A Novel Fuzzy Ant System for Edge Detection

O. Verma, M. Hanmandlu, A. Sultania, Dhruv
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引用次数: 40

Abstract

A new approach for edge detection is presented in this paper using fuzzy derivative and Ant Colony Optimization (ACO) algorithm to reduce the discontinuities presented in the image filtered by Sobel operator. The number of ants are calculated and placed at the endpoints of the edges in the image filtered by Sobel Edge detector. Fuzzy Derivative Technique gives fuzzy probability factor. This probability factor is used to decide the next most probable pixel to be edge. The Ant colony optimization (ACO) technique is taken from the behavior of some species of ants which uses certain chemicals (known as pheromone) to inform other ants about the appropriate path. The intensities of the pheromones help ants for making decision for the right path. This concept is used by placing artificial ants on the image and edges are calculated by considering intensity difference as heuristic information. Two rules are also proposed for reducing movement of ant.
一种新的模糊蚁群边缘检测系统
本文提出了一种新的边缘检测方法,利用模糊导数和蚁群优化算法来减少索贝尔算子滤波后图像中的不连续现象。计算蚂蚁的数量,并将其放置在索贝尔边缘检测器滤波后的图像的边缘端点上。模糊导数技术给出了模糊概率因子。这个概率因子被用来决定下一个最有可能成为边缘的像素。蚁群优化(蚁群优化)技术是从某些种类的蚂蚁的行为中提取的,这些蚂蚁使用某些化学物质(称为信息素)来通知其他蚂蚁合适的路径。信息素的强度有助于蚂蚁选择正确的路径。这个概念是通过在图像上放置人工蚂蚁来实现的,并且通过考虑强度差作为启发式信息来计算边缘。同时提出了减少蚂蚁移动的两条规则。
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