基于高斯混合模型的图像处理方法

Method of Image Processing Based on Gaussian Mixture Model

文章提出了一种新型的颜色空间转换形式以及一种优化的高斯混合模型,可以有效地减少了迭代次数,增强图像分割边缘。首先,采用GMM(Gaussian Mixture Model)对图像进行分割捕获,并利用开源视觉库中的函数对图像中的异物进行放大和分割操作,同时对彩色图像进行去噪处理,使图像中的异物清晰可见最后利用HSS彩色空间变换对GMM图像进行预处理,达到最佳效果。结果表明该方法能提高图像检测和处理的效率和异物识别的准确性。

This paper presents a new color space conversion form and an optimized Gaussian Mixture Model,which can effectively reduce the number of iterations and enhance the edge of image segmentation.Firstly,GMM(Gaussian Mixture Model)is used to segment and capture the image,and the functions in the open source visual library are used to amplify and segment the foreign objects in the image.At the same time,the color image is denoised to make the foreign objects in the image clearly visible.Finally,HSS color space transform is used to preprocess the GMM image to achieve the best effect.The results show that the method can improve the efficiency of image detection and processing and the accuracy of foreign objects recognition.