Geometrical scene analysis using co-motion statistics

Zoltán Szlávik, László Havasi, Tamás Szirányi

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

5 Citations (Scopus)

Abstract

Deriving the geometrical features of an observed scene is pivotal for better understanding and detection of events in recorded videos. In the paper methods are presented for the estimation of various geometrical scene characteristics. The estimated characteristics are: point correspondences in stereo views, mirror pole, light source and horizon line. The estimation is based on the analysis of dynamical scene properties by using co-motion statistics. Various experiments prove the feasibility of our approach.

Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 9th International Conference, ACIVS 2007, Proceedings
PublisherSpringer Verlag
Pages968-979
Number of pages12
ISBN (Print)9783540746065
DOIs
Publication statusPublished - Jan 1 2007
Event9th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2007 - Delft, Netherlands
Duration: Aug 28 2007Aug 31 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4678 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2007
CountryNetherlands
CityDelft
Period8/28/078/31/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Geometrical scene analysis using co-motion statistics'. Together they form a unique fingerprint.

  • Cite this

    Szlávik, Z., Havasi, L., & Szirányi, T. (2007). Geometrical scene analysis using co-motion statistics. In Advanced Concepts for Intelligent Vision Systems - 9th International Conference, ACIVS 2007, Proceedings (pp. 968-979). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4678 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-74607-2_88