A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization

Abstract

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.

References in corpus (2)

• Spectral Clustering Based on Local PCA
• Foundations of a Multi-way Spectral Clustering Framework for Hybrid Linear Modeling

Cited by in corpus (7)

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• Riemannian Multi-Manifold Modeling
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• Better Feature Tracking Through Subspace Constraints
• Superpixel-guided Two-view Deterministic Geometric Model Fitting
• Motion Segmentation Using Locally Affine Atom Voting
• Automatic Segmentation of Dynamic Objects from an Image Pair