摘要
为准确地实现目标识别,提出了将二维最大熵图像分割方法应用于红外图像实行分割.利用图像的二维直方图,二维最大熵分割方法不仅考虑了象素的灰度信息,而且还充分利用了象素的空间领域信息,能取得较为理想的分割结果.然而该方法所需的巨大运算量限制了其实际应用.运用PSO算法代替穷尽搜索获得阈值向量,求解速度可提高300～400倍,提高了分割效率.通过对实际的红外图像分割表明,这种方法简单、有效.
In order to detect objects accurately, an image thresholding approach named two dimensions （2-D） maximum entropy was proposed to do infrared image segmentation. By using the 2-D histogram of image, the 2-D maximum entropy method not only considers the distribution of gray information, but also takes advantage of the spatial neighbor information. However, its great computation was often an obstacle in application. The threshold vector was obtained by using a new optimization algorithm, namely, the particle swarm optimization algorithm （PSO）. The new way was proposed to realize the 2-D maximum entropy method instead of exhaustive search method. And it is 300 ～ 400 times faster than the traditional method. Through the example of segmenting the infrared image, the proposed method has been proved to be a fast method of segmenting infrared image.
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2005年第5期370-373,共4页
Journal of Infrared and Millimeter Waves
基金
国防重点实验室基金资助项目(51476040103JW13)
关键词
图像分割
微粒群优化
熵
目标识别
image segmentation
particle swarm optimization
entropy
target recongnition
