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Study of Improved Ant Colony Algorithm Applied to Image Edge Detection

Author: JieHuanQing
Tutor: LiHongXin
School: Lanzhou University
Course: Communication and Information System
Keywords: Pheromone Ant Colony Algorithm Image Edge Detection Image Recognition
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 80
Quote: 1
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Abstract


The ant colony algorithm is formed according to the nature of ants foraging rules a combinatorial optimization algorithm, originally used to solve the traveling salesman problem (TSP). In view of the ant colony algorithm to solve quadratic optimization outstanding performance on the problem, more and more scholars ant colony algorithm is applied to solve combinatorial optimization problems in various fields, including path planning, shop scheduling, network routing, enterprise planning, image identification. Ability of the ants the individual itself is limited, but the entire ant colony was able to complete the task can not be completed in a single individual. Which this pheromone from the medium of self-organization mechanism. Ant colony algorithm is also simulated this mechanism, the artificial ants search path size to determine the concentration of the pheromone. However, this mechanism easily lead ant colony in the local optimum, and the large number of ants also makes the computing speed of the algorithm significantly slow. In order to solve these two problems, many scholars have proposed improved methods. In this paper, the idea of ??genetic algorithm to improve the ant colony algorithm, the algorithm introduced variability factor the variation factor values, and can be adjusted with the conduct of the algorithm, the search range of the ant colony is large enough so that in the early stages of the algorithm that effectively prevent local convergence of the algorithm while in the late of the algorithm guarantees the ant pheromone sensitivity so as to speed up the convergence rate of the algorithm. The improved algorithm for the simulation comparison experiments, the final experimental results show that the improved algorithm is superior to the basic algorithm, to achieve the purpose of improvement in the convergence rate, the number of iterations. Usually the method of image edge detection: gradient operator method, Sobel operator method, Robet operator method, Log operator method, Canny operator sub-law, these are different degrees of noise-sensitive. Ant colony algorithm for image edge detection, the first is to create a matrix consistent with the size of the image, ants moving in the image along the way will leave a pheromone. Pixels obvious for those changes, there will be more ants access by changing the pheromone update mechanism, so that the ants tend to browse images in grayscale gradient value pixels. This creates a positive feedback mechanism the ants will outline the edges of the image. This article uses the ant colony algorithm to achieve an improved image edge detection After test simulation proved the algorithm can more clearly depict the image edge information, especially on the leading edge of more prominent. After changing the threshold size can effectively suppress the noise in the image.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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