Background Clutter Suppression Techniques In Image Sequences With Moving Point Targets

As we know, gloal motion caused by camera vibration is quite common in image sequence . This nuisance factor resukts in translational,totational and parallax distortions in images . So, the image registration process has become a critical and non-separable part of motion analysis and segmentation . Global motion can be modeled by a few parameters will be stated in chapter 2. In chapter two are given the system structure and key technologies of global estimation and compensation, describing several possible details of each key technology. Global motion parameters estimated using regression technique which first estimate the local motion and then uses the local information to find the global motion that minimize the least square error. Then the global motion was compensated using these parameters. Refinements are carried out during the estimation inner process based on iterative elimination of singular values which introduce the bias to the estimated parameters. The proposed technique is not only computationally simple, but also has high performance. Theoretical analysis of the technique's performance and experimental results are also given in this paper.And it is an inevitable thing that background clutter is an appliance of to imager sensor, So itimports much that an approach for clutter suppression to construct the virtual observer. Nonparametric regression studies optimal methods of function (background clutter) estimation from noisy observations. The distinguishing feature of nonparametric regression is that there is no (or very little) a priori knowledge about the form of the true function which is being estimated.