多类特征显微图像的识别

针对粉末状态下的中药材显微图像明显具有纹理的特性,提出了一种基于多类型特征组合的中药材显微图像的识别方法.分别使用了三种分析方法,即分形分析方法、小波分析方法、灰度/梯度共生矩阵法,在各种方法中提取对图像分类贡献较大的特征量,并组合成特征向量,作为识别的依据.实验中采用了k-近邻分类方法,实验结果表明了该方法的可行性和有效性,为中药材的定量识别提供了理论依据.

Multiple-Feature Recognition of Microimages

A new way to recognize the microimage of powdered Chinese medical herbs is put forward on multiple-feature basis according to their textural characteristics.It includes three analytic methods,i.e.,fractal analysis,wavelet analysis and gray level-gradient joint occurrence matrix.As the foundation on which the images are efficiently recognized,the characteristic vector is formed by combining together the characteristic quantities that are picked out according to different methods as above and how important the rates they play in the classification of images.Using the k-nearest neighbour classification,the experimental results show that the way proposed is feasible and effective and provides a theoretical reference for the quantitative recognition of Chinese medical herbs.