LOCAL MINIMUM DISTANCE FOR THE DENSE DISPARITY ESTIMATION

Eric Alvernhe, Philippe Montesinos, Stefan Janaqi, Min Tang

2006

Abstract

This paper presents a new algorithm to solve the problem of dense disparity map estimation in stereo-vision. Our method is an iterative process inspired by variationnal approach. A new criteria is used as the attachment term based on the distance to local minimum of a similarity measure. Our iterative process is heuristic. Nevertheless, we are able to interpret this algorithm presenting both combinatorial and continuous characteristics. The quality and precision of the results obtained by our method both on image benchmarks and real data clearly demonstrate the the validity of this approach.

Paper Citation

in Harvard Style

Alvernhe E., Montesinos P., Janaqi S. and Tang M. (2006). LOCAL MINIMUM DISTANCE FOR THE DENSE DISPARITY ESTIMATION . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 972-8865-40-6, pages 341-348. DOI: 10.5220/0001369803410348

in Bibtex Style

@conference{visapp06,
author={Eric Alvernhe and Philippe Montesinos and Stefan Janaqi and Min Tang},
title={LOCAL MINIMUM DISTANCE FOR THE DENSE DISPARITY ESTIMATION},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2006},
pages={341-348},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001369803410348},
isbn={972-8865-40-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - LOCAL MINIMUM DISTANCE FOR THE DENSE DISPARITY ESTIMATION
SN - 972-8865-40-6
AU - Alvernhe E.
AU - Montesinos P.
AU - Janaqi S.
AU - Tang M.
PY - 2006
SP - 341
EP - 348
DO - 10.5220/0001369803410348