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Image Primitives and Correspondence

Image Primitives and Correspondence. Jana Kosecka George Mason University. Image Primitives and Correspondence. Given an image point in left image, what is the (corresponding) point in the right image, which is the projection of the same 3-D point . Matching - Correspondence.

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Image Primitives and Correspondence

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  1. Image Primitives and Correspondence Jana Kosecka George Mason University

  2. Image Primitives and Correspondence Given an image point in left image, what is the (corresponding) point in the right image, which is the projection of the same 3-D point ICRA 2003

  3. Matching - Correspondence Lambertian assumption Rigid body motion Correspondence ICRA 2003

  4. Local Deformation Models • Translational model • Affine model • Transformation of the intensity values and occlusions ICRA 2003

  5. Feature Tracking and Optical Flow • Translational model • Small baseline • RHS approx. by first two terms of Taylor series • Brightness constancy constraint ICRA 2003

  6. Aperture Problem • Normal flow ICRA 2003

  7. Optical Flow • Integrate around over image patch • Solve ICRA 2003

  8. Optical Flow, Feature Tracking Conceptually: rank(G) = 0 blank wall problem rank(G) = 1 aperture problem rank(G) = 2 enough texture – good feature candidates In reality: choice of threshold is involved ICRA 2003

  9. Optical Flow • Previous method - assumption locally constant flow • Alternative regularization techniques (locally smooth flow fields, • integration along contours) • Qualitative properties of the motion fields ICRA 2003

  10. Feature Tracking ICRA 2003

  11. 3D Reconstruction - Preview ICRA 2003

  12. Point Feature Extraction • Compute eigenvalues of G • If smalest eigenvalue  of G is bigger than  - mark pixel as candidate • feature point • Alternatively feature quality function (Harris Corner Detector) ICRA 2003

  13. Harris Corner Detector - Example ICRA 2003

  14. Wide Baseline Matching ICRA 2003

  15. Region based Similarity Metric • Sum of squared differences • Normalize cross-correlation • Sum of absolute differences ICRA 2003

  16. Edge Detection original image gradient magnitude Canny edge detector • Compute image derivatives • if gradient magnitude >  and the value is a local maximum along gradient • direction – pixel is an edge candidate ICRA 2003

  17. y   x Line fitting Non-max suppressed gradient magnitude • Edge detection, non-maximum suppression • (traditionally Hough Transform – issues of resolution, threshold • selection and search for peaks in Hough space) • Connected components on edge pixels with similar orientation • - group pixels with common orientation ICRA 2003

  18. Line Fitting second moment matrix associated with each connected component • Line fitting Lines determined from eigenvalues and eigenvectors of A • Candidate line segments - associated line quality ICRA 2003

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