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.

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.

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.

scholarly journals Integrated Tomato Picking and Distribution Scheduling Based on Maturity

2020 ◽  
Vol 12 (19) ◽  
pp. 7934
Author(s):  
Anqi Zhu ◽  
Bei Bian ◽  
Yiping Jiang ◽  
Jiaxiang Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Joint Optimization ◽  
Adaptive Genetic Algorithm ◽  
Maturity Model ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Order Processing ◽  
Supply Chain Efficiency ◽  
Distribution Scheduling

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

Research on the Optimization of the Numerical Value Based on Improved Genetic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1256-1260
Author(s):  
Zhen Dong Li ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Adaptive Genetic Algorithms ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability ◽  
New Algorithms ◽  
Relevance Index

For the lack of crossover operation, from three aspects of crossover operation , systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithms global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithms convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.

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