Edge detection technique by fuzzy logic and Cellular Learning Automata using fuzzy image processing

2013 ◽  
Author(s):  
Dhiraj Kumar Patel ◽  
Sagar A. More
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Edge Detection ◽  
Learning Automata ◽  
Detection Technique ◽  
Cellular Learning Automata ◽  
Fuzzy Image

A Novel Approach for Edge Detection in Images Based on Cellular Learning Automata

2012 ◽  
Vol 2 (4) ◽  
pp. 51-61 ◽  
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Samira Ebrahimi
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Learning Automata ◽  
Edge Detector ◽  
Noise Elimination ◽  
Minimum Period ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Result Show ◽  
Cellular Learning Automata

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

An Algorithmic Approach Based on CMS Edge Detection Technique for the Processing of Digital Images

2020 ◽  
pp. 252-272
Author(s):  
Kalyan Kumar Jena ◽  
Sasmita Mishra ◽  
Sarojananda Mishra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Images ◽  
Color Images ◽  
Experimental Results ◽  
Detection Technique ◽  
Algorithmic Approach ◽  
Current Scenario ◽  
Edge Detection Method

Research in the field of digital image processing (DIP) has increased in the current scenario. Edge detection of digital images is considered as an important area of research in DIP. Detecting edges in different digital images accurately is a challenging work in DIP. Different methods have been introduced by different researchers to detect the edges of images. However, no method works well under all conditions. In this chapter, an edge detection method is proposed to detect the edges of gray scale and color images. This method focuses on the combination of Canny, mathematical morphological, and Sobel (CMS) edge detection operators. The output of the proposed method is produced using matrix laboratory (MATLAB) R2015b and compared with Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Canny, and mathematical morphological edge detection operators. The experimental results show that the proposed method works better as compared to other existing methods in detecting the edges of images.

scholarly journals An Intelligent Automatic Fault Detection Technique Incorporating Image Processing and Fuzzy Logic

2019 ◽  
Vol 1244 ◽  
pp. 012035
Author(s):  
Kulsoom Iftikhar ◽  
Shahzad Anwar ◽  
Muhammad Tahir Khan ◽  
Yasser Abd. Djawad
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Fault Detection ◽  
Detection Technique

scholarly journals Diabetic Retinopathy Diagnosing using Fuzzy Image Processing

2020 ◽  
Vol 8 (6) ◽  
pp. 4210-4215
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Diabetic Retinopathy ◽  
Expert System ◽  
Diagnostic System ◽  
Diabetic Patients ◽  
Input Output ◽  
Fuzzy Image ◽  
Retina Images ◽  
Oct Image

Aim: To design diagnostic expert system using fuzzy image processing for diabetic retinopathy, measures diabetic eye morbidity. Method: From this research paper, diagnosing diabetic retinopathy using fuzzy image processing for diabetic patients. Firstly collection of OCT images of the patient who has diabetic retinopathy. Author’s proposed method finds out the edge detection of the OCT image. Then fuzzy logic is applied on that result of image processing. Design a fuzzy rules and input- output parameter. This method gives accurate diagnosing the diabetic retinopathy from the image of the patient’s retina images. Result: This diagnostic system gives patient’s eye morbidity, vision threatening of the diabetic patients. In the result, edges of the retina images, and from that retinal ruptures, thickness of the proliferative in the retina. From these result, diagnostic of diabetic retinopathy conditions such as PDR, NPDR, and NORMAL, and CSME in the diabetic patients. Conclusion: author has design diagnostic system for endocrinologist and ophthalmology to diagnosed diabetic retinopathy in the patients. From this system doctors don’t need patients for diagnosing purposed.

scholarly journals Fuzzy Logic based Edge Detection Method for Image Processing

2018 ◽  
Vol 8 (3) ◽  
pp. 1863 ◽  
Author(s):  
Abdulrahman Moffaq Alawad ◽  
Farah Diyana Abdul Rahman ◽  
Othman O. Khalifa ◽  
Norun Abdul Malek
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Detection Method ◽  
Detection Task ◽  
Fuzzy Logic Method ◽  
Recognition Systems ◽  
Edge Detection Method ◽  
Depth Curves

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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