Algorithm for the Detection of Feeble Edge of Complex Image

2012 ◽  
Vol 487 ◽  
pp. 915-919
Author(s):  
Xi Yun Wang
Keyword(s):  
Edge Detection ◽  
Ant Colony Algorithm ◽  
Detection Algorithm ◽  
Information Value ◽  
Ant Colony ◽  
Position Detection ◽  
Canny Edge ◽  
Key Steps ◽  
Improved Ant Colony Algorithm ◽  
Weak Edge

Image weak edge detection has a wide application. An improved ant colony algorithm is used for image edge detection . When image background is very complex ,the weak edge detection is more difficult. Around the key steps in the existing ant colony algorithm , we propose two improved methods. First, the expression of pheromone has been improved, and second, to improve the heuristic information value is calculated. Finally, the improved ant colony algorithm with the traditional Canny edge detection algorithm are compared.The improved edge detection experiments show that the new algorithm is position detection accurate, continuity, and the advantages of less interference.

A New Medical Image Edge Detection Algorithm Based on BC-ACO

2017 ◽  
Vol 31 (03) ◽  
pp. 1757002
Author(s):  
Jingyu Zhang ◽  
Jianfu Teng ◽  
Yu Bai
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Ant Colony Algorithm ◽  
Bacterial Chemotaxis ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Ant Colony ◽  
Image Edge Detection ◽  
Image Edge ◽  
Improved Ant Colony Algorithm

Taking the improved ant colony algorithm based on bacterial chemotaxis as a means, this paper proposes one new swarm intelligence optimization algorithm to solve the medical image edge detection problem. The improved ant colony algorithm based on bacterial chemotaxis mainly aims at the shortcoming that the basic ant colony algorithm lacks initial pheromone, and combines bacterial chemotaxis algorithm with basic ant colony algorithm. Firstly, feasible better solution can be found through bacterial chemotaxis algorithm and fed back as initial pheromone. Then ant colony algorithm is implemented to search for the global optimal solution. The algorithm test indicates that the improved ant colony algorithm is more effective in the aspects of searching precision, reliability, optimization speed and stability compared with basic ant colony algorithm. Finally, the improved ant colony algorithm is applied into the edge detection of medical image. It can be seen from the computer simulation that compared with other operators and basic ant colony algorithm on the issue of solving medical image edge detection, the improved ant colony algorithm has superiority and the detected edge is clearer.

scholarly journals An Image Edge Detection Method Based on Improved Ant Colony Algorithm

2011 ◽  
Vol 1 ◽  
pp. 236-240
Author(s):  
Xi Yun Wang ◽  
Pan Feng Huang ◽  
Ying Pings Fan
Keyword(s):  
Edge Detection ◽  
Ant Colony Algorithm ◽  
Detection Method ◽  
Ant Colony ◽  
Improved Method ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method ◽  
Background Image ◽  
Improved Ant Colony Algorithm

This paper raises an improved ant colony algorithm, for the detection of weak edge of complex background image, considering edge positioning accuracy, edge pixels, edge continuity and interference edges. This algorithm is improved in two aspects: first, we improved the expression of pheromone; second, we improved the calculation of Heuristic information. Compared with traditional Canny detector indicates, the improved method is proved to be accurate in edge detection, good continuity and less interference by experiment.

scholarly journals Research on the combination of improved Sobel operator and ant colony algorithm for defect detection

2021 ◽  
Vol 336 ◽  
pp. 01009
Author(s):  
Yilu Zhou ◽  
Xiaojin Fu
Keyword(s):  
Edge Detection ◽  
Defect Detection ◽  
Ant Colony Algorithm ◽  
Detection System ◽  
Detection Algorithm ◽  
Initial Position ◽  
Ant Colony ◽  
Image Preprocessing ◽  
Sobel Operator ◽  
Image Contour

There are many aspects in the defect detection system. Any deviation in any link will affect the accuracy of the final detection, and edge detection is very important in the image preprocessing stage. In this paper, a new edge detection algorithm is proposed. Firstly, the improved Sobel operator is used to detect the image contour, and then the position of the contour is taken as the initial position of ant colony algorithm. The experimental results show that the algorithm can extract the contour with uniform thickness and length from the original image collected by the industrial camera, and the running time of the algorithm is almost the same as that of the traditional ant colony algorithm, thus providing more accurate data for the defect detection of products in the later stage.

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