Window-Based Image Registration using Variable Window Sizes

We present an unsupervised image registration algorithm to estimate the background object motion in a real video sequence. The algorithm is based on a Gaussian minimisation technique. It has been shown earlier that initialization of such an approach is very important to achieve the motion parameters of the background object precisely, and that the use of a windowing technique can give better background object motion estimation results, even with large background occlusions. In some cases, however, the fixed window size initializes the gradient descent algorithm in a sub-optimal way. Here, another window size would bring the desired estimation direction. In this paper, we present a technique where variable window sizes are used to prevent these outliers. Experimental results show that the technique works very well with the considered test sequences.