图切分( Graph Cuts,GC)是近年来兴起的基于图论框架的图像分割方法,该理论的新颖之处在于它的全局最优性和结合多种知识的统一性. 但当图像较大时运算非常耗时.该文提出了一种基于GC的层次式图像分割方法.先在低分辨率中用GC以较低的分割代价获取粗尺度的初始分割,再将结果轮廓映射回高分辨率图像中并构造出窄带,进而采用matting思想,在窄带内获取精确分割.实验结果表明,本文方法在确保分割结果准确性的同时,运算速度大幅度提高.%Graph Cuts (GC) is a novel image segmentation method based on graph theory framework. The innovations of this theory lie in its global optimization and the unity of knowledge. However, if the image is large, computation will be very time-consuming. This paper presents a GC-based Hierarchical image segmentation method. First the initial segmentation is obtained through GC in the low-resolution with a very low computational cost. Then the contour is projected back to the high-resolution image to construct a narrow band. At last accurate segmentation in the narrow-band is achieved by using of matting arithmetic. Experimental results show that this method can ensure the accuracy of segmentation results with a significant increasing in computing speed.
展开▼
