基于MRF的进化规划算法应用于红外图像分割

Infrared Image Segmentation by Evolutionary Programming Algorithm based on Markov Random Field

在中近程反坦克导弹红外制导系统中,图像分割的结果更注重于目标的位置信息。本文利用马尔可夫随机场(MRF)的局部相关特性,同时结合进化规划的全局优化搜索算法,提出了一种针对简单背景的红外图像分割算法。该算法模拟生物圈种群的竞争进化关系,对可能的目标区域给予较高的适应度,并对其余无关的背景和噪声进行快速的抑制,以达到突出目标和快速分割的目的。试验证明,该算法对简单背景下的中等目标有很好的分割效果,对噪声也有良好的抑制效果。

The position information of targets is important in the image guidance systems of middle/short-range anti-tank missiles. The encompass pertinence of Markov Random Field and Evolutionary Programming have been applied in many image segmentation problems. Considering the scarce texture and fuzzy edges of infrared, an infrared image segmentation algorithm with simply background is proposed. To quickly segmenting target's areas from an IR image, the algorithm imitates the natural evolutions and uses the competitions of different creatures. The target areas can get higher fitness mark than the background and trivial areas, so that the noise and unimportant areas are restrained. Experimental results have shown that quick segmentation and good restraint to noise can be achieved with the proposed scheme.