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Hypergeometric Filters for Optical Flow and Affine Matching

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Abstract

This paper proposes new “hypergeometric” filters for the problem of image matching under the translational and affine model. This new set of filters has the following advantages: (1) High-precision registration of two images under the translational and affine model. Because the window effects are eliminated, we are able to achieve superb performance in both translational and affine matching. (2) Affine matching without exhaustive search or image warping. Due to the recursiveness of the filters in the spatial domain, We are able to analytically express the relation between filter outputs and the six affine parameters. This analytical relation enables us to directly compute these affine parameters. (3) Generality. The approach we demonstrate here can be applied to a broad class of matching problems as long as the transformation between the two image patches can be mathematically represented in the frequency domain.

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Authors and Affiliations

  1. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 1521

    Yalin Xiong & Steven A. Shafer

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  1. Yalin Xiong
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  2. Steven A. Shafer
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Xiong, Y., Shafer, S.A. Hypergeometric Filters for Optical Flow and Affine Matching. International Journal of Computer Vision 24, 163–177 (1997). https://doi.org/10.1023/A:1007915105826

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