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Augmented Lagrangian-based approach for dense three-dimensional structure and motion estimation from binocular image sequences. / De Cubber, Geert; Sahli, Hichem.

In: IET Computer Vision, Vol. 8, No. 2, 2013, p. 98-109.

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@article{455cd65f6ee84b8a8b0e7c1d0a0acb58,
title = "Augmented Lagrangian-based approach for dense three-dimensional structure and motion estimation from binocular image sequences",
abstract = "In this study, the authors propose a framework for stereo–motion integration for dense depth estimation. They formulate the stereo–motion depth reconstruction problem into a constrained minimisation one. A sequential unconstrained minimisation technique, namely, the augmented Lagrange multiplier (ALM) method has been implemented to address the resulting constrained optimisation problem. ALM has been chosen because of its relative insensitivity to whether the initial design points for a pseudo-objective function are feasible or not. The development of the method and results from solving the stereo–motion integration problem are presented. Although the authors work is not the only one adopting the ALMs framework in the computer vision context, to thier knowledge the presented algorithm is the first to use this mathematical framework in a context of stereo–motion integration. This study describes how the stereo–motion integration problem was cast in a mathematical context and solved using the presented ALM method. Results on benchmark and real visual input data show the validity of the approach.",
keywords = "computer vision, 3D reconstruction, stereo, motion",
author = "{De Cubber}, Geert and Hichem Sahli",
year = "2013",
language = "English",
volume = "8",
pages = "98--109",
journal = "IET Computer Vision",
issn = "1751-9632",
publisher = "Institution of Engineering and Technology",
number = "2",

}

RIS

TY - JOUR

T1 - Augmented Lagrangian-based approach for dense three-dimensional structure and motion estimation from binocular image sequences

AU - De Cubber, Geert

AU - Sahli, Hichem

PY - 2013

Y1 - 2013

N2 - In this study, the authors propose a framework for stereo–motion integration for dense depth estimation. They formulate the stereo–motion depth reconstruction problem into a constrained minimisation one. A sequential unconstrained minimisation technique, namely, the augmented Lagrange multiplier (ALM) method has been implemented to address the resulting constrained optimisation problem. ALM has been chosen because of its relative insensitivity to whether the initial design points for a pseudo-objective function are feasible or not. The development of the method and results from solving the stereo–motion integration problem are presented. Although the authors work is not the only one adopting the ALMs framework in the computer vision context, to thier knowledge the presented algorithm is the first to use this mathematical framework in a context of stereo–motion integration. This study describes how the stereo–motion integration problem was cast in a mathematical context and solved using the presented ALM method. Results on benchmark and real visual input data show the validity of the approach.

AB - In this study, the authors propose a framework for stereo–motion integration for dense depth estimation. They formulate the stereo–motion depth reconstruction problem into a constrained minimisation one. A sequential unconstrained minimisation technique, namely, the augmented Lagrange multiplier (ALM) method has been implemented to address the resulting constrained optimisation problem. ALM has been chosen because of its relative insensitivity to whether the initial design points for a pseudo-objective function are feasible or not. The development of the method and results from solving the stereo–motion integration problem are presented. Although the authors work is not the only one adopting the ALMs framework in the computer vision context, to thier knowledge the presented algorithm is the first to use this mathematical framework in a context of stereo–motion integration. This study describes how the stereo–motion integration problem was cast in a mathematical context and solved using the presented ALM method. Results on benchmark and real visual input data show the validity of the approach.

KW - computer vision

KW - 3D reconstruction

KW - stereo

KW - motion

M3 - Article

VL - 8

SP - 98

EP - 109

JO - IET Computer Vision

JF - IET Computer Vision

SN - 1751-9632

IS - 2

ER -

ID: 2428825