基于自适应粒子群算法的最大势值阈值分割

Maximum Potential Value Method of Image Segmentation Based on Adaptive Particle Swarm Optimization

为解决图像分割中最优阈值选择的难题,本文提出了一种基于粒子群优化算法和数据场概念的图像二维阈值分割方法.新方法把数据场的概念引入到图像处理中来,将图像的灰度值空间映射到数据场的势空间,再通过自适应的粒子群优化算法寻找数据场中能量最大的阈值.在进行空间转换的过程中,把二维直方图中的频率作为数据场中数据对象的质量,选用拟核力场的高斯势函数计算二维直方图各元素之间的相互作用,生成了二维直方图的三维数据场.实验结果表明,该方法不仅是合理、有效的,而且大大降低了计算的复杂性,能够适应大多数图像的分割.

In order to correctly select the optimal threshold for image segmentation, a novel method of image segmentation based on adaptive particle swarm optimization and data field is imposed. The method maps the image from grayscale space to the data field of potential space. By taking the frequency of two-dimension gray histogram as the mass of data field, it calculates the interactions between elements in the two-dimension histogram, thus generating a three-dimension data field. Then, by employing adaptive particle swarm optimization, the optimal threshold, which is the point of maximum potential value, is found and good segmentation result is obtained. It is indicated by the experiments that the proposed method is not only effective, but also greatly reduces the computational complexity.