摘要
在蚁群算法的基础上针对大米轮廓检测提出了一种改进的边缘检测蚁群算法。该算法能有效地检测出米粒的边缘信息,解决了传统大米颗粒检测方法的不稳定和不精确等问题。与此同时,还将其结果与原蚁群算法、Roberts、Sobel和Prewitt等边缘检测算子对图像处理的结果进行了研究对比,实验结果表明,采用改进的边缘检测蚁群算法对大米粒形的检测效果较好,正确率较高,且具有适应性强、效率高等特点。
Ant colony algorithm is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. On this basis, an improved ant colony algorithm is proposed to detect the edge of rice figure. It could detect the edge of the rice figure effectively, and also solve the instability and inaccuracy problem of the traditional method. In the mean time, a comparative analysis is made between the result of the improved ant colony algorithm and the results of ant colony algorithm and other boundary algorithms like Roberts, Sobel and Prewitt. The experiment results prove that the improved ant colony algorithm has better effect, and has the characteristics of strong adaptability and high efficiency.
出处
《微型机与应用》
2012年第13期42-45,共4页
Microcomputer & Its Applications
关键词
蚁群算法
大米粒形
图像分割
边缘检测
ant colony algorithm
flee figure
image segmentation
edge detection
