Infrared small target image segmentation based on niche chaotic mutation particle swarm optimization (NCPSO)
In practice, such a problem is often encountered that the proportion of target to background in image segmentation is very small. For this, a thresholding method used for the image segmentation of infrared small target is proposed in this paper, which is based on the area difference between background and target and intra-cluster variance. This method points out the existing methods of image threshold segmentation can not effectively extract infrared small targets, and offers the thresholding formula of the area difference between background and target and intra-cluster variance based on 2-D histogram of vertical and more effective oblique. It puts forward the niche chaotic mutation particle swarm optimization algorithm based on 2-D histogram of vertical and oblique. Finally the result and running time of this paper are given out in the result and analysis of experimental, which is compared with the NCPSO of 2-D Otsu, maximum and Fisher. The results show that the method in this paper can accurately extract infrared small targets, and has short running time and strong anti-noise performance.
image segmentation infrared small target thresholding chaotic mutation niche particle swarm optimization small target 2-D histogram of oblique
Jiaming Wu Yiquan Wu
School of Information Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing,China
国际会议
南京
英文
413-417
2010-08-26（万方平台首次上网日期，不代表论文的发表时间）