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Optimal Estimation of Matching Constraints

Bill Triggs 1
1 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : We describe work in progress on a numerical library for estimating multi-image matching constraints, or more precisely the multi-camera geometry underlying them. The library will cover several variants of homographic, epipolar, and trifocal constraints, using various different feature types. It is designed to be modular and open-ended, so that (i) new feature types or error models, (ii) new constraint types or parametrizations, and (iii) new numerical resolution methods, are relatively easy to add. The ultimate goal is to provide practical code for stable, reliable, statistically optimal estimation of matching geometry under a choice of robust error models, taking full account of any nonlinear constraints involved. More immediately, the library will be used to study the relative performance of the various competing problem parametrizations, error models and numerical methods. The paper focuses on the overall design, parametrization and numerical optimization issues. The methods described extend to many other geometric estimation problems in vision, e.g. curve and surface fitting.
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https://hal.inria.fr/inria-00548324
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Submitted on : Monday, December 20, 2010 - 8:43:21 AM
Last modification on : Monday, December 28, 2020 - 3:44:02 PM
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Bill Triggs. Optimal Estimation of Matching Constraints. Workshop on 3D Structure from Multiple Images of Large-scale Environments (SMILE), Jun 1998, Freiburg, Germany. pp.63--77, ⟨10.1007/3-540-49437-5_5⟩. ⟨inria-00548324⟩

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