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Multibody Grouping by Inference of Multiple Subspaces from High-Dimensional Data Using Oriented-Frames
January 2006 (vol. 28 no. 1)
pp. 91-105
ASCII Text
x 
 
Zhimin Fan, Jie Zhou, Ying Wu, "Multibody Grouping by Inference of Multiple Subspaces from High-Dimensional Data Using Oriented-Frames," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 91-105, January, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2006.16,
author = {Zhimin Fan and Jie Zhou and Ying Wu},
title = {Multibody Grouping by Inference of Multiple Subspaces from High-Dimensional Data Using Oriented-Frames},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {28},
number = {1},
issn = {0162-8828},
year = {2006},
pages = {91-105},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.16},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Multibody Grouping by Inference of Multiple Subspaces from High-Dimensional Data Using Oriented-Frames
IS - 1
SN - 0162-8828
SP91
EP105
EPD - 91-105
A1 - Zhimin Fan,
A1 - Jie Zhou,
A1 - Ying Wu,
PY - 2006
KW - Index Terms- Computer vision
KW - motion segmentation
KW - subspace constraints.
VL - 28
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Zhimin Fan, IEEE Computer Society
Jie Zhou, IEEE
Ying Wu, IEEE
Recently, subspace constraints have been widely exploited in many computer vision problems such as multibody grouping. Under linear projection models, feature points associated with multiple bodies reside in multiple subspaces. Most existing factorization-based algorithms can segment objects undergoing independent motions. However, intersections among the correlated motion subspaces will lead most previous factorization-based algorithms to erroneous segmentation. To overcome this limitation, in this paper, we formulate the problem of multibody grouping as inference of multiple subspaces from a high-dimensional data space. A novel and robust algorithm is proposed to capture the configuration of the multiple subspace structure and to find the segmentation of objects by clustering the feature points into these inferred subspaces, no matter whether they are independent or correlated. In the proposed method, an Oriented-Frame (OF), which is a multidimensional coordinate frame, is associated with each data point indicating the point's preferred subspace configuration. Based on the similarity between the subspaces, novel mechanisms of subspace evolution and voting are developed. By filtering the outliers due to their structural incompatibility, the subspace configurations will emerge. Compared with most existing factorization-based algorithms that cannot correctly segment correlated motions, such as motions of articulated objects, the proposed method has a robust performance in both independent and correlated motion segmentation. A number of controlled and real experiments show the effectiveness of the proposed method. However, the current approach does not deal with transparent motions and motion subspaces of different dimensions.

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Index Terms:
Index Terms- Computer vision, motion segmentation, subspace constraints.
Citation:
Zhimin Fan, Jie Zhou, Ying Wu, "Multibody Grouping by Inference of Multiple Subspaces from High-Dimensional Data Using Oriented-Frames," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 91-105, Jan. 2006, doi:10.1109/TPAMI.2006.16
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