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DETERMINING PLAUSIBLE MAPPING BETWEEN IMAGES WITHOUT A PRIORI KNOWLEDGE

This paper presents a method of automatically determining dense and smooth mapping between two images without a priori knowledge of either the camera pose or objects. In order to find plausible correspondences, images are first convolved with a set of linear filters similar to those that are used in early vision. The vector fields consisting of filter responses are then matched with each other by minimizing the cost function which expresses the similarity of transformed images and mapping smoothness in a multiresolutional hierarchy. Since a point in one image can correspond to its counterpart far away, globally optimized mapping may result in strong local distortion. Such distortions are prevented by guaranteeing the convexity of mapping in nonlinear optimization. In this paper, certain experimental results are discussed for various data sets, including images of slightly rotating objects, human faces, texture patterns and volume data sets, to demonstrate the usefulness of the proposed method.