Research On Improved FastICA Algorithm And Its Application

Independent Component Analysis (ICA) is a data processing method which search for its instrinsic statistical independence and non-gauss's factor from multivariate data. ICA can separate source signals which is statistically independent when nothing is known about source signals and hybrid matrix. ICA has very important application value in many aspects due to that the independence assumptions can be satisfied in the most practical problems, such as speech signal processing, image processing, face recognition, pattern recognition, data mining, and medical signal processing and so on. The research of its theory and algorithm also draws the attention of broad scholars. ICA comes to mature with nearly 20 years'development, but there are still some problems to be further perfect at present, including how to improve the convergence of the algorithm to better apply to the practice.This paper focuses on the relatively mature and widely used fast independent component analysis algorithm based on negative entropy (FastICA), The FastICA algorithm based on the-fifteen order Newton iterative correction form is improved by modifying kernel iterative process aiming to solving the problem that basic FastICA algorithm is sensitive to initial values. Simulation experiments show that initial value sensitivity is improved with more faster and stable convergence speed when the convergence of the results is ensured.In this paper, the improved FastICA algorithm is introduced to the moving object detection of video sequences, each video frame is regarded as mixtures of the independent sources such as background image and the foreground image which consists of the moving object. With the improved FastICA algorithm, the moving object image and the background image could be estimated, so the moving object could be detected and extracted from the video frames. Experimental results show that the moving object detection method using the improved FastICA algorithm can get more complete and clear moving object information when compared with the background difference method which can lost some information of moving object.Therefore, the moving object detection method with improved FastICA algorithm has a certain theoretical significance and application value for it can overcome some drawbacks of conventional methods.