2013/04/30 by Bryan Poling, Gilad Lerman · 32 citations
Computer Science · Mathematics · #Advanced Vision and Imaging #Anomaly Detection Techniques and Applications #Artificial intelligence #Cluster analysis #Computer science #Computer vision #Dimension (graph theory) #Embedding #Geometry #Human Pose and Action Recognition #Linear subspace #Market segmentation #Mathematics #Motion (physics) #Outlier #Pattern recognition (psychology) #Segmentation #cs.CV
paper · pdf · doi:10.1007/s11263-013-0694-0
published in International Journal of Computer Vision 108(3), 165-185 (Springer Science+Business Media)
arxiv created 2014/01/07 · openalex publication_date 2014/01/13 · arxiv updated 2014/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.