, XOR and comparators only. Experimental results of noisy blurred images are given.From our experimental results, it shows that the performance of fuzzy-edge-operator for edge detection is much more satisfactory than that of other operators in considerations of noise tolerance, speed and cost.
(9777542), Mohamed Anver. "Fuzzy algorithms for image enhancement and edge detection". Thesis, 2004. https://figshare.com/articles/thesis/Fuzzy_algorithms_for_image_enhancement_and_edge_detection/13465622.
Texto completoLibros sobre el tema "FUZZY EDGE DETECTION"
Gonzalez, Claudia I., Patricia Melin, Juan R. Castro y Oscar Castillo. Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53994-2.
Texto completoGonzalez, Claudia I., Oscar Castillo, Patricia Melin y Juan R. Castro. Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic. Springer International Publishing AG, 2017.
Buscar texto completoCapítulos de libros sobre el tema "FUZZY EDGE DETECTION"
Marco-Detchart, C., G. Lucca, G. Dimuro, T. Asmus, C. Lopez-Molina, E. Borges, J. A. Rincon, V. Julian y H. Bustince. "Fuzzy Integrals for Edge Detection". En Fuzzy Logic and Technology, and Aggregation Operators, 330–41. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39965-7_28.
Texto completoTizhoosh, Hamid R. "Fast and Robust Fuzzy Edge Detection". En Fuzzy Filters for Image Processing, 178–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36420-7_8.
Texto completoHo, Kenneth H. L. y Noboru Ohnishi. "FEDGE — Fuzzy edge detection by Fuzzy Categorization and Classification of edges". En Fuzzy Logic in Artificial Intelligence Towards Intelligent Systems, 182–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62474-0_14.
Texto completoWojcik, Zbigniew M. "Accurate Edge Detection Using Rough Sets". En Rough Sets, Fuzzy Sets and Knowledge Discovery, 403–11. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-3238-7_47.
Texto completoRubio, Yoshio, Oscar Montiel y Roberto Sepúlveda. "Cellular Automata Enhanced Quantum Inspired Edge Detection". En Fuzzy Logic in Intelligent System Design, 141–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67137-6_15.
Texto completoBecerikli, Yasar y Tayfun M. Karan. "A New Fuzzy Approach for Edge Detection". En Computational Intelligence and Bioinspired Systems, 943–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494669_116.
Texto completoWang, Gang y Bernard De Baets. "Edge Detection Based on the Fusion of Multiscale Anisotropic Edge Strength Measurements". En Advances in Fuzzy Logic and Technology 2017, 530–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66827-7_49.
Texto completoKatoch, Rachita y Rosepreet Kaur Bhogal. "Edge Detection Using Fuzzy Logic (Fuzzy Sobel, Fuzzy Template, and Fuzzy Inference System)". En Advances in Intelligent Systems and Computing, 741–52. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5903-2_76.
Texto completoZhu, Wen, Beiping Hou, Zhegen Zhang y Kening Zhou. "Study on Wavelet-Based Fuzzy Multiscale Edge Detection Method". En Fuzzy Systems and Knowledge Discovery, 703–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_86.
Texto completoLi, Zhe y Yindi Wang. "Moving Vehicle Detection Combining Edge Detection and Gaussian Mixture Models". En Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 229–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_24.
Texto completoActas de conferencias sobre el tema "FUZZY EDGE DETECTION"
Yalcin, Eyup, Hasan Badem y Mahit Gunes. "CUDA-based hybrid intuitionistic fuzzy edge detection algorithm". En 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7338008.
Texto completoLing, Wing-Kuen y Kwong-Shun Tam. "Edge detection using fuzzy switch". En Electronic Imaging 2002, editado por Edward R. Dougherty, Jaakko T. Astola y Karen O. Egiazarian. SPIE, 2002. http://dx.doi.org/10.1117/12.468016.
Texto completoMadasu, Vamsi Krishna y Shantaram Vasikarla. "Fuzzy Edge Detection in Biometric Systems". En 2007 36th IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2007). IEEE, 2007. http://dx.doi.org/10.1109/aipr.2007.10.
Texto completoAnas, Essa. "Edge detection techniques using fuzzy logic". En 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2016. http://dx.doi.org/10.1109/spin.2016.7566682.
Texto completoBarskar, Raju y Gulfishan Firdose Ahmed. "CBIR using fuzzy edge detection mask". En the 2011 International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1947940.1947999.
Texto completoBezdek, James C., Ramachandran Chandrasekhar y Yianni Attikiouzel. "New fuzzy model for edge detection". En Aerospace/Defense Sensing and Controls, editado por Bruno Bosacchi y James C. Bezdek. SPIE, 1996. http://dx.doi.org/10.1117/12.243250.
Texto completoMusilek, Petr, Madan M. Gupta y Gregory J. Schmidt. "Adaptive fuzzy approach to edge detection". En Photonics East '99, editado por George K. Knopf, Patrick F. Muir y Peter E. Orban. SPIE, 1999. http://dx.doi.org/10.1117/12.371167.
Texto completoThakkar, Mehul y Hitesh Shah. "Edge detection techniques using fuzzy thresholding". En 2011 World Congress on Information and Communication Technologies (WICT). IEEE, 2011. http://dx.doi.org/10.1109/wict.2011.6141263.
Texto completoTicala, Cristina, Camelia-M. Pintea, Simone A. Ludwig, Mara Hajdu-Macelaru, Oliviu Matei y Petrica C. Pop. "Fuzzy Index Evaluating Image Edge Detection obtained with Ant Colony Optimization". En 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882851.
Texto completoXiao-Ping Zong y Wei-Wei Liu. "Fuzzy edge detection based on wavelets transform". En 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620897.
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