Monocular Model-Based 3D Tracking of Rigid Objects: A Survey<br />2008. 12. 15.백운혁<br />Chapter 4. Natural Features, Model...
Agenda<br />Monocular Model-Based 3D Tracking of Rigid Objects : A Survey<br />Chapter 4. Natural Features, Model-Based Tr...
4.1 Edge-Based Methods<br />straight line segments and to fit the model outlines<br />
4.1.1 RAPiD<br />
4.1.1 RAPiD<br />Origin<br />Control point<br />Control point in camera coordinates<br />Motion<br />
4.1.1 RAPiD<br />
4.1.1 RAPiD<br />distance<br />is vector made of the distances<br />
4.1.2 Making RAPiD Robust<br />Minimize the distance<br />Control points lying on the same object edge are grouped into pr...
4.1.3 Explicit Edge Extraction<br />The middle point, the orientation and the length of the segment<br />Of a  model segme...
4.2 Optical Flow-Based Methods<br />Its corresponding location in the next image<br />The projection of a point in an imag...
4.2.1 Using Optical Flow Alone<br />Normal optical flow<br />For large motions<br />Causes error accumulation<br />
4.2.2 Combining Optical Flow and Edges<br />To avoid error accumulation<br />Depends of the pose         and the image spa...
4.3 Template Matching<br />To register a 2D template to an image under a family of deformations<br />
4.3.1 2D Tracking<br />To find the parameters        of some deformation     <br />That warps a template         into the ...
4.4 Interest Point-Based Methods<br />Use localized features<br />Rely on matching individual features across images and a...
4.4.1 Interest Point Detection<br />Harris-Stephen detector / Shi-Tomasi detector<br />The pixels can be classified from t...
4.4.2 Interest Point Matching<br />to use7x7 correlation windows<br />reject matches for which measure is less than 0.8<br...
4.4.3 Pose Estimation by Tracking Planes<br />Pose Estimation for Planar Structures<br />
Thanks for your attention<br />
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3d tracking : chapter4 natural features, model-based tracking

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3d tracking : chapter4 natural features, model-based tracking

  1. 1. Monocular Model-Based 3D Tracking of Rigid Objects: A Survey<br />2008. 12. 15.백운혁<br />Chapter 4. Natural Features, Model-Based Tracking<br />
  2. 2.
  3. 3. Agenda<br />Monocular Model-Based 3D Tracking of Rigid Objects : A Survey<br />Chapter 4. Natural Features, Model-Based Tracking<br />4.1. Edge-Based Methods<br />4.2. Optical Flow-Based Methods<br />4.3. Template Matching<br />4.4. Interest Point-Based Methods<br />4.5. Tracking Without 3D Models<br />
  4. 4. 4.1 Edge-Based Methods<br />straight line segments and to fit the model outlines<br />
  5. 5. 4.1.1 RAPiD<br />
  6. 6. 4.1.1 RAPiD<br />Origin<br />Control point<br />Control point in camera coordinates<br />Motion<br />
  7. 7. 4.1.1 RAPiD<br />
  8. 8. 4.1.1 RAPiD<br />distance<br />is vector made of the distances<br />
  9. 9. 4.1.2 Making RAPiD Robust<br />Minimize the distance<br />Control points lying on the same object edge are grouped into primitives. And a whole primitive can be rejected from the pose estimation.<br />RANSAC methodology<br />The number of edge strength maxima visible<br />
  10. 10. 4.1.3 Explicit Edge Extraction<br />The middle point, the orientation and the length of the segment<br />Of a model segment<br />Of a an extracted segment<br />Mahalanobis distance<br />Is the covariance matrix<br />The pose is then estimated by minimizing<br />
  11. 11. 4.2 Optical Flow-Based Methods<br />Its corresponding location in the next image<br />The projection of a point in an image at time<br />
  12. 12. 4.2.1 Using Optical Flow Alone<br />Normal optical flow<br />For large motions<br />Causes error accumulation<br />
  13. 13. 4.2.2 Combining Optical Flow and Edges<br />To avoid error accumulation<br />Depends of the pose and the image spatial gradients at time<br />Is a vector made of the temporal gradient at the chosen locations<br />
  14. 14. 4.3 Template Matching<br />To register a 2D template to an image under a family of deformations<br />
  15. 15. 4.3.1 2D Tracking<br />To find the parameters of some deformation <br />That warps a template into the input image<br /> is the pseudo-inverse of the Jacobian matrix of computed at <br />
  16. 16. 4.4 Interest Point-Based Methods<br />Use localized features<br />Rely on matching individual features across images and are therefore easy to robustify against partial occlusions or matching errors<br />
  17. 17. 4.4.1 Interest Point Detection<br />Harris-Stephen detector / Shi-Tomasi detector<br />The pixels can be classified from the behavior of the eigen values of <br />The coefficients of are the sums over a window<br />of the first derivatives and of image intensities<br />with respect to pixel coordinates<br />
  18. 18. 4.4.2 Interest Point Matching<br />to use7x7 correlation windows<br />reject matches for which measure is less than 0.8<br />search of correspondents for a maximum movement of 50 pixels<br />Kanade-Lucas-Tomasi tracker<br />Keep the points that choose each other<br />
  19. 19. 4.4.3 Pose Estimation by Tracking Planes<br />Pose Estimation for Planar Structures<br />
  20. 20. Thanks for your attention<br />

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