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This paper proposes a new method of calculating a matching metric for motion estimation. The proposed method splits the information in the source images into multiple scale and orientation subbands, reduces the subband values to a binary representation via an adaptive thresholding algorithm, and uses mutual information to model the similarity of corresponding square windows in each image. A moving window strategy is applied to recover a dense estimated motion field whose properties are explored. The proposed matching metric is a sum of mutual information scores across space, scale, and orientation. This facilitates the exploitation of information diversity in the source images. Experimental comparisons are performed amongst several related approaches, revealing that the proposed matching metric is better able to exploit information diversity, generating more accurate motion fields. [ABSTRACT FROM AUTHOR]

Fast and accurate motion estimation takes an important place in many fields of computer vision and image processing. Using Radon transform to compute projections of the images along specified directions is an effective way to show the relationship between the 2D image object and its projections and estimate motions between the images. All existing projection-based motion estimation methods without the use of iteration have a severe defect that only five of the six affine parameters can be estimated. There are some other methods that can estimate the six parameters, but most of them are usually based on a certain iterative framework, which is computationally intensive and sensitively dependent on the initial values. In this paper, a novel method based on Radon transform is proposed to estimate all the six affine parameters directly. The relationship in the projection domain between a pair of images connected by an affine motion is studied and a linear model is established, by which all the six affine parameters can be directively found. The employment of a hierarchical framework can produce more accurate results. The experimental results reveal that the proposed method has a much better performance than the state-of-the-art methods in this field. [ABSTRACT FROM AUTHOR]