Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Tensor based sparse decomposition of 3D shape for visual detection of mirror symmetry
Author: Yin, X.
Ng, B.
Kotagiri, R.
Abbott, D.
Citation: Computer Methods and Programs in Biomedicine, 2012; 108(2):629-643
Publisher: Elsevier Sci Ireland Ltd
Issue Date: 2012
ISSN: 0169-2607
Statement of
X.-X. Yin, B.W.-H. Ng, K. Ramamohanarao, D. Abbott
Abstract: This study explores an approach for analysing the mirror (reflective) symmetry of 3D shapes with tensor based sparse decomposition. The approach combines non-negative tensor decomposition and directional texture synthesis, with symmetry information about 3D shapes that is represented by 2D textures synthesised from sparse, decomposed images. This technique requires the center of mass of 3D objects to be at the origin of the coordinate system. The decomposition of 3D shapes and analysis of their symmetry are useful for image compression, pattern recognition, as well as there being an emerging interest in the medical community due to its potential to find morphological changes between healthy and pathological structures. This paper postulates that sparse texture synthesis can be used to describe the decomposed basis images acting as symmetry descriptors for a 3D shape. We apply the theory of non-negative tensor decomposition and sparse texture synthesis, deduce the new representation, and show some application examples.
Keywords: Non-negative tensor decomposition; Texture synthesis; Sparse sampling; MRI; Symmetry detection
Rights: © 2011 Elsevier Ireland Ltd. All rights reserved.
RMID: 0020122550
DOI: 10.1016/j.cmpb.2011.10.007
Appears in Collections:Electrical and Electronic Engineering publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.