云环境下改进TLBO算法的图像边缘检测  

Image edge detection using an improved TLBO Algorithm in cloud computing

作　　者：杨晓红[1,2] 杨娟 陈韬[1,2] 张清扬[3] 

机构地区：[1]安徽省基础测绘信息中心,合肥230031 [2]安徽省智慧城市与地理国情监测重点实验室,合肥230031 [3]合肥工业大学计算机与信息学院,合肥230001

出　　处：《测绘科学》2017年第11期188-194,共7页Science of Surveying and Mapping

基　　金：安徽省国土资源科技项目(2015-K-16);安徽省智慧城市与地理国情重点实验室开放基金项目(2016-K-02Y)

摘　　要：针对TLBO算法在解决复杂高维问题时,表现容易陷入局部最优,多样性丢失过快等不足的问题,提出了一种基于云计算环境下改进的TLBO算法(ITLBO),并将其应用到图像边缘检测中。通过对基本TLBO算法中的‘教’与‘学’阶段进行改进,引入一种新的种群更新方法,提高算法的迭代寻优能力。利用云环境下将该方法应用到图像边缘检测中进行实际性能验证,实验仿真结果表明,该方法提取图像的边缘信息的有效性。本文研究可为智慧城市的建设提供关键技术参考。According to the problem that global exploration performance of basic teaching-learningbased optimization（TLBO）algorithm in solving complex high-dimensional,the paper puts forward a new method of improved teaching-learning-based optimization（ITLBO）being used in image edge detection.By improving the teaching and learning phases of the basic TLBO algorithm,a new population updating method is introduced to improve the iterative optimization ability of the algorithm,The method is applied to image edge detection in the cloud environment.Experimental results show that the effectiveness of the proposed method to extract the edge information of the image.The research results haves certain reference value for the construction of smart city.

关 键 词：TLBO算法 图像边缘检测 优化方法 图像处理 

分 类 号：P273[天文地球—测绘科学与技术]