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Готові списки джерел за темами / FUZZY EDGE DETECTION / Статті в журналах

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Статті в журналах з теми "FUZZY EDGE DETECTION"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

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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2

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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3

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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4

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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5

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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6

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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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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7

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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8

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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9

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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10

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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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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11

Sun, Shu Liang, Cheng Lian Liu, and Si Sheng Chen. "Edge Detection Based on a Fuzzy Inference System." Applied Mechanics and Materials 121-126 (October 2011): 4436–40. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4436.

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Анотація:

First-order linear ﬁlter is a wide application algorithm to detect edge in digital image. However it dosen’t make good effort to the image where contrast varies much, or luminance takes on non-uniform. In this paper, a fuzzy inference system (FIS) is made up and used to detect edges. The experiment shows that FIS is much better in edge detection when the image with high contrast variation than with the linear Sobel operator. The FIS is also more precise in edge detection than Sobel operator.

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12

Saxena, Kumud. "Wavelets assisted fuzzy edge refinement." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 2 (December 5, 2014): 5409–18. http://dx.doi.org/10.24297/ijct.v14i2.2063.

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Анотація:

Image enhancement is a crucial pre-processing step to be performed for various applications where object recognition, identification, verification is required. Among various image enhancement methods, edge enhancement has taken its importance as it is widely used for understanding features in an image. Several types of edge detectors are available for certain types of edges. If edges are enhanced and clear, the reliability for feature extraction increases. The Quality of edge detection can be measured from several criteria objectively. In this paper, a novel algorithm for edge enhancement has been proposed for multiple types of images. The features can be extracted clearly by using this method. For comparison purpose Roberts, Sobel, Prewitt, Canny, and Log edge operators are used and their results are displayed. Experimental results demonstrate the effectiveness of the proposed approach.

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13

KAUR, AMANDEEP, and CHANDAN SINGH. "A HYBRID EDGE DETECTOR USING FUZZY LOGIC AND MATHEMATICAL MORPHOLOGY." International Journal of Image and Graphics 10, no. 02 (April 2010): 251–72. http://dx.doi.org/10.1142/s0219467810003767.

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Анотація:

Edge detection concerns the localization of significant variations of the grey level image. Detection of edges in an image is very important step for a complete image understanding system. This paper proposes a new approach to edge detection which adopts fuzzy reasoning to detect edges and mathematical morphology for edge thinning. The results achieved by this algorithm are comparable to the Canny approach. In Canny edge detector we may require many runs using different combinations of the three parameters (two threshold and one sigma values) whereas in the proposed technique only one parameter needs to be set by the user coarsely to get the same results, also the computation load of Canny is higher than the proposed approach.

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14

SuQun Cao, QuanYin Zhu, BoKui Li, Rong Gao, and HaiFei Zhai. "Fuzzy Fisher Criterion based Edge Detection." International Journal of Digital Content Technology and its Applications 5, no. 8 (August 31, 2011): 381–88. http://dx.doi.org/10.4156/jdcta.vol5.issue8.44.

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15

Verma, O. P., Veni Jain, and Rajni Gumber. "Simple Fuzzy Rule Based Edge Detection." Journal of Information Processing Systems 9, no. 4 (December 31, 2013): 575–91. http://dx.doi.org/10.3745/jips.2013.9.4.575.

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16

Anita Shanthi, S., and R. Valarmathi. "Edge detection on fuzzy near sets." Materials Today: Proceedings 51 (2022): 2504–11. http://dx.doi.org/10.1016/j.matpr.2021.12.120.

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17

Sim, Kwee-Bo. "Fuzzy Classifier System for Edge Detection." International Journal of Fuzzy Logic and Intelligent Systems 3, no. 1 (June 1, 2003): 52–57. http://dx.doi.org/10.5391/ijfis.2003.3.1.052.

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18

Jacquey, Florence, Frédéric Comby, and Olivier Strauss. "Fuzzy edge detection for omnidirectional images." Fuzzy Sets and Systems 159, no. 15 (August 2008): 1991–2010. http://dx.doi.org/10.1016/j.fss.2008.02.022.

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19

Mehran, Pejman, and Kudret Demirli. "Fuzzy automated visual broken edge detection." International Journal of Advanced Manufacturing Technology 67, no. 5-8 (October 26, 2012): 1113–25. http://dx.doi.org/10.1007/s00170-012-4552-y.

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20

Liu, Xi Wen, and Chao Ying Liu. "Paper Currency CIS Image Fuzzy Enhancement and Boundary Detection." Applied Mechanics and Materials 651-653 (September 2014): 2356–61. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2356.

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Анотація:

According to the features of paper currency CIS image, the fuzzy set theory is initial used to improve the contrast of banknote target and background, paper money’s edge pixels are obtained by scanning, and then the sub-pixel edges are fitted by least square method; in the fitting process, the error threshold method is applied for eliminating noisy edge points, then slope correction of paper money is carried out according to the angle of sub-pixel edge line and the note area is extracted, finally, fuzzy enhancement is used for the extracted image again. Experiments show that, this method has good versatility, high accuracy and strong anti-interference ability, it can even extract the paper currency area effectively in the edge deterioration and unfilled corner condition, and the enhanced image is in favor of subsequent paper currency recognition.

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21

Bobyr, Maksim, Alexander Arkhipov, Sergey Gorbachev, Jinde Cao, and Siddhartha Bhattacharyya. "Fuzzy Logic Approaches in the Task of Object Edge Detection." Informatics and Automation 21, no. 2 (February 27, 2022): 376–404. http://dx.doi.org/10.15622/ia.21.2.6.

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Анотація:

The task of reducing the computational complexity of contour detection in images is considered in the article. The solution to the task is achieved by modifying the Canny detector and reducing the number of passes through the original image. In the first case, two passes are excluded when determining the adjacency of the central pixel with eight adjacent ones in a frame of size 3х3. In the second case, three passes are excluded, two as in the first case and the third one necessary to determine the angle of gradient direction. This passage is provided by a combination of fuzzy rules. The goal of the work is to increase the performance of computational operations in the process of detecting the edges of objects by reducing the number of passes through the original image. The process of edge detection is carried out by some computational operations of the Canny detector with the replacement of the most complex procedures. In the proposed methods, fuzzification of eight input variables is carried out after determining the gradient and the angle of its direction. The input variables are the gradient difference between the central and adjacent cells in a frame of size 3х3. Then a base of fuzzy rules is built. In the first method, four fuzzy rules and one pass are excluded depending on the angle of gradient direction. In the second method, sixteen fuzzy rules themselves set the angle of the gradient direction, while eliminating two passes along the image. The gradient difference between the central cell and adjacent cells makes it possible to take into account the shape of the gradient distribution. Then, based on the center of gravity method, the resulting variable is defuzzified. Further use of fuzzy a-cut makes it possible to binarize the resulting image with the selection of object edges on it. The presented experimental results showed that the noise level depends on the value of the a-cut and the parameters of the labels of the trapezoidal membership functions. The software was developed to evaluate fuzzy edge detection methods. The limitation of the two methods is the use of piecewise-linear membership functions. Experimental studies of the performance of the proposed edge detection approaches have shown that the time of the first fuzzy method is 18% faster compared to the Canny detector and 2% faster than the second fuzzy method. However, during the visual assessment, it was found that the second fuzzy method better determines the edges of objects.

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22

Selvathi, D., Henry Selvaraj, and J. Dharani. "FPGA Implementation of Fuzzy Inference System Based Edge Detection Algorithm." International Journal of Computational Intelligence and Applications 14, no. 02 (June 2015): 1550009. http://dx.doi.org/10.1142/s1469026815500091.

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Edge detection is a very important area in the field of Computer Vision. Edge detectors behave very poorly, their behavior may fall within tolerance in specific situations and have difficulty in adapting to different situations. Human vision is inherently a multiscale phenomenon and is sensitive to orientation and elongation. This work proposes the hardware implementation of efficient fuzzy logic based algorithm, which is used to detect the edges of an image without determining the threshold value. Edge detection in software is not suited for strong real-time applications. This problem is resolved by using hardware implementation on field programmable gate arrays (FPGAs). Fuzzy inference system is developed with four input pixel containing two fuzzy sets (FSs) one for white and another for black and one output pixel containing three FSs for white, black and edge. Fuzzy if-then rules are used to modify the membership functions. Finally, Mamdanidefuzzifier method is used to form the final edge image. For comparison, the same work was implemented using sobel operator. The hardware part is developed by using Verilog language. The FPGA implementation is targeted on Virtex5 Starter kit (xc5vlx50tff1136-1) and Virtex7 starter kit (xc7vx485tffq1157-1) using the updated Xilinx PlanAhead within the ISE 13.4 development suite. The edge thickness can be changed easily by adding new rules or changing output parameters. That is, rule-based approach has flexible structure that can be easily adapted to any time or anywhere and the new fuzzy approach produces better result than sobel operator. Experimental results show the ability and high performance of the proposed algorithm.

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23

Guo, Yan Ying, and Zhi Gang Liu. "Airport Runway Debris Edge Detection Algorithm." Advanced Materials Research 1065-1069 (December 2014): 671–74. http://dx.doi.org/10.4028/www.scientific.net/amr.1065-1069.671.

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Airport runway debris detection decides security of plane and passengers. The relative merits of the various types of sensor and the effects of adverse environmental conditions on their performance, a novel fuzzy weighted morphology algorithm of detection about runway debris is approved. The experimental results show that the proposed algorithm, compared with a few classic algorithms of edge detection, which can achieves the image edge precisely and reduce noises, better retain the image details, and adaptively detect the complete and successive edges.

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24

Wang, Dingxian. "Edge Detection technique based on HDR image quality assessment." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012029. http://dx.doi.org/10.1088/1742-6596/2078/1/012029.

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Abstract Image edge detection is one of the major study aspects in current computer image processing field. The quality of the input images is uneven, some have large fuzzy areas, some are underexposed, and the edges of objects in the images are difficult to detect, and the application scenarios of image edge detection are limited. In the view of the above problems, this paper has proposed that by applying High Dynamic Range (HDR) image quality assessment technology, combining multiple images with different exposures into one HDR image with detailed edge information, This technology effectively solved problem of low edge information richness, improved the effectiveness of edge detection algorithms, and contributed to the development of edge detection technology.

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25

Alawad, Abdulrahman Moffaq, Farah Diyana Abdul Rahman, Othman O. Khalifa, and Norun Abdul Malek. "Fuzzy Logic based Edge Detection Method for Image Processing." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (June 1, 2018): 1863. http://dx.doi.org/10.11591/ijece.v8i3.pp1863-1869.

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Анотація:

Edge detection is the first step in image recognition systems in a digital image processing. An effective way to resolve many information from an image such depth, curves and its surface is by analyzing its edges, because that can elucidate these characteristic when color, texture, shade or light changes slightly. Thiscan lead to misconception image or vision as it based on faulty method. This work presentsa new fuzzy logic method with an implemention. The objective of this method is to improve the edge detection task. The results are comparable to similar techniques in particular for medical images because it does not take the uncertain part into its account.

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26

Ma, Li, Li Shang, Long Zhang, and Wei Shi Shao. "An Effective Image Edge Detection Algorithm – Fuzzy Box-Counting." Applied Mechanics and Materials 543-547 (March 2014): 2711–15. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2711.

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Edge detection plays an important role in computer vision and image processing. Fractal and Fuzzy theory show significant effect in the edge detection and have attracted much attention. Compared with traditional edge detection methods, this paper proposes a Fuzzy Box-counting Dimension Method (FBDM). This algorithm introduces the pre-judging mechanism to improve the speed of image segmentation, and the self-adaptive dimension threshold and the voting mechanism under multi-windows to improve the accuracy of the determination of edge points. Finally, closest principle is used to clear edge and reduce noise. Experimental results show FBDM can improve the precision of image edge detection effectively without pretreatment, and it has a very superior de-noising performance.

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27

Law, T., H. Itoh, and H. Seki. "Image filtering, edge detection, and edge tracing using fuzzy reasoning." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 5 (May 1996): 481–91. http://dx.doi.org/10.1109/34.494638.

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28

Ighoyota Ben, Ajenaghughrure, Ogini Nicholas.O., and Onyekweli Charles O. "Optimum Fuzzy based Image Edge Detection Algorithm." International Journal of Image, Graphics and Signal Processing 9, no. 4 (April 8, 2017): 44–55. http://dx.doi.org/10.5815/ijigsp.2017.04.06.

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29

Levrat, E., J. Bremont, B. Heit, and G. Dubois. "Edge Detection Using the Fuzzy Sets Theory." IFAC Proceedings Volumes 22, no. 6 (July 1989): 401–4. http://dx.doi.org/10.1016/s1474-6670(17)54408-0.

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30

Moghaddamzadeh, A., D. Goldman, and N. Bourbakis. "A Fuzzy-Like Approach for Smoothing and Edge Detection in Color Images." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 06 (September 1998): 801–16. http://dx.doi.org/10.1142/s0218001498000440.

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Анотація:

Edge detection is one of the most important image processing steps towards image understanding. It is desired that edges be continuous and that the resultant regions or segments be completely isolated from their neighbors. Initially, images must first be smoothed to remove noise. In this paper, a novel fuzzy-like smoother algorithm is presented which removes camera noise and enhances edge contrast. The edge detection algorithm, which is applied on the smoothed image, is then presented. In this algorithm normalized hue in HSI space and color contrast in RGB space are combined using an aggregate operator. Pixels considered to be at least "nearly" locally maximum (defined within) are then found for all edge directions and the results are combined.

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31

Sadjadi, Ebrahim Navid, Danial Sadrian Zadeh, Behzad Moshiri, Jesús García Herrero, Jose Manuel Molina López, and Roemi Fernández. "Application of Smooth Fuzzy Model in Image Denoising and Edge Detection." Mathematics 10, no. 14 (July 11, 2022): 2421. http://dx.doi.org/10.3390/math10142421.

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In this paper, the bounded variation property of fuzzy models with smooth compositions have been studied, and they have been compared with the standard fuzzy composition (e.g., min–max). Moreover, the contribution of the bounded variation of the smooth fuzzy model for the noise removal and edge preservation of the digital images has been investigated. Different simulations on the test images have been employed to verify the results. The performance index related to the detected edges of the smooth fuzzy models in the presence of both Gaussian and Impulse (also known as salt-and-pepper noise) noises of different densities has been found to be higher than the standard well-known fuzzy models (e.g., min–max composition), which demonstrates the efficiency of smooth compositions in comparison to the standard composition.

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32

Yang, Chang Niu, and Xing Bo Sun. "Research on Broken Filament Detection Method of Silk Product." Applied Mechanics and Materials 716-717 (December 2014): 848–50. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.848.

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Анотація:

In this paper, we put forward a kind of adaptive edge detection algorithm of Gabor filter for silk product broken filament image. Use different directions’ Gabor filter to respectively get the broken filament image edge information. Using the method proposed in this paper to fuse the edges adaptively obtained from different directions Gabor filter, we obtain ideal image edges, and effectively eliminate the noise, also enhance the fuzzy edges at the same time. Experimental results show that the algorithm for silk products processing is effective, and the broken filament detected is clear.

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33

Martínez, Gabriela E., Claudia I. Gonzalez, Olivia Mendoza, and Patricia Melin. "General Type-2 Fuzzy Sugeno Integral for Edge Detection." Journal of Imaging 5, no. 8 (August 16, 2019): 71. http://dx.doi.org/10.3390/jimaging5080071.

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Анотація:

A type-2 fuzzy edge detection method is presented in this paper. The general process consists of first obtaining the image gradients in the four directions—horizontal, vertical, and the two diagonals—and this technique is known as the morphological gradient. After that, the general type-2 fuzzy Sugeno integral (GT2 FSI) is used to integrate the four image gradients. In this second step, the GT2 FSI establishes criteria to determine at which level the obtained image gradient belongs to an edge during the process; this is calculated assigning different general type-2 fuzzy densities, and these fuzzy gradients are aggregated using the meet and join operators. The gradient integration using the GT2 FSI provides a methodology for achieving more robust edge detection, even more if we are working with blurry images. The experimental evaluations are performed on synthetic and real images, and the accuracy is quantified using Pratt’s Figure of Merit. The results values demonstrate that the proposed edge detection method outperforms other existing algorithms.

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34

Torres, Cesar, Claudia I. Gonzalez, and Gabriela E. Martinez. "Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification." Sensors 22, no. 15 (August 7, 2022): 5892. http://dx.doi.org/10.3390/s22155892.

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Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination of fuzzy image edge-detection and the usage of a convolutional neural network for a computer vision system to classify guitar types according to their body model. The focus of this investigation is to compare the effects of performing image-preprocessing techniques on raw data (non-normalized images) with different fuzzy edge-detection methods, specifically fuzzy Sobel, fuzzy Prewitt, and fuzzy morphological gradient, before feeding the images into a convolutional neural network to perform a classification task. We propose and compare two convolutional neural network architectures to solve the task. Fuzzy edge-detection techniques are compared against their classical counterparts (Sobel, Prewitt, and morphological gradient edge-detection) and with grayscale and color images in the RGB color space. The fuzzy preprocessing methodologies highlight the most essential features of each image, achieving favorable results when compared to the classical preprocessing methodologies and against a pre-trained model with both proposed models, as well as achieving a reduction in training times of more than 20% compared to RGB images.

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35

Yujie, Wang. "A comparison of canny edge detection algorithm and edge detection algorithm based on fuzzy logic." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 780–85. http://dx.doi.org/10.54254/2755-2721/4/2023422.

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Анотація:

Edge detection techniques in digital image processing are a very valuable area of research. In recent years, there are two more popular edge detection algorithms, one is Canny edge detection algorithm,and the other is edge detection algorithm based on Fuzzy Logic. Both algorithms have been used in various fields. The main work of this paper is to do a brief introduction of two algorithms and compare the results from them.

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36

LAW, Todd, Hidenori ITOH, and Hirohis SEKI. "Fuzzy Reasoning Techniques for Image Filtering, Edge Detection, and Edge Tracing." Journal of Japan Society for Fuzzy Theory and Systems 7, no. 4 (1995): 849–61. http://dx.doi.org/10.3156/jfuzzy.7.4_849.

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37

Han, Fangfang, Bin Liu, Junchao Zhu, and Baofeng Zhang. "Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment." Sensors 19, no. 2 (January 16, 2019): 343. http://dx.doi.org/10.3390/s19020343.

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Анотація:

For some measurement and detection applications based on video (sequence images), if the exposure time of camera is not suitable with the motion speed of the photographed target, fuzzy edges will be produced in the image, and some poor lighting condition will aggravate this edge blur phenomena. Especially, the existence of noise in industrial field environment makes the extraction of fuzzy edges become a more difficult problem when analyzing the posture of a high-speed moving target. Because noise and edge are always both the kind of high-frequency information, it is difficult to make trade-offs only by frequency bands. In this paper, a noise-tolerant edge detection method based on the correlation relationship between layers of wavelet transform coefficients is proposed. The goal of the paper is not to recover a clean image from a noisy observation, but to make a trade-off judgment for noise and edge signal directly according to the characteristics of wavelet transform coefficients, to realize the extraction of edge information from a noisy image directly. According to the wavelet coefficients tree and the Lipschitz exponent property of noise, the idea of neural network activation function is adopted to design the activation judgment method of wavelet coefficients. Then the significant wavelet coefficients can be retained. At the same time, the non-significant coefficients were removed according to the method of judgment of isolated coefficients. On the other hand, based on the design of Daubechies orthogonal compactly-supported wavelet filter, rational coefficients wavelet filters can be designed by increasing free variables. By reducing the vanishing moments of wavelet filters, more high-frequency information can be retained in the wavelet transform fields, which is benefit to the application of edge detection. For a noisy image of high-speed moving targets with fuzzy edges, by using the length 8-4 rational coefficients biorthogonal wavelet filters and the algorithm proposed in this paper, edge information could be detected clearly. Results of multiple groups of comparative experiments have shown that the edge detection effect of the proposed algorithm in this paper has the obvious superiority.

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38

Jeyaraman, Gowri, and Janakiraman Subbiah. "An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 1 (October 1, 2017): 59. http://dx.doi.org/10.11591/ijeecs.v8.i1.pp59-68.

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Анотація:

<p>Edge exposure or edge detection is an important and classical study of the medical field and computer vision. Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for CFCM is fruitful. According to the calculation, a result acquired by using CFCM clustering function divides the image into four clusters in common. The proposed method is evidently robust into the modification of fuzzy c-means and canny algorithm. The convergence of this algorithm is very speedy compare to the entire edge detection algorithms. The consequences of this proposed algorithm make enhanced edge detection and better result than any other traditional image edge detection techniques.</p>

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39

Li, Dong Xing, Liang Geng, Qin Jun Du, Han Ren, Ai Jun Li, and Shi Tang. "A Novel Extracting Algorithm of the Low Gray Fuzzy Edges for Infrared Images." Advanced Materials Research 1037 (October 2014): 411–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.411.

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The fuzzy edge detection algorithm proposed by Pal-King has some disadvantages for extracting the low gray level edge information for the infrared images, such as high computation complexity, low threshold segmentation inaccuracy and the leakage edge information. For overcoming the disadvantages, the improved image fuzzy edge detection algorithm is proposed in this paper. First, redefining membership function to simplify computation complexity, the new conversion function enable the function transform interval is [0, 1], thus the value of the low gray level edge is not to be set to 0. Second, Ostu's algorithm is used in the selection of segmentation threshold named as transit point. The traditional threshold value is improved in order to make the segmentation accurate. The experimental results show that the lower gray infrared image edge information is preserved via proposed algorithm in this paper. The detecting results are more accurate. The run time is decreased obviously than the traditional Pal - king algorithm.

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40

Ahmad Zaki, Muhammad Zainul Arif, Mohd Hanafi Mat Som, Haniza Yazid, Khairul Salleh Basaruddin, Shafriza Nisha Basah, and Megat Syahirul Amin Megat Ali. "A Review on Edge Detection on Osteogenesis Imperfecta (OI) Image using Fuzzy Logic." Journal of Physics: Conference Series 2071, no. 1 (October 1, 2021): 012040. http://dx.doi.org/10.1088/1742-6596/2071/1/012040.

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Анотація:

Abstract Osteogenesis Imperfecta (OI) is a bone disorder that causes bone to be brittle and easy to fracture. The patient suffered from this disease will have poor quality of life. Simulation on the bone fracture risk would help medical doctors to make decision in their diagnosis. Detection of edges from the OI images is very important as it helps radiologist to segmentize cortical and cancellous bone to make a good 3D bone model for analysis. The purpose of this paper is to review the fundamentals of fuzzy logic in edge detection of OI bone as it is yet to be implemented. Several fuzzy logic concepts are reviewed by previous studies which include fuzziness, membership functions and fuzzy sets regarding digital images. The OI images were produced by modalities such as Magnetic Resonance Imaging (MRI), Ultrasound, or Computed Tomography (CT). In summary, researchers from the reviewed papers concluded that fuzzy logic can be implemented to detect edges in noisy clinical images.

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41

Feng, Lin, Jian Wang, and Chao Ding. "Image Edge Detection Algorithm Based on Fuzzy Radial Basis Neural Network." Advances in Mathematical Physics 2021 (December 21, 2021): 1–9. http://dx.doi.org/10.1155/2021/4405657.

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Анотація:

Digital image processing technology is widely used in production and life, and digital images play a pivotal role in the ever-changing technological development. Noise can affect the expression of image information. The edge is the reflection of the main structure and contour of the image, and it is also the direct interpretation of image understanding and the basis for further segmentation and recognition. Therefore, suppressing noise and improving the accuracy of edge detection are important aspects of image processing. To address these issues, this paper presents a new detection algorithm combined with information fusion based on the existing image edge detection techniques, and the algorithm is studied from two aspects of fuzzy radial basis fusion discrimination, in terms of preprocessing algorithm, comparing the denoising effect of mean and median filters with different template sizes on paper images with added noise, and selecting the improved median filter denoising, comparing different operator edge detection. The effect of image edge detection contour is finally selected as the 3 ∗ 3 Sobel operator for edge detection; the binarized image edge detection contour information is found as the minimum outer rectangle and labeled, and then, the original paper image is scanned line by line to segment the target image edge region. The image edge detection algorithm based on fuzzy radial basis fuser can not only speed up the image preprocessing, meet the real-time detection, and reduce the amount of data processed by the upper computer but also can accurately identify five image edge problems including folds and cracks, which has good application prospects.

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42

Singh, SanjayKumar, MadhavJ Nigam, and Kirat Pal. "Fuzzy Edge Detection Based on Maximum Entropy Thresholding." IETE Journal of Research 57, no. 4 (2011): 325. http://dx.doi.org/10.4103/0377-2063.86281.

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43

Jingying Ren. "Research on the Improved Fuzzy Edge Detection Algorithm." International Journal of Digital Content Technology and its Applications 7, no. 3 (February 15, 2013): 403–9. http://dx.doi.org/10.4156/jdcta.vol7.issue3.51.

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44

Wang, Shuqiang, Shuo Liang, and Fei Peng. "Image edge detection algorithm based on fuzzy set." Journal of Intelligent & Fuzzy Systems 38, no. 4 (April 30, 2020): 3557–66. http://dx.doi.org/10.3233/jifs-179578.

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45

Russo, F. "Edge detection in noisy images using fuzzy reasoning." IEEE Transactions on Instrumentation and Measurement 47, no. 5 (1998): 1102–5. http://dx.doi.org/10.1109/19.746564.

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46

Seng, N. H., Z. Samad, and N. M. Nor. "A 3-Pixel Fuzzy Mask for Edge Detection." IOP Conference Series: Materials Science and Engineering 530 (July 15, 2019): 012023. http://dx.doi.org/10.1088/1757-899x/530/1/012023.

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47

Boaventura, I. A. G., and A. Gonzaga. "Edge detection in digital images using fuzzy numbers." International Journal of Innovative Computing and Applications 2, no. 1 (2009): 1. http://dx.doi.org/10.1504/ijica.2009.027992.

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48

Dhivya, R., and R. Prakash. "Edge detection of satellite image using fuzzy logic." Cluster Computing 22, S5 (December 22, 2017): 11891–98. http://dx.doi.org/10.1007/s10586-017-1508-x.

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49

Suryakant, Suryakant, and Renu Dhir. "Novel Adaptive Neuro-Fuzzy based Edge Detection Technique." International Journal of Computer Applications 49, no. 4 (July 28, 2012): 23–27. http://dx.doi.org/10.5120/7616-0666.

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50

Vertan, C., A. Stoica, and V. Buzuloiu. "Multiscale Color Edge Detection by a Fuzzy Approach." Conference on Colour in Graphics, Imaging, and Vision 1, no. 1 (January 1, 2002): 503–6. http://dx.doi.org/10.2352/cgiv.2002.1.1.art00105.

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