红外成像制导二维斜分最大熵分割的快速实现

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。

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

To deal with the low contrast infrared images in complex background for infrared imaging guidance, a new fast recursive method based on Kapur's maximum entropy threshold discriminant was presented, coarse-fine searching strategy with successive approximation was adopted to reduce the threshold searching area, and the best threshold was searched by pixel in the possible area. The analysis of 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' segmentation, the running time and memory cell needed by the presented method are all less than existing fast maximum entropy threshod recursive methods based on two-dimensional histogram vertical or oblique segmentation, the running time is about 14% of the original method. In the result image, the noise is less, the boundary is more elaborate and complete, the applicability is stronger. The presented method meets the engineering practical requirement of the infrared imaging guidance system.