Image estimation and compensation based on correspondence technique

An algorithm based on correspondence technique for image motion estimation and compensation is proposed. In conventional methods, the motion estimation and compensation are frequently solved with a block-matching based strategy, making it computationally intensive. In fact, the correspondence between feature points in successive image sequence is an useful information to estimate the object motion in picture. From the correspondence found by a Hopfield neural network, the vectors, called displacement vectors, are constructed rapidly, and they will characterize the object motion process. By means of the massive parallel processing-power of neural network and the proposed computational model, an efficient and accurate solution can be obtained.