Journal
INTERNATIONAL JOURNAL OF COMPUTER VISION
Volume 108, Issue 3, Pages 165-185Publisher
SPRINGER
DOI: 10.1007/s11263-013-0694-0
Keywords
Global dimension; Empirical dimension; Subspace clustering; Hybrid-linear modeling; Motion segmentation; Outliers; Robust statistics
Categories
Funding
- NSF [DMS-09-15064, DMS-09-56072]
- IMA
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0956072] Funding Source: National Science Foundation
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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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