| 改进二维最大类间方差法及其在黄瓜目标分割中的应用 |
| |
| 引用本文： | 孙,俊.改进二维最大类间方差法及其在黄瓜目标分割中的应用[J].农业工程学报,2009,25(10):176-181. |
| |
| 作者姓名： | 孙 俊 |
| |
| 作者单位： | 江苏大学江苏省现代农业装备与技术重点实验室,镇江,212013;江苏大学电气信息工程学院,镇江,212013 |
| |
| 基金项目： | 国家自然科学基金资助项目（60575020）；江苏大学高级专业人才科研启动基金（05JDG050）；中国博士后科学基金面上资助项目（20070420972） |
| |
| 摘 要： | 为了提高图像阈值分割算法的应用广适性和处理实时性，该文在二维最大类间方差分割算法的基础上，研究邻域模板尺寸对最佳阈值的影响，将图像的灰度值、邻域尺寸及邻域均值进行遗传基因编码，利用遗传算法得到阈值最优解的小范围，在此小范围内进行二次遗传算法运算寻求全局最优解。将此基于两级遗传算法的二维最大类间方差分割算法应用于黄瓜计算机视觉识别目标试验中，试验结果表明，在计算类间方差次数上，基于两级遗传算法的二维最大类间方差算法分别为二维最大类间方差耗时的0.18%和一维Otsu算法耗时的46.87%，耗时上也较传统二维最大类间方差算法和一维Otsu算法有很大缩短，分割效果也有了明显改善。同时该算法也为目标识别领域提供了一种新型的实时图像分割方法，具有一定的推广价值。
|
| 关 键 词： | 图像分割，遗传算法，阈值，二维，最大类间方差法，黄瓜 |
| 收稿时间： | 1/8/2008 12:00:00 AM |
| 修稿时间： | 2009/9/14 0:00:00 |
Improved 2D maximum between-cluster variance algorithm and its application to cucumber target segmentation |
| |
| Sun Jun.Improved 2D maximum between-cluster variance algorithm and its application to cucumber target segmentation[J].Transactions of the Chinese Society of Agricultural Engineering,2009,25(10):176-181. |
| |
| Authors: | Sun Jun |
| |
| Institution: | 1. Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang 212013, China; 2. School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China |
| |
| Abstract: | In order to improve the wide adaptability and the real-time processing property of image threshold segmentation algorithm, a 2D maximum between-cluster variance image segmentation algorithm was brought forward. On the base of 2D maximum between-cluster variance algorithm, the impact of the size of the neighborhood template on the best threshold value was studied, and not only the gray level information of each pixel and its spatial correlation information within the neighborhood, but also the dimension of neighborhood domain were encoded by genetic factors. The small range of the optimal threshold was gotten based on genetic algorithm, and in the small range, the global optimal threshold was found based on the second genetic algorithm computing. The improved algorithm was applied into cucumber computer vision system. The experiment results showed that, the consuming time of computing between-cluster variance of 2D maximum between-cluster variance algorithm based on two level genetic algorithm was 0.18% more than that of 2D maximum between-cluster variance algorithm, and was 46.87% more than that of Otsu algorithm. At the same time, the consuming time of computing between-cluster variance of 2D maximum between-cluster variance algorithm spent shorter time on running and had better segmentation effect than that of the traditional algorithms such as 2D maximum between-cluster variance algorithm and Otsu algorithm. The improved segmentation algorithm provides a new real-time image segmentation method for the object recognition field, so it has a certain promotion value. |
| |
| Keywords: | image segmentation genetic algorithms threshold logic 2 dimensional maximum between-cluster variance algorithm cucumber |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
