最大流模型是图像分割领域强有力的工具。 近年来, 一种基于连续的最大流模型被提出并有效应用于图像分割。然而, 该模型的空间流约束变量为全局常数, 未与图像的结构特征相联系。 同时, 源和汇的初始值计算量大, 模型的数值实现效率不甚理想。 针对这些问题, 结合图像的结构、统计特征和预处理算法(包括分片常数算法和最大类间方差—直方图算法), 给出了连续最大流图像分割模型及算法。实验结果验证了本文算法的有效性,能够提高分割精度,加快运行速度。

The max-flow model is used as a powerful tool to image segmentation problems. In recent years, a new model based on the continuous max-flow approach is presented and well adapted to segment images. However, the constraint of spatial flow in the model is simply set to a global constant, and it is not associated with architectural feature. Meanwhile, the initial terms of the source flow and sink flow need large amount of calculation, and cannot obtain satisfactory segmentation results. In order to overcome these shortages, we combine architectural features and statistical features of the image and a preprocessing algorithm, which include piecewise constant algorithm and Otsu-Histogram algorithm. Experimental results verify that the model is efficient.