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Multiresolution Mean Shift Clustering Algorithm for Shape Interpolation
September/October 2009 (vol. 15 no. 5)
pp. 853-866
ASCII Text
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Hung-Kuo Chu, Tong-Yee Lee, "Multiresolution Mean Shift Clustering Algorithm for Shape Interpolation," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 5, pp. 853-866, September/October, 2009.
 
 
BibTex
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@article{ 10.1109/TVCG.2009.40,
author = {Hung-Kuo Chu and Tong-Yee Lee},
title = {Multiresolution Mean Shift Clustering Algorithm for Shape Interpolation},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {15},
number = {5},
issn = {1077-2626},
year = {2009},
pages = {853-866},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.40},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Multiresolution Mean Shift Clustering Algorithm for Shape Interpolation
IS - 5
SN - 1077-2626
SP853
EP866
EPD - 853-866
A1 - Hung-Kuo Chu,
A1 - Tong-Yee Lee,
PY - 2009
KW - Shape interpolation
KW - pose configuration
KW - multiresolution mean shift (MMS) clustering.
VL - 15
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Hung-Kuo Chu, National Cheng-Kung University, Tainan
Tong-Yee Lee, National Cheng-Kung University, Tainan
In this paper, we solve the problem of 3D shape interpolation with significant pose variation. For an ideal 3D shape interpolation, especially the articulated model, the shape should follow the movement of the underlying articulated structure and be transformed in a way that is as rigid as possible. Given input shapes with compatible connectivity, we propose a novel multiresolution mean shift (MMS) clustering algorithm to automatically extract their near-rigid components. Then, by building the hierarchical relationship among extracted components, we compute a common articulated structure for these input shapes. With the aid of this articulated structure, we solve the shape interpolation by combining 1) a global pose interpolation of near-rigid components from the source shape to the target shape with 2) a local gradient field interpolation for each pair of components, followed by solving a Poisson equation in order to reconstruct an interpolated shape. As a result, an aesthetically pleasing shape interpolation can be generated, with even the poses of shapes varying significantly. In contrast to a recent state-of-the-art work [CHECK END OF SENTENCE], the proposed approach can achieve comparable or even better results and have better computational efficiency as well.

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Index Terms:
Shape interpolation, pose configuration, multiresolution mean shift (MMS) clustering.
Citation:
Hung-Kuo Chu, Tong-Yee Lee, "Multiresolution Mean Shift Clustering Algorithm for Shape Interpolation," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 5, pp. 853-866, Sept.-Oct. 2009, doi:10.1109/TVCG.2009.40
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