Graph rigidity for near-coplanar structure from motion
Contribuinte(s) |
Bradley, Andrew Jackway, Paul Gal, Yaniv Salvado, Olivier |
---|---|
Data(s) |
01/12/2011
|
Resumo |
Recent algorithms for monocular motion capture (MoCap) estimate weak-perspective camera matrices between images using a small subset of approximately-rigid points on the human body (i.e. the torso and hip). A problem with this approach, however, is that these points are often close to coplanar, causing canonical linear factorisation algorithms for rigid structure from motion (SFM) to become extremely sensitive to noise. In this paper, we propose an alternative solution to weak-perspective SFM based on a convex relaxation of graph rigidity. We demonstrate the success of our algorithm on both synthetic and real world data, allowing for much improved solutions to marker less MoCap problems on human bodies. Finally, we propose an approach to solve the two-fold ambiguity over bone direction using a k-nearest neighbour kernel density estimator. |
Formato |
application/pdf |
Identificador | |
Publicador |
IEEE Computer Society Conference Publishing Services (CPS) |
Relação |
http://eprints.qut.edu.au/48169/1/06128707.pdf http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6128707 Valmadre, Jack, Upcroft, Ben, Sridharan, Sridha, & Lucey, Simon (2011) Graph rigidity for near-coplanar structure from motion. In Bradley, Andrew, Jackway, Paul, Gal, Yaniv, & Salvado, Olivier (Eds.) Proceedings of the 2011 International Conference on Digital Image Computing: Techniques and Applications, IEEE Computer Society Conference Publishing Services (CPS), Australia, pp. 480-486. |
Direitos |
Copyright © 2011 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved. |
Fonte |
Faculty of Built Environment and Engineering; School of Engineering Systems |
Palavras-Chave | #080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING #Coplanar #Graph rigidity #Human #Non-rigid #Structure from motion #Torso #Transmission line matrix methods #Three dimensional displays #Image edge detection |
Tipo |
Conference Paper |