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Linear search applied to global motion estimation

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Abstract

Gradient-based algorithms for global motion estimation are effective in many image-processing tasks. However, when analytical estimation of derivatives of objective function is not possible, linear search based algorithms such as Powell perform better than the gradient-based ones. In this paper we propose global motion estimation algorithm that exploits linear search based algorithm, particularly Powell, instead of commonly used gradient-based one. We also introduce a new approach for extracting global motion parameters called Two Step Powell-based GME. Using this approach we further improve the Powell-based GME. The proposed Powell-based GME outperforms Gauss–Newton algorithm (gradient-based) in terms of PSNR. The proposed Two Step Powell GME algorithm outperforms Powell-based GME in terms of PSNR and computational time.

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Authors and Affiliations

  1. Communication Systems Engineering Department, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva, 84105, Israel

    Shlomo Greenberg

  2. Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva, 84105, Israel

    Daniel Kogan

Authors
  1. Shlomo Greenberg
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  2. Daniel Kogan
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Correspondence to Shlomo Greenberg.

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Greenberg, S., Kogan, D. Linear search applied to global motion estimation. Multimedia Systems 12, 493–504 (2007). https://doi.org/10.1007/s00530-006-0069-2

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  • DOI: https://doi.org/10.1007/s00530-006-0069-2

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