红外成像制导二维斜分最大熵分割的快速实现  

Fast maximum entropy thresholding based on two-dimensional histogram oblique segmentation in infrared imaging guidance

作　　者：乔立永[1] 徐立新[1] 高敏[2] 

机构地区：[1]北京理工大学机电学院,北京100081 [2]军械工程学院导弹工程系,河北石家庄050003

出　　处：《红外与激光工程》2013年第7期1691-1699,共9页Infrared and Laser Engineering

基　　金：军队科研资助项目

摘　　要：针对红外成像制导复杂背景下低对比度红外图像的分割问题,提出了一种新的基于Kapur最大熵阈值判别式的二维斜分快速递推算法,并采用逐步逼近的粗细搜索策略,减少阈值搜索区域,在可能的阈值范围内逐点寻找最佳阈值。通过对算法的复杂度进行分析,并对实际获取的红外图像进行分割实验表明,Kapur最大熵阈值判别式更加适合于低对比度红外图像分割,提出的二维斜分快速算法所需的运行时间和存储单元均少于现有的二维直分或斜分最大熵分割快速递推算法,运行时间约为原始算法的14%,分割结果的噪点更少,边界更加细致完整,适用性更强,满足红外成像制导系统工程实用化要求。To deal with the low contrast infrared images in complex background for infrared imagingguidance, a new fast recursive method based on Kapur＇s maximum entropy threshold discriminant waspresented, coarse-fine searching strategy with successive approximation was adopted to reduce thethreshold searching area,and the best threshold was searched by pixel in the possible area. The analysisof the methods＇ complexity and the segmentation experiments of the real infrared images show that,Kapur＇s maximum entropy threshold discriminant is more suitable for low contrast infrared images＇mentation, the running time and memory cell needed by the presented method are all less than existingfast maximum entropy threshod recursive methods based on two-dimensional histogram vertical or obliquesegmentation, the running time is about 14% of the original method. In the result image, the noise isless, the boundary is more elaborate and complete, the applicability is stronger. The presented methodmeets the engineering practical requirement of the infrared imaging guidance system.

关 键 词：红外制导 二维斜分 最大熵 快速算法 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]