利用云模型和数据场的图像分割方法

摘要

An image thresholding method is proposed to select the optimal threshold for image segmentation. The data field is introduced to map the original image from grayscale space to the corresponding potential space by the proposed method, and the relative and the absolute data fields are produced with Iwo different mass functions. Then, by considering the features of the data fields and combining the global and the local statistical characteristics of the image, a potential threshold for data field can be yielded. Next, the pixels are divided into possible backgrounds and objects, which are represented with background and object cloud models generated by backward cloud generator. According to the membership degrees of pixels over two cloud models, the final result of image thresholding is obtained by maximum determination method. It is indicated by the experiments that the proposed method yields accurate and robust result, and it is reasonable and effective.%针对图像自动分割中的最优阈值选择问题,提出一种基于云模型和数据场的图像分割方法.该方法引入数据场实现图像灰度值特征空间到数据场势值空间的非线性映射,设定两个不同的质量函数分别形成相对数据场和绝对数据场.利用两类数据场的特点,结合全局和局部统计特征获得自适应势阈值对图像像素进行划分,产生图像潜在的背景或目标像素集合.进一步由逆向云发生器算法产生图像背景和目标的云模型表示,根据图像像素隶属于背景、目标云模型的程度,采用极大判定法则得到最终的分割结果.实验结果表明,该方法的分割效果较好、性能稳定,具有合理性和有效性.