A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Multi-frame compression: theory and design
Signal Processing - Special section on signal processing technologies for short burst wireless communications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate and Scalable Surface Representation and Reconstruction from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hemispherical Harmonic Surface Description and Applications to Medical Image Analysis
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
A Spatio-Temporal Modeling Method for Shape Representation
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
A general framework for surface modeling using geometric partial differential equations
Computer Aided Geometric Design
In vivo tracking of 3D organs using spherical harmonics and subspace clustering
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Learning sparse representation using iterative subspace identification
IEEE Transactions on Signal Processing
Real time tracking of 3D organ surfaces using single MR image and limited optical viewing
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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This paper proposed a novel algorithm to sparsely represent a deformable surface (SRDS) with low dimensionality based on spherical harmonic decomposition (SHD) and orthogonal subspace pursuit (OSP). The key idea in SRDS method is to identify the subspaces from a training data set in the transformed spherical harmonic domain and then cluster each deformation into the best-fit subspace for fast and accurate representation. This algorithm is also generalized into applications of organs with both interior and exterior surfaces. To test the feasibility, we first use the computer models to demonstrate that the proposed approach matches the accuracy of complex mathematical modeling techniques and then both ex vivo and in vivo experiments are conducted using 3D magnetic resonance imaging (MRI) scans for verification in practical settings. All results demonstrated that the proposed algorithm features sparse representation of deformable surfaces with low dimensionality and high accuracy. Specifically, the precision evaluated as maximum error distance between the reconstructed surface and the MRI ground truth is better than 3mm in real MRI experiments.