基于独立分子量分析的图象分离技术及应用

简要介绍了有关独立分量分析的基本理论和算法，探讨了独立分量分析在序列图象处理方面的应用，提出了基于独立分量分析的运动目标检测新方法，同时用独立分量分析方法对含有运动目标的序列图象进行了独立分量分离的试验，试验中，首先获取序列图象的独立分量和模型混合矩阵，然后将含有背景干扰的独立分量置零，并用混合矩阵进行逆运算，从而获得非常清晰的运动目标轨迹，试验结果表明，这种独立分量分析方法具有良好的盲源分离性能，而且在运动目标检测等方面，基于独立分量分析的检测方法较传统的检测方法更有效。

Image Separation Technique and Application Based on Independent Component Analysis

Independent Component Analysis(ICA) is a novel approach of blind source separation and has received attention because of its potential application in signal processing such as in speech recognition system, image processing, telecommunication and medical signal processing. In this paper, the fundamental theory and algorithm of ICA is introduced. The feasibility of ICA for sequential image processing is studied and a novel ICA based approach of moving target detection is proposed. One of the main tasks of moving target detection is how to remove the interference of background efficiently. In our research work, we find that ICA has the good performance for the background rejection. The steps of our method are that: firstly, we apply ICA to the sequential images of containing moving target to get the mixing matrix of ICA linear mixing model and the vector of the independent components. The result of ICA show that the background component is separated from other independent components. So, secondly we can set the independent components that contain obvious background image to be zero and then do the inverse computation of ICA using the mixing matrix. After the two processing steps above, we get the clear track of moving target. The experiment results illustrate that ICA has good performance for blind source separation, and the novel approach of moving target detection based on ICA is more effective than the traditional detection method.