1. Line Matching Jonghee Park GIST CV-Lab.

2.  Lines –Fundamental feature in many computer vision fields 3D reconstruction, SLAM, motion estimation –Useful features in low-textured and man-made structure  Stereo Technology –Assume that depth discontinuity mainly occurs nearby edges –Applied vehicle, robot and aerial systems because of its low cost and depth information  To apply lines into practical stereo systems, computational complexity is critical issue for real-time performance. Introduction 2

3.  Line matching with pre-processing –Usually, point matching is conducted to find geometric relations between features and scenes Scale and rotation [CVPR12], fundamental matrix and tri-pocal tensor [CVPR97] –Point matching takes long time because of feature extraction in scale space and construction of HOG  Line matching without pre-processing –Grouping based matching Make groups or clusters lines for distinctive similarity measure according to the topological relation LS[ICCV09] takes long time because of intensive 2D search for making multiple clusters –Individual matching Match lines individually without topological relation MSLD[PR12] makes multiple HOG for a line Previous works 3

4. MSLD: A robust descriptor for line matching Zhiheng Wang, Fuchao Wu, and Zhanyi Hu National Lab. Of Pattern Recognition PR 2009 Jonghee Park GIST CV-Lab.

5. Pixel Support Region 5 Orientation

6.  Rotation relation of gradients between two images  Approximation  Weighting with gaussian kernel like SIFT  Interpolation along orientation direction for boundary effect Sub-region Representation 6 Gradient Distance from line

7.  Each sub-region has following 4 dimension feature vector  Gradient description matrix  To cover line length variantion Sub-region Representation 7

8. MSLD Matching result 8

9. Line Matching with Binary Costs Jonghee Park GIST CV-Lab.

10. Comparison 10

11. Matching Results 11