Genetic Algorithm Image Segmentation Method Based on Membrane Computing

2021 ◽  
pp. 220-225
Author(s):  
Yongxing Lin ◽  
Quan Wen
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Membrane Computing ◽  
Segmentation Method

Study on Otsu Threshold Method for Image Segmentation Based on Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 925-928 ◽  
Author(s):  
Zhi Bo Xu ◽  
Pei Jiang Chen ◽  
Shi Li Yan ◽  
Tai Hua Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Image Process ◽  
Segmentation Method ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
Maximum Variance ◽  
Segmentation Image ◽  
Selection Of

Threshold segmentation method was widely applied in image process and the selection of threshold affected the final results of image segmentation to a large extent. In order to improve the accuracy and the calculation speed of image segmentation, an Otsu threshold segmentation method based on genetic algorithm was offered. According to the threshold and the gray scale values of pixels, the pixels were divided into two categories, and then the genetic algorithm was used to find the maximum variance between clusters and obtain the optimal threshold of segmentation image. The experimental results show that this method can be used to segment the image effectively, which make the basis for image processing and analysis in the next step.

A two-dimensional image segmentation method based on genetic algorithm and entropy

Optik ◽  
2017 ◽  
Vol 131 ◽  
pp. 414-422 ◽  
Author(s):  
S. Abdel-Khalek ◽  
Anis Ben Ishak ◽  
Osama A. Omer ◽  
A.-S.F. Obada
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Dimensional Image ◽  
Two Dimensional Image

Implementation and Evaluation of the Image Segmentation Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1314-1317
Author(s):  
Li Jun Chen ◽  
Yong Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
And Performance ◽  
Standard Genetic Algorithm ◽  
Better Than

In order to achieve better image segmentation and evaluate the segmentation algorithm, a segmentation method based on 2-D maximum entropy and improved genetic algorithm is proposed in this paper, and the ultimate measurement accuracy criterion is adopted to evaluate the performance of the algorithm. The experimental results and the evaluation results show that segmentation results and performance of the proposed algorithm are both better than the segmentation method based on 2-D maximum entropy method and the standard genetic algorithm. The segmentation of the proposed algorithm is complete and spends less time; it is an effective method for image segmentation.

Test Methods Research of Cracks in Ceramics Based on Genetic Algorithm

2014 ◽  
Vol 977 ◽  
pp. 25-29
Author(s):  
Bing Xiang Liu ◽  
Feng Qin Wang ◽  
Xu Dong Wu ◽  
Ying Xi Li
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Optimization Problem ◽  
Fitness Function ◽  
Recognition Rate ◽  
Test Methods ◽  
Segmentation Method ◽  
Maximum Variance ◽  
Best Value ◽  
Crossover And Mutation

In order to improve the reliability of cracks in ceramics test, this paper puts forward a target adaptive segmentation method used by genetic algorithm and maximum-variance algorithm in all classes. This proposed method makes some appropriate improvements about crossover and mutation in genetic algorithm. Besides, the fitness function draws merits of maximum-variance algorithm in all classes and turns the best value in image segmentation into corresponding optimization problem. The simulation results of experiment shows the method proposed shortens the searching time and strengthens anti-noise property during image segmentation and improves recognition rate of cracks in ceramics.

Image Segmentation Method Based on Improved Genetic Algorithm and Fuzzy Clustering

2010 ◽  
Vol 143-144 ◽  
pp. 379-383 ◽  
Author(s):  
Jing Zhang ◽  
Xiang Zhang ◽  
Jie Zhang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Cluster Center ◽  
Adaptive Genetic Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
Advantages And Disadvantages ◽  
Initial Cluster ◽  
Important Means

Image segmentation is an important means of the implementation of image analysis. The existing segmentation methods have their own advantages and disadvantages in segmentation time and segmentation effect. Image segmentation based on fuzzy clustering and genetic algorithm is studied. An adaptive genetic algorithm is improved, the crossover rate and mutation rate are optimized, and a new adaptive operator is adopted to achieve a non-linear adaptive adjustment. A new combined image segmentation means is presented, in which the genetic algorithm is adopted to optimize the initial cluster center and then the fuzzy clustering is used for image segmentation. The practice proves that this image segmentation method and algorithm is superior to the traditional one, which improves the segmentation performance and the segmentation effect.

Image Segmentation Based on Improved Adaptive Genetic Algorithm

2011 ◽  
Vol 464 ◽  
pp. 151-154 ◽  
Author(s):  
Zu Jue Chen ◽  
Xian Xiang Fu ◽  
Xiang Zhou
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Precision Agriculture ◽  
Automatic Segmentation ◽  
Computational Method ◽  
Adaptive Genetic Algorithm ◽  
Segmentation Method ◽  
Crossover Probability ◽  
Computer Vision Technology ◽  
Salt And Pepper

Using computer vision technology to accurately identify weeds and crops, positioning weed and spraying of weedcide has become a hotspot of precision agriculture. To determine the optimal threshold in image automatic segmentation and solve one-dimensional histogram without obvious peak and valley distribution, image segmentation method based on two-dimensional histogram and Improved Adaptive Genetic Algorithm is proposed. In the method, the genetic algorithm carries on the global optimization to get the threshold rapidly, and the computational method for crossover probability and mutation probability of the Adaptive Genetic Algorithm is improved. The Improved Adaptive Genetic Algorithm can preserve the multifamily of population and the astringency of the algorithm, and can overcome the problems of poor astringency and premature occurrence in Simple Genetic Algorithm. The result shows that the proposed approach greatly enhances the speed of thresholding and has better immunity to Salt and Pepper Noise.

Image Segmentation Method Based on Fisher Criterion and Genetic Algorithm

2011 ◽  
Vol 474-476 ◽  
pp. 928-932
Author(s):  
Xian Xiang Fu ◽  
Zu Jue Chen ◽  
Yong Fu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Precision Agriculture ◽  
Automatic Segmentation ◽  
Adaptive Genetic Algorithm ◽  
Salt And Pepper Noise ◽  
Segmentation Method ◽  
Fisher Criterion ◽  
One Dimensional ◽  
Salt And Pepper

Precise recognition of the weed by computer vision, furthermore raising the weeding efficiency, reducing the use of herbicide, and decreasing the pollution to the environment is one of the key technologies in the field of precision agriculture. To determine the optimal threshold in image automatic segmentation and solve one-dimensional histogram without obvious peak and valley distribution, image segmentation method based on fisher criterion and improved adaptive genetic algorithm is proposed. This method can preserve the multifamily of population and the astringency of the algorithm, and can overcome the problems of poor astringency and premature occurrence. The result shows that the proposed approach has better immunity to Salt and Pepper Noise and greatly shortens the time of image segmentation.

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