scholarly journals An Improved Adaptive Genetic Algorithm for Image Segmentation and Vision Alignment Used in Microelectronic Bonding

2014 ◽  
Vol 19 (3) ◽  
pp. 916-923 ◽  
Author(s):  
Fujun Wang ◽  
Junlan Li ◽  
Shiwei Liu ◽  
Xingyu Zhao ◽  
Dawei Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Adaptive Genetic Algorithm ◽  
Vision Alignment

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.

A Vision Alignment Method Based on Improved Genetic Algorithm for IC Packaging

2011 ◽  
Vol 189-193 ◽  
pp. 4177-4181 ◽  
Author(s):  
Shi Wei Liu ◽  
Jun Lan Li ◽  
Xing Yu Zhao ◽  
Da Wei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Moment Invariants ◽  
Improved Genetic Algorithm ◽  
Ic Packaging ◽  
Otsu Method ◽  
Vision Alignment ◽  
The Moment ◽  
Positioning Time ◽  
Speed And Accuracy

In order to improve the speed and accuracy of vision alignment for IC packaging, genetic algorithm and Otsu method are applied to vision alignment. According to the features of the image in IC packaging, an improved self-adaptive genetic algorithm combined with Otsu method is proposed in this paper, and the moment invariants method is used to carry out the remaining steps of vision alignment. Finally, experiments are undertaken by using a kind of IC chip, results show that the positioning error is less than 2μm, and the positioning time is less than 60ms.

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