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AAM based Face Tracking with Temporal Matching and Face Segmentation

CVPR 2010. AAM based Face Tracking with Temporal Matching and Face Segmentation. Mingcai Zhou 1 、 Lin Liang 2 、 Jian Sun 2 、 Yangsheng Wang 1. 1 Institute of Automation Chinese Academy of Sciences, Beijing, China. 2 Microsoft Research Asia Beijing, China. Problems- AAM tracker.

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AAM based Face Tracking with Temporal Matching and Face Segmentation

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  1. CVPR 2010 AAM based Face Tracking with Temporal Matching and Face Segmentation Mingcai Zhou1、 Lin Liang2、Jian Sun2、Yangsheng Wang1 1Institute of Automation Chinese Academy of Sciences, Beijing, China 2Microsoft Research Asia Beijing, China

  2. Problems- AAM tracker • Difficultly generalize to unseen images • Clutterd backgrounds

  3. How to do? • A temporal matching constraint in AAM fitting • Enforce an inter-frame local appearance constraint between frames • Introduce color-based face segmentation as a soft constraint

  4. Temporal Matching Constraint • Select feature points with salient local appearances at previous frame • I(t−1) to the Model coordinate and get the appearance A(t-1) • Use warping function W(x;pt) maps R(t-1) to a patch R(t) at frame t

  5. Shape Initialization Face Motion Direction t-1 t Those feature points whose motion directions are inconsistent with the main direction are most likely to be outliers. Improve the stability in tracking fast face motions

  6. Face Segmentation Constraint

  7. Face Segmentation Constraint

  8. Experiments

  9. PG 2009 Image and Video Abstraction byAnisotropic Kuwahara Filtering Jan Eric Kyprianidis1、Henry Kang2、Jürgen Döllner1 1Hasso-Plattner-Institut, Germany 2University of Missouri, St. Louis

  10. Features • preserving shape boundaries • exhibit directional information as found in oil paintings • use to video without extra processing

  11. Edge-Preserving Filter

  12. Edge-Preserving Filter Kuwahara filter [KHEK76] removes detail in high-contrast regions while also protecting shape boundaries in low-contrast regions.

  13. Block Artifacts

  14. Anisotropic Filter

  15. Method • Orientation and Anisotropy Estimation • Anisotropic Kuwahara Filter

  16. Experiments

  17. SIGGRAPH Asia 2009 Fast Motion Deblurring Sunghyun Cho、Seungyong Lee POSTECH

  18. Features • fast deblurring method

  19. Single Image Blind Deconvolution

  20. Prediction 1.Suppress noise: bilateral filter 2.Restore strong edge: shock filter 3.Gradient magnitude threshold Blurred image Image gradient maps

  21. Kernel Estimation Image gradient maps Minimize the energy function:

  22. Kernel Estimation CG method A size: (5n^2) x (m^2) , L:n x n ,K:m x m

  23. Kernel Estimation

  24. Kernel Estimation

  25. Deconvolution latent image

  26. Experiments

  27. Experiments

  28. SIGGRAPH Asia 2009 Noise Brush: Interactive High Quality Image-Noise Separation Jia Chen1、 Chi-Keung Tang1、Jue Wang2 1The Hong Kong University of Science and Technology 2Adobe Systems, Inc.

  29. Problems-denoising • Over-smoothed image structure • Residual noise in smooth regions

  30. Joint Image-Noise Filtering Purpose: spatial W大小 color Image structure

  31. Joint Image-Noise Filtering

  32. Result

  33. Joint Flash Nonflash Result Noisy Input Single Image Denoised by Noiseware Single Image Our Result

  34. Result

  35. SIGGRAPH 2008 Inverse Texture Synthesis Li-Yi Wei1、Jianwei Han2、 Kun Zhou1,2 Hujun Bao2、Baining Guo1、Harry Shum1 1Microsoft Research Asia 2Zhejiang University

  36. Flow Diagram New

  37. What is the mean of Inverse? • From a large input texture • produce a small output that best summarizes input inverse texture synthesis output input http://www.youtube.com/watch?v=w5HY2xMCldI

  38. Benefits • Reduce storage size • Increase processing speed 890^2X11 128^2X11

  39. Generate Compaction For globally varying texture:

  40. Generate Compaction

  41. Generate Compaction

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