Extracting good features for motion estimation

Selecting image features whose correspondences can be accurately established between images is a key step in many image processing problems, such as camera and object motion estimation, 3D structure reconstruction, and image registration. In this paper, we present a new method of selecting good features for estimating motion from images. Our approach is different from other existing approaches in that we formulate feature tracking as a signal parameter estimation problem, give a quantitative measure of feature quality in terms of how accurately the feature can be tracked, and can adaptively select features with different shapes and sizes which depend on the local variations of the images. Through the analysis of this feature quality measure, we can characterize the basic properties that allow a feature to be well tracked. Some experimental results are shown to demonstrate the advantages and robustness of the proposed method.