Global non-rigid alignment of surface sequences

Budd, C, Huang, P, Klaudiny, M and Hilton, A (2013) Global non-rigid alignment of surface sequences International Journal of Computer Vision, 102 (1-3). pp. 256-270.

Preview	Text budd12ijcv.pdf Available under License : See the attached licence file. Download (2MB)
Preview	Text (licence) SRI_deposit_agreement.pdf Download (33kB)

Abstract

This paper presents a general approach based on the shape similarity tree for non-sequential alignment across databases of multiple unstructured mesh sequences from non-rigid surface capture. The optimal shape similarity tree for non-rigid alignment is defined as the minimum spanning tree in shape similarity space. Non-sequential alignment based on the shape similarity tree minimises the total non-rigid deformation required to register all frames in a database into a consistent mesh structure with surfaces in correspondence. This allows alignment across multiple sequences of different motions, reduces drift in sequential alignment and is robust to rapid non-rigid motion. Evaluation is performed on three benchmark databases of 3D mesh sequences with a variety of complex human and cloth motion. Comparison with sequential alignment demonstrates reduced errors due to drift and improved robustness to large non-rigid deformation, together with global alignment across multiple sequences which is not possible with previous sequential approaches. © 2012 The Author(s).

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Name Email ORCID Budd, C Huang, P Klaudiny, M Hilton, A
Date :	March 2013
Identification Number :	10.1007/s11263-012-0553-4
Additional Information :	This article is published under the Creative Commons Attribution license which allows users to read, copy distribute and make derivative works, as long as the author of the original work is cited.
Depositing User :	Symplectic Elements
Date Deposited :	08 Apr 2013 09:45
Last Modified :	31 Oct 2017 15:02
URI:	http://epubs.surrey.ac.uk/id/eprint/766024

Actions (login required)

View Item

Downloads