一种新的基于图谱理论的图像阈值分割方法

A New Image Thresholding Method Based on Graph Spectral Theory

提出了一种新的图像阈值分割方法,该方法采用图谱划分测度作为区分目标和背景的阈值分割准则.采用基于灰度级的权值矩阵来代替通常所用的基于图像像素的权值矩阵来描述图像各像素的关系,因而算法所需的存储空间及实现的复杂性与其他基于图论的图像分割方法相比大大减少,从而有利于应用在各种实时视觉系统(如自动目标识别,ATR).大量的实验结果表明:与现有的阈值分割方法相比,文中提出的方法具有更为优越的分割性能.

In this paper, a novel thresholding algorithm is presented to achieve improved image segmentation performance at low computational cost. The proposed algorithm uses the normalized graph cut measure as the thresholding principle to distinguish an object from the background, as such fair treatment of different sets of diversified sizes is ensured. The weight matrices used in evaluating the graph cuts are based on the gray levels of an image, rather than the commonly used image pixels. For most images, the number of gray levels is much smaller than the number of pixels. Therefore, the proposed algorithm occupies much smaller storage space and requires much lower computational costs and implementation complexity than other graph-based image segmentation algorithms. This fact makes the proposed algorithm attractive in various real-time vision applications such as automatic target recognition （ATR）. A large number of examples are presented to show the superior performance of the proposed thresholding algorithm compared to existing thresholding algorithms.