Block-based motion vector smoothing for periodic pattern region

Young Wook Sohn, Moon Gi Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

When finding true motion vectors in video sequences, multiple local minima areas such as periodic pattern cause severe motion errors. There have been efforts to reduce motion errors in the region, where they use exhaustive full-search motion estimation scheme to analyze or find a solution for the region. To find robust motion vectors in the periodic pattern region using non-exhaustive motion estimator, we propose a recursive motion vector smoothing method. Recursively averaged vectors are used for periodic pattern region and the input vectors from a conventional search method are used for other regions, controlled by a weighting parameter. Properties of periodic pattern is considered in calculating the parameter to adaptively weight on the input or the mean vectors. Experimental results show motion vector improvements in the periodic pattern region with the input vectors from non-exhaustive search method.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 4th International Conference, ICIAR 2007, Proceedings
Pages491-500
Number of pages10
Volume4633 LNCS
Publication statusPublished - 2007 Dec 1
Event4th International Conference on Image Analysis and Recognition, ICIAR 2007 - Montreal, Canada
Duration: 2007 Aug 222007 Aug 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4633 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Image Analysis and Recognition, ICIAR 2007
CountryCanada
CityMontreal
Period07/8/2207/8/24

Fingerprint

Motion Vector
Smoothing
Search Methods
Motion
Smoothing Methods
Motion Estimation
Local Minima
Weighting
Motion estimation
Estimator
Experimental Results
Weights and Measures

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Sohn, Y. W., & Kang, M. G. (2007). Block-based motion vector smoothing for periodic pattern region. In Image Analysis and Recognition - 4th International Conference, ICIAR 2007, Proceedings (Vol. 4633 LNCS, pp. 491-500). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4633 LNCS).
Sohn, Young Wook ; Kang, Moon Gi. / Block-based motion vector smoothing for periodic pattern region. Image Analysis and Recognition - 4th International Conference, ICIAR 2007, Proceedings. Vol. 4633 LNCS 2007. pp. 491-500 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Sohn, YW & Kang, MG 2007, Block-based motion vector smoothing for periodic pattern region. in Image Analysis and Recognition - 4th International Conference, ICIAR 2007, Proceedings. vol. 4633 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4633 LNCS, pp. 491-500, 4th International Conference on Image Analysis and Recognition, ICIAR 2007, Montreal, Canada, 07/8/22.

Block-based motion vector smoothing for periodic pattern region. / Sohn, Young Wook; Kang, Moon Gi.

Image Analysis and Recognition - 4th International Conference, ICIAR 2007, Proceedings. Vol. 4633 LNCS 2007. p. 491-500 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4633 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Sohn YW, Kang MG. Block-based motion vector smoothing for periodic pattern region. In Image Analysis and Recognition - 4th International Conference, ICIAR 2007, Proceedings. Vol. 4633 LNCS. 2007. p. 491-500. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).