摘要
针对花粉显微图像处理提出了一种自动分割方法,将有助于花粉识别系统的开发。使用归一化颜色分量训练图像块分类器,并且结合条件随机场和图割进行建模和优化,利用最大化后验概率(MAP)的方法实现花粉显微图像中花粉区域的分割。对于实验中的133幅图像,自动分割同人工分割的结果相比较,统计得到距离误差均值为7.3像素,准确率的平均值为87%。实验结果表明,使用图像块分类器和条件随机场模型可以用于花粉图像的分割。
An automatic segmentation for pollen microscopic images was proposed in this paper,which was useful to develop a recognition system of airborne pollen.First,the image patch classifier was trained with normalized color component.Then,conditional random field was employed to model pollen images and Maximum A Posterior（MAP） was used to segment the pollen areas in microscopic images,with graph cut algorithm for optimization.In the experiments,the respective average values of mean distance error was 7.3 pixels and the true positive rate was 87% on 133 images.The experimental results show that image patch classifier and conditional random field model can be used to accomplish segmentation of the pollen microscopic images.
出处
《计算机应用》
CSCD
北大核心
2011年第8期2249-2252,共4页
journal of Computer Applications
关键词
花粉显微图像
图像分割
图像块分类器
条件随机场
图割
pollen microscopic image
image segmentation
image patch classifier
Conditional Random Field（CRF）
graph cut
