摘要
图像分割是在一个复杂的参数空间寻找最优分割参数,针对传统方法计算量大,分割效率低的特点,该文提出基于粒子群算法的图像分割方法。每个粒子代表一个可行的阈值向量,通过各个粒子的飞行来获得最优阈值。选用典型棉花病虫害图像作为研究对象,并与最大熵算法比较,结果表明,利用粒子群算法进行图像分割可以简化计算,提高分割效率,为棉花病虫害图像处理奠定基础。
Image segmentation is to find the optimal segmentation parameters in a complex parameter space.In view of the large amount of calculation and low segmentation efficiency of traditional methods,an image segmentation method based on particle swarm optimization algorithm is proposed.Each particle represents a feasible threshold vector,and the optimal threshold is obtained through the flight of each particle.The typical cotton pest image is selected as the research object,and compared with the maximum entropy algorithm,the results show that the use of particle swarm optimization algorithm for image segmentation can simplify the calculation,improve the segmentation efficiency,and lay a foundation for cotton pest image processing.
作者
宋莉莉
马莉
王泽伟
SONG Lili;MA Li;WANG Zewei
出处
《智慧农业导刊》
2023年第15期1-5,共5页
JOURNAL OF SMART AGRICULTURE
基金
新疆维吾尔自治区自然科学基金项目(2022D01C08)。
关键词
图像分割
粒子群算法
最大熵算法
病虫害
农作物
image segmentation
particle swarm optimization
maximum entropy algorithm
diseases and pests
crops
