航空学报 > 2002, Vol. 23 Issue (4): 368-372

红外航空图像自动目标识别的形态滤波神经网络算法

李予蜀1,3, 余农2,3, 吴常泳2, 汤心溢2, 李范鸣2   

  1. 1. 华中科技大学 湖北武汉 430074;2. 中国科学院上海技术物理研究所 上海 200083;3. 空军第一航空学院 河南信阳 464000
  • 收稿日期:2001-06-18 修回日期:2002-04-15 出版日期:2002-08-25 发布日期:2002-08-25

MORPHOLOGICAL NEURAL NETWORKS WITH APPLICATIONS TO AUTOMATIC TARGET RECOGNITION IN AERONAUTICS INFRARED IMAGE

LI Yu-shu1,3, YU Nong2,3, WU Chang-yong2, TANG Xin-yi2, LI Fan-ming2   

  1. 1. Huazhong U niver sity of Science and Technolog y, Wuhan, 430074, China;2. Shang hai Institute o f Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China;3. Air Force College of Aeronautical Technology, Xinyang 464000, China
  • Received:2001-06-18 Revised:2002-04-15 Online:2002-08-25 Published:2002-08-25

摘要:

提出了一种有实用意义的形态滤波神经网络模型及其自适应 BP学习算法。形态滤波网络的优化设计过程实际上是网络参数 (结构元素 )不断调整、逐步适应图像环境的优化学习过程,从而将目标客体的特征规律反映到网络结构上来,以实现对复杂变化的图像具有良好的滤波性能和稳健的适应能力。为结合运动图像目标的检测需要,采用了渐进收缩误差、适时校正网络权值的动态跟踪学习算法。通过实验结果可以看出,该算法不仅能适应复杂多样的背景环境,而且对运动目标的连续检测能力具有位移不变、伸缩不变和旋转不变的特性。

关键词: 数学形态学, 图像分析, 目标检测, 神经网络, 优化计算

Abstract:

A practical neural network model of morphological filters with its optimal parameters training algorithm is proposed. For application to the infrared motional image target detection, a dynamic training algorithm, namely Morphological Adjusted-Weight Neural Network (MANN), is applied to the detecting process using the asymptotic shrinking error and appropriate network weights adjusting. Experimental results show that the algorithm has an invariant property with respect to the shift, scale and rotation of the moving target in the continuing detection of moving targets.

Key words: mathematical morphology, image analyzing, target detection, neural net work, optimization computing