Location of Repository

MVMP: MULTI-VIEW MATCHING PURSUIT WITH GEOMETRY CONSTRAINTS

By Antonio Ortega and Pascal Frossard

Abstract

Sets of multi-view images that capture plenoptic information from different viewpoints are typically related by geometric constraints. The proper analysis of these constraints is key to the definition of consistent compact representations of such images. We propose an algorithm for joint sparse approximation of multi-view images driven by epipolar geometry considerations. We extend greedy pursuit algorithms, such that the representation of multi-view images into linear combination of geometric atoms is able to balance approximation error and geometric consistency. We further add a rate penalty constraint that favors representations with small entropy towards efficient coding applications. Experimental results illustrate the trade-off between approximation, geometry and rate constraints in the representation of stereo omnidirectional images. In particular, we show that geometry constraints lead to a consistent description of the correlation among views, which is particularly beneficial for scene analysis or view interpolation applications. At the same time, we show that the rate constraint leads to compact representations, possibly to the detriment of geometry consistency. 1

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.188.2059
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://infoscience.epfl.ch/rec... (external link)
  • http://infoscience.epfl.ch/rec... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.