Готові списки джерел за темами / FUZZY EDGE DETECTION

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Добірка наукової літератури з теми "FUZZY EDGE DETECTION"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "FUZZY EDGE DETECTION"

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Nayak, Dillip Ranjan. "Edge Detection Using Fuzzy Double Gradient Morphology." Bonfring International Journal of Advances in Image Processing 04, no. 01 (December 15, 2014): 01–04. http://dx.doi.org/10.9756/bijaip.10357.

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Ling, Xian Qing, Jun Lu, and Lei Wang. "Image Edge Detection Based on Direction Fuzzy Entropy." Advanced Materials Research 268-270 (July 2011): 1234–38. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1234.

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Анотація:
To improve the ability of the fuzzy edge detection and anti-noise performance, the paper proposes a new weighted direction fuzzy entropy image edge detection method. The proposed method converts the feature space of image gray to the fuzzy feature space, and then extracts the weighted information measure of the direction structural in the fuzzy entropy feature space. Finally, the proposed method determines the edge pixel by an adaptive threshold after non-maxima suppression. The experiment demonstrates that the proposed method can extract the image edges effectively by means of the fuzzy edge detection.
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Peric, Nebojsa. "Fuzzy logic and fuzzy set theory based edge detection algorithm." Serbian Journal of Electrical Engineering 12, no. 1 (2015): 109–16. http://dx.doi.org/10.2298/sjee1501109p.

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Анотація:
In this paper we will show a way how to detect edges in digital images. Edge detection is a fundamental part of many algorithms, both in image processing and in video processing. Therefore it is important that the algorithm is efficient and, if possible, fast to carry out. The fuzzy set theory based approach on edge detection is good for use when we need to make some kind of image segmentation, or when there is a need for edge classification (primary, secondary, ...). One example that motivated us is region labeling; this is a process by which the digital image is divided in units and each unit is given a unique label (sky, house, grass, ?, etc.). To accomplish that, we need to have an intelligent system that will precisely determine the edges of the region. In this paper we will describe tools from image processing and fuzzy logic that we use for edge detection as well as the proposed algorithm.
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Liang, Lily Rui, and Carl G. Looney. "Competitive fuzzy edge detection." Applied Soft Computing 3, no. 2 (September 2003): 123–37. http://dx.doi.org/10.1016/s1568-4946(03)00008-5.

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Ranjan, Rakesh, and Dr Vinay Avasthi. "Enhanced Edge Detection Technique in Digital Images Using Optimised Fuzzy Operation." Webology 19, no. 1 (January 20, 2022): 5402–16. http://dx.doi.org/10.14704/web/v19i1/web19362.

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Анотація:
In image processing, edge detection is a critical issue. Edge detection is a key approach for evaluating the edge of various objects in a digital image. These edges are found using the gradients, which are present in the image. The intensity and value of pixels determine the gradients. In digital images, edge detection lowers the quantity of data and filters out irrelevant data while maintaining the image's key structural features. In this paper, a new edge detection approach based on a fuzzy rule-based system is proposed. In digital image processing, the proposed method typically depends on fuzzy logic systems. The main goal of this system is to show how fuzzy logic may be used in image processing. This paper provides a fuzzy logic-based edge detection technique that uses a sharpening Gabor filter to regulate edge quality and a Gaussian filter to reduce noise caused by sharpening. This is determined by utilizing applications such as “Peak Signal to Noise Ratio (PSNR) F-Measure, and Hausdorff distance (HoD) to prove that fuzzy logic outperforms the proposed system. The findings for edge detection approaches are included in high quality. The proposed approach outperforms most commonly used traditional edge detection methods. The proposed method also reduces the number of noisy features and may be used for a wide range of images.
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Abdul Khalid, Noor Elaiza, Mazani Manaf, and Mohd Ezane Aziz. "Efficient Edge Detection Using Fuzzy Heuristic Particle Swarm Optimization." Scientific Research Journal 6, no. 1 (June 30, 2009): 43. http://dx.doi.org/10.24191/srj.v6i1.5637.

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Анотація:
This paper presents a hybridization of Particle Swarm Optimization (PSO) and Fuzzy edge detector. The edge detector is used as the initial population and as the objective function. The purpose of hybridizing the algorithm is to create an optimized edge detector. Classical Fuzzy Heuristics (CFH) detects thick edges. These thick edges need to be optimized to obtain a thin line. In this research the PSO is used to optimize the edge detection detected by the CFH algorithm and it is referred to as FHPSO. The test images are radiographs images of the metacarpal. These images have been used, because there is a need to detect strong and thin edges. Radiograph images are noisy in nature, which makes it difficult to measure the cortical thickness, the cortical outline of the inner cortical and outer cortical of the long tubular bone. The outer cortical edges are considered to be the strong edges due to high discontinuity values and the inner cortical edges are considered weak edges due to low their discontinuity values. The performance of FHPSO in detecting edges has been shown to be quite efficient.
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Paika, Er Vishal, and Er Pankaj Bhambri. "Edge Detection Fuzzy Inference System." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 4, no. 1 (June 30, 2013): 148–55. http://dx.doi.org/10.24297/ijmit.v4i1.811.

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Анотація:
In this paper a method has been developed for automatic edge detection of an digital image. An edge is made up of those pixels at which there is an abrupt change in the intensity. These pixels are known as edge pixels and are connected to give an edge. In this paper we have developed a mamdanis fuzzy inference system in MATLAB 2008 using fuzzy logic tool box. A smallest possible 2X2 window is used as a scanning mask. Mask slides over the whole image pixel by pixel, first horizontally in topmost horizontal line then after reaching at the end of line, it increments to check the next vertical location and it continues till the whole image is scanned. The FIS built has 4 inputs, each input representing a pixel for 2X2 mask, and 1 output that represents pixel under consideration. The rule editor consists of sixteen fuzzy rules. The results thus obtained are compared with Sobel edge operator and Canny edge operator.
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Sheikh Akbari, A., and J. J. Soraghan. "Fuzzy-based multiscale edge detection." Electronics Letters 39, no. 1 (2003): 30. http://dx.doi.org/10.1049/el:20030074.

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Seethalakshmi, K., S. Valli, T. Veeramakali, K. V. Kanimozhi, S. Hemalatha, and M. Sambath. "An efficient fuzzy deep learning approach to recognize 2D faces using FADF and ResNet-164 architecture." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 3241–50. http://dx.doi.org/10.3233/jifs-211114.

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Анотація:
Deep learning using fuzzy is highly modular and more accurate. Adaptive Fuzzy Anisotropy diffusion filter (FADF) is used to remove noise from the image while preserving edges, lines and improve smoothing effects. By detecting edge and noise information through pre-edge detection using fuzzy contrast enhancement, post-edge detection using fuzzy morphological gradient filter and noise detection technique. Convolution Neural Network (CNN) ResNet-164 architecture is used for automatic feature extraction. The resultant feature vectors are classified using ANFIS deep learning. Top-1 error rate is reduced from 21.43% to 18.8%. Top-5 error rate is reduced to 2.68%. The proposed work results in high accuracy rate with low computation cost. The recognition rate of 99.18% and accuracy of 98.24% is achieved on standard dataset. Compared to the existing techniques the proposed work outperforms in all aspects. Experimental results provide better result than the existing techniques on FACES 94, Feret, Yale-B, CMU-PIE, JAFFE dataset and other state-of-art dataset.
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Dhivya, R., and R. Prakash. "Edge Detection of Images Using Improved Fuzzy C-Means and Artificial Neural Network Technique." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1284–93. http://dx.doi.org/10.1166/jmihi.2019.2719.

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Анотація:
Edge detection (ED) is an embryonic development, which is essential for any intricate image processing and recognition undertaking. This paper proposed another system to upgrade the method and Artificial neural network for speaking to vulnerability in the image slopes and collection. The vulnerability in the image inclination distinguishes the genuine edges which might be overlooked by other systems. This e is valuable in the field of restorative imaging applications, for example, MRI division, cerebrum tumor, filtering and so on. Attractive reaction imaging connected in restorative science to analyze tumors in body parts by creating great images of within the human body, by utilizing different edge identifiers. There exist many edge finders yet at the same time, requirement for inquire about is felt improve their execution. And furthermore, this paper distinguishes the edges in the broken bones, edge ID, satellite edge detection ID. An exceptionally basic issue looked by many edge finders is the decision of limit esteems. This paper presents fuzzy and ANN based edge detection utilizing Improved Fuzzy C-means clustering (FCM) strategy. Enhanced FCM approach is utilized in producing different gatherings which are then contribution to the Mamdani fuzzy surmising framework. In this, we are utilizing versatile middle separating for evacuating commotion; this strategy adequately expels the clamor and gives better outcomes. This entire procedure results in the age of the limit parameters which is then encouraged to the established sobel edge locator which helps in improving its edge detection capacity utilizing the fuzzy logic. This entire setup is connected to Images. The recovered outcomes express to that fuzzy and ANN based Improved Fuzzy C-means clustering enhances the introduction of customary sobel edge identifier in associate with retentive information around the tumors of the mind.
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Більше джерел

Дисертації з теми "FUZZY EDGE DETECTION"

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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.

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Zhao, 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.

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Bueno, 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/.

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Анотація:
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.
This 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.
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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.

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Анотація:
Submitted by Claudinei Pracidelli (cpracide@ipen.br) on 2016-11-11T13:03:47Z No. of bitstreams: 0
Made 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.
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IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP
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SINGH, ISHA. "SOME STUDIES ON IMAGE ENHANCEMENT AND FILTERING." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18449.

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Анотація:
Images are one of the best sources for research & communication. Proper analysis of image data is essential for various fields like satellite images & remote sensing, Biometrics, Criminology, military surveillance, astrology & many more. Requirements of the users vary according to the nature of application in use.Image processing plays a critical role in the analysis of data and is an integral part for various applications. Image denoising is one of the challenging branches of image processing. Impulse noise is among the prevalent noises that degrade the image quality and its subsequent noise suppression plays a pivotal role in the enhancement of images. The presence of impulse noise cannot be averted during the digitization, acquisition, and transmission of images. Many state of art filters are available in the literature to deal with impulse noise encountered in images. Filters having dual modes of detection and restoration exhibit superior performance in removing noise and thereby keeping the original information of the images intact. In real world sometimes the user is uncertain about his requirements therefore the characteristics of the employed filter for impulse noise removal should be adaptable to the indecisive features of an image.The denoising filter should be robust enough to handle the varying amounts of noise density and should be intuitive in nature. This thesis work tries to cater to all the essential features required for an efficient filter. Incorporation of fuzzy logic makes the filter more versatile.Investigations performed in this thesis show that the proposed work excels in the quantitative as well as qualitative manner. Four schemes introduced in this thesis are : (i) High-density impulse noise detection using FCM algorithm (HDIND) viii (ii) Edge preserving fuzzy filter for the suppression of impulse noise in images (EFFSIN) (iii) Heuristic analysis of neighboring pixels for impulse noise detection (SPHN) (iv) Impulse noise removal in color image sequences using Fuzzy logic (INFL) The initial three schemes namely HDIND, EFFSIN, and SPHN focus on the grayscale images and INFL is proposed for color image sequences. All the schemes incorporate an efficient detection criterion and after proper classification of noisy and noise-free pixels, performs the restoration procedure. The use of fuzzy logic in the methods has enhanced the decision making aspect of the algorithms to classify the noisy pixels present in an image. The simulation results are done in isolation for all the schemes deduce that HDIND and EFFSIN are robust in nature and their performance does not deteriorates with a rise in noise density. The edge-preserving nature of EFFSIN preserves the original image data and false alarm rates are reduced. SPHN provides good PSNR and MSE results. INFL is a Spatio-temporal filter that gives excellent performance. SSIM, PSNR, MSE, False alarm and Miss detection rates are used as quality measures to analyze the proposed mechanisms.
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Boaventura, 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/.

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Анотація:
O objetivo deste trabalho é apresentar uma nova abordagem, baseada no conceito de números fuzzy, para detecção de bordas em imagens digitais chamado FUNED (Fuzzy Number Edge Detector). A técnica de detecção de bordas implementada pelo FUNED considera uma vizinhança local dos pixels da imagem, definida pelo usuário e, baseado no conceito de números fuzzy, é verificado se um pixel pertence ou não aquela região da imagem, com base na intensidade dos tons de cinza que compõem a região. O pixel que não pertence à região é então classificado como um possível pixel de borda. Através de uma função de pertinência, a técnica proposta fornece uma matriz de pertinência em tons de cinza e, pela escolha de um limiar, as bordas da imagem são segmentadas. Para a modelagem do problema, os tons de cinza são considerados como números fuzzy e, para cada pixel gi,j da imagem, calcula-se a sua pertinência em relação a uma determinada região, considerando os vizinhos que possuem níveis de cinza próximos de gi,j. Ao considerar os valores de cinza como números fuzzy, incorpora-se a variabilidade inerente dos valores de cinza de imagens, proporcionando assim uma abordagem mais adequada ao tratamento de imagens digitais, em comparação ao tratamento clássico, baseado em uma formulação analítica. Para avaliação do desempenho da técnica, foram usadas imagens sintéticas e imagens reais em tons de cinza, obtidas na literatura, e realizados testes qualitativos e quantitativos. Para a realização dos testes quantitativos, foi desenvolvida uma nova metodologia de avaliação de detectores de bordas baseada na análise ROC. O processo de avaliação desenvolvido considera diferentes medidas, que são tomadas comparando-se as bordas obtidas com as bordas ideais. Os resultados da avaliação de desempenho mostraram que o FUNED é eficaz computacionalmente quando comparado aos detectores de Canny e de Sobel e, também a outras abordagens fuzzy. A técnica permite ao usuário o ajuste dos seguintes parâmetros: o tamanho da vizinhança local, o suporte de um número fuzzy e o limiar. O ajuste desses parâmetros proporciona diversas possibilidades de visualização das bordas de uma imagem, permitindo a escolha de detalhes da imagem. A implementação computacional do FUNED é intuitiva e com bom desempenho tanto para obtenção de bordas como em tempo de processamento, sendo adequada para aplicações em tempo real com implementação em hardware.
The 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.
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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.

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Анотація:
Les uninormes són uns operadors d'agregació que, per la seva definició, es poden considerar com a conjuncions o disjuncions, i que han estat aplicades a camps molt diversos. En aquest treball s'estudien algunes equacions funcionals que tenen com a incògnites les uninormes, o operadors definits a partir d'elles. Una d'elles és la distributivitat, que és resolta per les classes d'uninormes conegudes, solucionant, en particular, un problema obert en la teoria de l'anàlisi no-estàndard. També s'estudien les implicacions residuals i fortes definides a partir d'uninormes, trobant solució a la distributivitat d'aquestes implicacions sobre uninormes. Com a aplicació d'aquests estudis, es revisa i s'amplia la morfologia matemàtica borrosa basada en uninormes, que proporciona un marc inicial favorable per a un nou enfocament en l'anàlisi d'imatges, que haurà de ser estudiat en més profunditat.
Las 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.
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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.

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Анотація:
Le terme "usinage à porte fermée", fréquemment employé par les PME de l'aéronautique et de l'automobile, désigne l'automatisation sécurisée du processus d'usinage des pièces mécaniques. Dans le cadre de notre travail, nous nous focalisons sur la vérification du montage d'usinage, avant de lancer la phase d'usinage proprement dite. Nous proposons une solution sans contact, basée sur la vision monoculaire (une caméra), permettant de reconnaitre automatiquement les éléments du montage (brut à usiner, pions de positionnement, tiges de fixation,etc.), de vérifier que leur implantation réelle (réalisée par l'opérateur) est conforme au modèle 3D numérique de montage souhaité (modèle CAO), afin de prévenir tout risque de collision avec l'outil d'usinage.
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Wang, Chyi-Cheng, and 王麒程. "Edge detection of noisy blurred image using fuzzy-edge-operator." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/20397846252447662560.

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Анотація:
碩士
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.
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10

(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.

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Анотація:
In this thesis we investigate how artificial intelligent techniques, namely fuzzy logic and genetic/evolutionary algorithms can be used for digital image processing applications. We demonstrate our techniques with respect to two main research areas: removal of heavy impulse noise from corrupted gray scale images and edge detection in digital images. Very often fuzzy logic systems need to deal with large number of rules. This results in two major design issues: (i) How to formulate the fuzzy knowledge base using human expertise and experience? (ii) How to reduce the high computational power and the high processing times required? In this thesis we use evolutionary algorithms (including coevolutionary algorithms) to learn fuzzy knowledge bases to handle the design issue (i) described above, while using multi-layered and hierarchical fuzzy logic systems to reduce the number of rules and hence the computational overhead involved, thereby addressing issue (ii) stated above. In this research, when fuzzy rules are learnt using evolutionary algorithms, each individual in the evolutionary algorithm is appropriately encoded to uniquely represent the fuzzy knowledge base. The fitness of each individual in the evolutionary algorithm is calculated with respect to a predefined reference. In the case of an algorithm learning to enhance a digital image this reference is often associated with the uncorrupted perfect image. Designing multi-layered and hierarchical fuzzy structures involves breaking down the total number of rules, to be fed into multiple fuzzy layers in the system. This process needs careful consideration in forming the appropriate fuzzy layers as well as deciding the parameters to be input to different layers, so that the desired result is obtained with highest precision using the least computation time. Coevolutionary algorithms are powerful tools that can be used in situations where several factors contributing towards the system performance need to be learnt simultaneously. Here multiple populations consisting of candidate solutions are evolved in parallel and the fitness of individuals in each of the population are evaluated by forming a vector of candidate solutions selected from each population. The artificial intelligence techniques briefly described above will be used in this thesis with application to enhancement and edge detection in digital images.
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Книги з теми "FUZZY EDGE DETECTION"

1

Gonzalez, Claudia I., Patricia Melin, Juan R. Castro, and 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.

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Gonzalez, Claudia I., Oscar Castillo, Patricia Melin, and Juan R. Castro. Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic. Springer International Publishing AG, 2017.

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Частини книг з теми "FUZZY EDGE DETECTION"

1

Marco-Detchart, C., G. Lucca, G. Dimuro, T. Asmus, C. Lopez-Molina, E. Borges, J. A. Rincon, V. Julian, and H. Bustince. "Fuzzy Integrals for Edge Detection." In 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.

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Tizhoosh, Hamid R. "Fast and Robust Fuzzy Edge Detection." In 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.

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3

Ho, Kenneth H. L., and Noboru Ohnishi. "FEDGE — Fuzzy edge detection by Fuzzy Categorization and Classification of edges." In 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.

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Wojcik, Zbigniew M. "Accurate Edge Detection Using Rough Sets." In 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.

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Rubio, Yoshio, Oscar Montiel, and Roberto Sepúlveda. "Cellular Automata Enhanced Quantum Inspired Edge Detection." In 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.

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Becerikli, Yasar, and Tayfun M. Karan. "A New Fuzzy Approach for Edge Detection." In Computational Intelligence and Bioinspired Systems, 943–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494669_116.

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Wang, Gang, and Bernard De Baets. "Edge Detection Based on the Fusion of Multiscale Anisotropic Edge Strength Measurements." In 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.

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Katoch, Rachita, and Rosepreet Kaur Bhogal. "Edge Detection Using Fuzzy Logic (Fuzzy Sobel, Fuzzy Template, and Fuzzy Inference System)." In Advances in Intelligent Systems and Computing, 741–52. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5903-2_76.

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Zhu, Wen, Beiping Hou, Zhegen Zhang, and Kening Zhou. "Study on Wavelet-Based Fuzzy Multiscale Edge Detection Method." In Fuzzy Systems and Knowledge Discovery, 703–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_86.

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Li, Zhe, and Yindi Wang. "Moving Vehicle Detection Combining Edge Detection and Gaussian Mixture Models." In 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.

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Тези доповідей конференцій з теми "FUZZY EDGE DETECTION"

1

Yalcin, Eyup, Hasan Badem, and Mahit Gunes. "CUDA-based hybrid intuitionistic fuzzy edge detection algorithm." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7338008.

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Ling, Wing-Kuen, and Kwong-Shun Tam. "Edge detection using fuzzy switch." In Electronic Imaging 2002, edited by Edward R. Dougherty, Jaakko T. Astola, and Karen O. Egiazarian. SPIE, 2002. http://dx.doi.org/10.1117/12.468016.

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Madasu, Vamsi Krishna, and Shantaram Vasikarla. "Fuzzy Edge Detection in Biometric Systems." In 2007 36th IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2007). IEEE, 2007. http://dx.doi.org/10.1109/aipr.2007.10.

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Anas, Essa. "Edge detection techniques using fuzzy logic." In 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2016. http://dx.doi.org/10.1109/spin.2016.7566682.

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Barskar, Raju, and Gulfishan Firdose Ahmed. "CBIR using fuzzy edge detection mask." In the 2011 International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1947940.1947999.

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Bezdek, James C., Ramachandran Chandrasekhar, and Yianni Attikiouzel. "New fuzzy model for edge detection." In Aerospace/Defense Sensing and Controls, edited by Bruno Bosacchi and James C. Bezdek. SPIE, 1996. http://dx.doi.org/10.1117/12.243250.

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Musilek, Petr, Madan M. Gupta, and Gregory J. Schmidt. "Adaptive fuzzy approach to edge detection." In Photonics East '99, edited by George K. Knopf, Patrick F. Muir, and Peter E. Orban. SPIE, 1999. http://dx.doi.org/10.1117/12.371167.

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Thakkar, Mehul, and Hitesh Shah. "Edge detection techniques using fuzzy thresholding." In 2011 World Congress on Information and Communication Technologies (WICT). IEEE, 2011. http://dx.doi.org/10.1109/wict.2011.6141263.

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Ticala, Cristina, Camelia-M. Pintea, Simone A. Ludwig, Mara Hajdu-Macelaru, Oliviu Matei, and Petrica C. Pop. "Fuzzy Index Evaluating Image Edge Detection obtained with Ant Colony Optimization." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882851.

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Xiao-Ping Zong and Wei-Wei Liu. "Fuzzy edge detection based on wavelets transform." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620897.

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