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层次化变分法用于稠密的视频运动分割 Peter Ochs and Thomas Brox University of Freiburg, Germany ICCV 2011

Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions. 层次化变分法用于稠密的视频运动分割 Peter Ochs and Thomas Brox University of Freiburg, Germany ICCV 2011. Thomas Brox, Professor in University of Freiburg, Germany Experience:

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层次化变分法用于稠密的视频运动分割 Peter Ochs and Thomas Brox University of Freiburg, Germany ICCV 2011

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  1. Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions 层次化变分法用于稠密的视频运动分割 Peter Ochs and Thomas Brox University of Freiburg, Germany ICCV 2011

  2. Thomas Brox, Professor in University of Freiburg, Germany Experience: Received PhD from Saarland University in 2005. 2005-2007 Post Doctor in Born University. 2007-2008 Temporary Professor in University of Dresden. 2008-2010 Post Doctor in UC Berkerley with J. Malik. Main Interests: Optical Flow, Segmentation, Human Motion Representative Work: Brox Optical Flow(ECCV’04 best paper) LDOF (PAMI’10) Segmentation (ECCV’10)

  3. Video Segmentation • Two Tasks • Shots Segmentation • Spatial-Temperal Cues Segmentation • Motion Segmentation

  4. Motion Segmentation • Optical flow based • Earlier Methods • Layers • Feature trajectory based • Most Popular in the last 10 years • Utilize 3D Motion • Related to Subspace Clustering • Hybrid methods using both motion and static cues

  5. Sparse Point Segmentation (1/6) • Optical Flow to obtain long-term point Trajectories Thomas Brox and Jitendra Malik, Object Segmentation by Long Term Analysis of Point Trajectories, ECCV 2010

  6. Sparse Point Segmentation (2/6) • Similarity Definition

  7. Sparse Point Segmentation (3/6) • Similarity Definition

  8. Sparse Point Segmentation (4/6) • Standard Spectral Clustering

  9. Sparse Point Segmentation (5/6) • Spectral Clustering with Spatial Regularity • Automatically determine cluster number

  10. Sparse Point Segmentation (6/6) • Main Contribution • Very Sparse Feature Points 0.01%-> Sparse points (3%)

  11. Motivation

  12. Single-Level Variational model +

  13. Solution (1/2) Euler-LagrangeEquation:

  14. Solution (2/2) Euler-LagrangeEquation: Successive over-relaxation: solve AX=B, where A = D - L - U by

  15. Multi-Level Variational model

  16. Multi-level Variational Model

  17. Why Multi-level continuous model? • Multi-level • Information can take a shortcut via a coarser level where this noise has been removed. • Continuous • Less block artifacts

  18. Solution Euler-LagrangeEquation: k>0 k=0

  19. Solution

  20. Qualitative Results

  21. Qualitative Results

  22. Quantitative Results

  23. Summary • Combining Motion Cues and Static Cues • Propose a Multi-level Variational Method

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