期刊
INTERNATIONAL JOURNAL OF COMPUTER VISION
卷 108, 期 3, 页码 165-185出版社
SPRINGER
DOI: 10.1007/s11263-013-0694-0
关键词
Global dimension; Empirical dimension; Subspace clustering; Hybrid-linear modeling; Motion segmentation; Outliers; Robust statistics
资金
- NSF [DMS-09-15064, DMS-09-56072]
- IMA
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0956072] Funding Source: National Science Foundation
We present a new approach to rigid-body motion segmentation from two views. We use a previously developed nonlinear embedding of two-view point correspondences into a 9-dimensional space and identify the different motions by segmenting lower-dimensional subspaces. In order to overcome nonuniform distributions along the subspaces, whose dimensions are unknown, we suggest the novel concept of global dimension and its minimization for clustering subspaces with some theoretical motivation. We propose a fast projected gradient algorithm for minimizing global dimension and thus segmenting motions from 2-views. We develop an outlier detection framework around the proposed method, and we present state-of-the-art results on outlier-free and outlier-corrupted two-view data for segmenting motion.
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