Linear search applied to global motion estimation

Shlomo Greenberg, Daniel Kogan

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)493-504
Number of pages12
JournalMultimedia Systems
Volume12
Issue number6
DOIs
StatePublished - 1 May 2007

Keywords

  • Global motion estimation
  • Gradient-based
  • Linear search
  • Optimization

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Linear search applied to global motion estimation'. Together they form a unique fingerprint.

Cite this