Microscopic Image Thresholding using Restricted Equivalence Function based Fuzzy Entropy Minimization and Bat Algorithm

2018 ◽  
Author(s):  
Hiranmoy Roy ◽  
Soumyadip Dhar ◽  
Poushali Choudhury ◽  
Abhirup Biswas ◽  
Abhrak Chatterjee
Keyword(s):  
Bat Algorithm ◽  
Fuzzy Entropy ◽  
Microscopic Image ◽  
Image Thresholding ◽  
Entropy Minimization

Thresholding Selection Based on Fuzzy Entropy and Bee Colony Algorithm for Image Segmentation

2013 ◽  
pp. 165-183
Author(s):  
Yonghao Xiao ◽  
Weiyu Yu ◽  
Jing Tian
Keyword(s):  
Image Segmentation ◽  
Fuzzy Entropy ◽  
Single Parameter ◽  
Entropy Function ◽  
Experimental Results ◽  
Superior Performance ◽  
Image Thresholding ◽  
Minimum Function ◽  
Minimum Value ◽  
Bee Colony

Image thresholding segmentation based on Bee Colony Algorithm (BCA) and fuzzy entropy is presented in this chapter. The fuzzy entropy function is simplified with single parameter. The BCA is applied to search the minimum value of the fuzzy entropy function. According to the minimum function value, the optimal image threshold is obtained. Experimental results are provided to demonstrate the superior performance of the proposed approach.

scholarly journals Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation

2018 ◽  
Vol 57 (3) ◽  
pp. 1643-1655 ◽  
Author(s):  
M.S.R. Naidu ◽  
P. Rajesh Kumar ◽  
K. Chiranjeevi
Keyword(s):  
Image Segmentation ◽  
Fuzzy Entropy ◽  
Image Thresholding

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 A Multilevel Image Thresholding Based on Hybrid Salp Swarm Algorithm and Fuzzy Entropy

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 181405-181422 ◽  
Author(s):  
Husein S. Naji Alwerfali ◽  
Mohamed Abd Elaziz ◽  
Mohammed A. A. Al-Qaness ◽  
Aaqif Afzaal Abbasi ◽  
Songfeng Lu ◽  
...  
Keyword(s):  
Fuzzy Entropy ◽  
Salp Swarm Algorithm ◽  
Image Thresholding ◽  
Swarm Algorithm
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