中文摘要：

为了提高高斯混合模型对小麦病叶的分割精度，减少分割时间，提出了一种基于PCA和高斯混合模型的分割方法。首先充分利用图像的颜色信息，将图像多个颜色通道进行主成分分析计算，获得3个主要颜色通道；在此基础上，将图像分成多个分块，根据其像素平均值排序，各取前后多个分块组成新的像素集合进行高斯混合模型运算；最后遍历整个图像，将每个像素归类到已求出的高斯模型上得出分割结果。通过对小麦锈病图像的分割试验表明，该方法的错分像素率分别比高斯混合模型、K—means等传统分割方法低5．46和13．44个百分点。

英文摘要：

In order to improve the segmentation accuracy and reduce the segmentation running time of Gaussian mixture model used on wheat lesion images, a segmentation method based on PCA and Gaussian mixture model was proposed. Firstly, in order to completely use the color information of an image, three primary color channels of the image were obtained through the principal component analysis （PCA） method from R, G, B or H, S, V color channels of this image. Secondly, the image was divided into many blocks, which were then sorted according to their mean pixel values. After sorting, those blocks lying in the front and the rear were selected to comprise a new pixel set by the Gaussian mixture model, and further, the corresponding Gaussian model parameters were obtained. Finally, the proposed method traveled all pixels in the image and classified each pixel into the corresponding Gaussian model category. Experimental results show that the proposed method has gained better promotions in segmentation error rate and running time compared with the traditional segmentation method and is effective for wheat leaf rust lesion segmentation.