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Joint estimation of segmentation and structure from motion

We present a novel optimisation framework for the estimation of the multi-body motion segmentation and 3D reconstruction of a set of point trajectories in the presence of missing data. The proposed solution not only assigns the trajectories to the correct motion but it also solves for the 3D location of multi-body shape and it fills the missing entries in the measurement matrix. Such a solution is based on two fundamental principles: each of the multi-body motions is controlled by a set of metric constraints that are given by the specific camera model, and the shape matrix that describes the multi-body 3D shape is generally sparse. We jointly include such constraints in a unique optimisation framework which, starting from an initial segmentation, iteratively enforces these set of constraints in three stages. First, metric constraints are used to estimate the 3D metric shape and to fill the missing entries according to an orthographic camera model. Then, wrongly segmented trajectories are detected by using sparse optimisation of the shape matrix. A final reclassification strategy assigns the detected points to the right motion or discards them as outliers. We provide experiments that show consistent improvements to previous approaches both on synthetic and real data

This work has been supported by the Spanish Ministry of Science and Innovation projects CTM2011-29691-C02-02. L. Zappella was supported by the Catalan government scholarship 2009FI B1 00068

© Computer Vision and Image Understanding, 2013, vol. 117, núm. 2, p. 113-129

Elsevier

Autor: Zappella, Luca
Del Bue, Alessio
Lladó Bardera, Xavier
Salvi, Joaquim
Data: 2013
Resum: We present a novel optimisation framework for the estimation of the multi-body motion segmentation and 3D reconstruction of a set of point trajectories in the presence of missing data. The proposed solution not only assigns the trajectories to the correct motion but it also solves for the 3D location of multi-body shape and it fills the missing entries in the measurement matrix. Such a solution is based on two fundamental principles: each of the multi-body motions is controlled by a set of metric constraints that are given by the specific camera model, and the shape matrix that describes the multi-body 3D shape is generally sparse. We jointly include such constraints in a unique optimisation framework which, starting from an initial segmentation, iteratively enforces these set of constraints in three stages. First, metric constraints are used to estimate the 3D metric shape and to fill the missing entries according to an orthographic camera model. Then, wrongly segmented trajectories are detected by using sparse optimisation of the shape matrix. A final reclassification strategy assigns the detected points to the right motion or discards them as outliers. We provide experiments that show consistent improvements to previous approaches both on synthetic and real data
This work has been supported by the Spanish Ministry of Science and Innovation projects CTM2011-29691-C02-02. L. Zappella was supported by the Catalan government scholarship 2009FI B1 00068
Format: application/pdf
ISSN: 1077-3142
Accés al document: http://hdl.handle.net/10256/11544
Llenguatge: eng
Editor: Elsevier
Col·lecció: MICINN/PN 2012-2014/CTM2011-29691-C02-02
Reproducció digital del document publicat a: http://dx.doi.org/10.1016/j.cviu.2012.09.004
Articles publicats (D-ATC)
És part de: © Computer Vision and Image Understanding, 2013, vol. 117, núm. 2, p. 113-129
Drets: Tots els drets reservats
Matèria: Imatges -- Processament
Image processing
Visió per ordinador
Computer vision
Títol: Joint estimation of segmentation and structure from motion
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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