摘要
叶片的形态测量在苗木生长自动监测中具有重要意义,在形态测量前首先要将完整的叶片从背景中提取出来。针对彩色苗木叶片图像的特点研究了利用几何活动轮廓模型进行完整叶片的自动分割的方法。首先利用图像的全局信息和C-V模型进行初始分割,当曲线到达目标边界附近时,利用改进的基于图像局部信息的能量模型进行边界的精确定位。该方法将C-V模型不受初始位置影响的优点和改进模型克服弱边界泄漏的优点结合起来,实现了叶片的自动分割,取得了令人满意的实验结果。
Measurement of leaf morphology is significant in automatic monitoring of seedling growth. The leaf images have to be extracted from the background prior to morphological measurement. We developed an automatic segmentation method of intact leaves based on the geometric active contour model according to the characteristics of color leaf images. The global information of images and C-V model were used for initial segmentation. When the curve moves close to the border of the object, the boundary of the object was located using the improved model based on the local information of the image. The proposed segmentation method combines the advantage of C-V model, i. e. , not likely to be affected by original position of the curve, and the strength of the improved model which overcomes the boundary leakage. Experimental results show that the proposed method can effectively segment leaf images.
出处
《北京林业大学学报》
CAS
CSCD
北大核心
2011年第1期90-93,共4页
Journal of Beijing Forestry University
基金
"863"国家高技术研究发展计划项目(2006AA10Z247)
关键词
叶片分割
几何活动轮廓模型
全局信息
局部能量信息
leaf segmentation
geometric active contour model
global information
local energy information
