Fuzzy Set Theory in Image Processing

2005 ◽  
pp. 209-226 ◽  
Keyword(s):  
Image Processing ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory

A NOVEL APPROACH TO IMAGE THRESHOLDING BASED ON 2D HOMOGENEITY HISTOGRAM AND MAXIMUM FUZZY ENTROPY

2011 ◽  
Vol 07 (01) ◽  
pp. 105-133 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Entropy ◽  
Image Thresholding ◽  
Novel Approach ◽  
Entropy Principle

Image thresholding is an important topic for image processing, pattern recognition and computer vision. Fuzzy set theory has been successfully applied to many areas, and it is generally believed that image processing bears some fuzziness in nature. In this paper, we employ the newly proposed 2D homogeneity histogram (homogram) and the maximum fuzzy entropy principle to perform thresholding. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively. Especially, it not only can process "clean" images, but also can process images with different kinds of noises and images with multiple kinds of noise well without knowing the type of the noise, which is the most difficult task for image thresholding. It will be useful for applications in computer vision and image processing.

scholarly journals Fuzzy logic and fuzzy set theory based edge detection algorithm

2015 ◽  
Vol 12 (1) ◽  
pp. 109-116 ◽  
Author(s):  
Nebojsa Peric
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Edge Detection ◽  
Set Theory ◽  
Fuzzy Set ◽  
Video Processing ◽  
Fuzzy Set Theory ◽  
Intelligent System ◽  
Detection Algorithm ◽  
Edge Classification

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.

Fuzzy Approaches and Analysis in Image Processing

2017 ◽  
pp. 1-31 ◽  
Author(s):  
Ezhilmaran D ◽  
Adhiyaman M
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Membership Functions ◽  
Significant Development ◽  
Diverse Field ◽  
Self Study

Fuzzy set theory originates to a great extent of interest among the researchers in past decades. It is a key tool to handle the imperfect of information in the diverse field. Typically, it plays a very important role in image processing and found the significant development in many active areas such as pattern recognition, neural network, medical imaging, etc. The use of fuzzy set theory is to tackle uncertainty in the form of membership functions when there is an image gray levels or information is lost. This chapter concerns the preliminaries of fuzzy, intuitionistic fuzzy, type-2 fuzzy and intuitionistic type-2 fuzzy set theory and its application in the fingerprint image; furthermore, the contrast enhancement and edge detection are carried out for that with the assistance of fuzzy set theory. It is useful to the students who want to self-study. This chapter composed just to address that issue.

Fuzzy Approaches and Analysis in Image Processing

2018 ◽  
pp. 511-542
Author(s):  
Ezhilmaran D ◽  
Adhiyaman M
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Membership Functions ◽  
Significant Development ◽  
Diverse Field ◽  
Self Study

Fuzzy set theory originates to a great extent of interest among the researchers in past decades. It is a key tool to handle the imperfect of information in the diverse field. Typically, it plays a very important role in image processing and found the significant development in many active areas such as pattern recognition, neural network, medical imaging, etc. The use of fuzzy set theory is to tackle uncertainty in the form of membership functions when there is an image gray levels or information is lost. This chapter concerns the preliminaries of fuzzy, intuitionistic fuzzy, type-2 fuzzy and intuitionistic type-2 fuzzy set theory and its application in the fingerprint image; furthermore, the contrast enhancement and edge detection are carried out for that with the assistance of fuzzy set theory. It is useful to the students who want to self-study. This chapter composed just to address that issue.

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