In this paper, we propose a method to estimate affine motion parameters from consecutive images with the assumption that the motion in progress can be characterized by an affine model. The motion may be caused either by a moving camera or moving object. The proposed method first extracts motion vectors from a sequence of images and then processes them by adaptive robust estimation to obtain affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a flexible weight function based on a sigmoid function. During the estimation process, we tune the sigmoid function gradually to its hard-limit as the errors between the input data and the estimation model are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. The experimental results show that the suggested approach is very effective in estimating affine parameters.

In this letter, we propose an effective method to retrieve images from a 2D shape image database using discrete shock graphs combined with an adaptive selection algorithm. Experimental results show that our method is more accurate and fast than conventional approaches and reduces computational complexity.