摘要
图像分割是目标识别的首要和关键步骤。目前的图像分割方法有多种,其中阈值方法优点比较突出,但是采用阈值方法分割的关键是要能高效率地找到被分图像的最佳熵阈值。针对这一问题,将Geese-LDW-PSO算法的位置更新公式作了改进,即用当前种群的全局极值取代所有粒子的当前位置,并将之用于熵阈值图像分割中。仿真实验表明,该算法可以快速稳定地获得一幅图像的最佳分割阈值。仿真结果显示,该方法对车牌分割具有较好的性能。
Image segmentation is a key part in image processing field.At present,there are several image segmentation methods, among which the thresholding method has predominant advantages.But the key of the thresholding method is to find the optimum entropy threshold of an image effectively.To solve the problem,the location updating equation of the Geese-LDW-PSO algorithm has improved that the present position of all particles is replaced by the global best value of the population.And the improved algorithm is applied into entropy thresholding image segmentation method.The simulation results show that this algorithm can ob- tain the optimum threshold value of an image rapidly and stably and has good performance in the segmentation of a vehicle brand image.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第29期174-176,187,共4页
Computer Engineering and Applications
基金
教育部科学技术研究重点项目(No.107106)
教育部高等学校科技创新工程重大项目培育基金项目
关键词
粒子群优化
雁群
线性递减惯性权重
直方图
熵
particle swarm optimization
wild geese
linear descend inertia weight（LDW）
Histogram
entropy
