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Lecture 1 4

Lecture 1 4. Previous Works. A target is assumed to be Scaled, Rotated Version Template With Edges Distorted. Problem: Target Image Search. Search on Images Database. Target. Template. Inspiration Jain et al [1] , “Object Matching Using Deformable Templates” Our Algorithm

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Lecture 1 4

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  1. Lecture 14 Previous Works

  2. A target is assumed to be Scaled, Rotated Version Template With Edges Distorted Problem: Target Image Search Search on Images Database Target Template

  3. Inspiration Jain et al [1] , “Object Matching Using Deformable Templates” Our Algorithm Finding Hypotheses : MGHT Peak Clustering : Watershed Method Contour Matching : Smooth Membrane Fitting Methodology: An Overview Hypotheses 3 Peaks in 3D Hough Space Template Target Contours Rejected Hypothesis Rejected Hypothesis Accepted Hypothesis

  4. r1, a1, q1, l1 r2, a2, q2, l2 r3, a3, q3, l3 r4, a4, q4, l4 0...19 15,180,195,99 9,179,219,101 8,177,216,102 9,176,198,100 20...39 17,160,23,5 14,159,38,7 18,161,175,62 15,162,195,95 30…49 19,165,31,53 20,170,8,52 22,167,15,52 18,159,158,12 … … … … … 340...359 23,105,346,11 24,103,165,11 21,102,346,18 22,104,195,24 Finding Candidate Target:Modified Generalized Hough • [Nimkerdpol and Madarasmi, 2001] • A line at the contour edge is extended in the g direction until it meets the other end of the contour • In MGHT, the relation between Dq  (r,a,q,l) is stored as a linked list in R-Table, not as q  (a,r) like in GHT

  5.  = 30-200 = -170 = 190  = 300-110 = 190 MGHT: Rotation/Scale

  6. MGHT xc = x + S r cos (a + b) yc = y + S r sin (a + b) Rotation Factor: Scaling Factor : New ref. Point :

  7. Watershed for Peak Clustering 1. Shed, by labeling, at the first level, calculate peaks of each label 2. Increase to higher level, shed again 2.1 Meet an area of previous level, shed to that area 2.2 Not meet any area of previous level, make a new area , calculate a new peak

  8. Parameter : (Dx,Dy) or (u,v)range -7, -6, …,0,… 6, 7 Deformation : Contour Matching

  9. Coarse and Fine Matching

  10. Energy Function Matching Algorithm Update (u,v) :Gibbs Sampler with simulated annealing Template Target Edge

  11. Experiment on Image Search Template Target Edge Map Result Hough Space

  12. Experiment on Image Search 1st Match Hypotheses Target Edge 2nd Match 3rd Match 4th Match

  13. Experiment on Image Search Template Target Edge Map Hough Space The Best Match

  14. Threshold Selection: Guitar 1 2.705226 0.929011 3.986274 Template Target Edge Hypotheses Threshold : 1.0 - 2.6

  15. Threshold Selection: Guitar 1 Template Target Edge Hypotheses Threshold : 1.0-1.6 1.755835 2.165488 0.965049

  16. Threshold Selection: Vase 1 Template Hypotheses Edge Map Threshold : 1.2-1.6 5.074061 1.799267 1.114566

  17. Threshold Selection: Vase 2 Template Target Edge Hypotheses 0.868600 0.879799 3.799124 Threshold : 0.9-3.6

  18. Threshold Selection: Vase 3 Threshold : 1.5-3.2 Template Target Edge Hypotheses 1.293034 1.452130 3.364521 4.4185782

  19. Energy Threshold

  20. Experiment on Image Querying Database Search for Circle shape

  21. Experiment on Image Querying Search for bulb shape Database

  22. Conclusion • A deformation model • Contour Matching • A method for image search • Future work: large image database, efficient method for minimizing energy, coarse-and-fine approach to computer vison modules

  23. Accurate 3-D surface map using stereo vision • This proposal research addresses 2 issues: • Find an accurate 3-D surface map using stereo vision • Combine surface from different views to a single 3-D object for CAD applications.

  24. Combine surface from multiple views to a single 3-D object. To combine multiple view, we need to find the rotation and transformation matrices from each camera combined to the world or reference co-ordination system. ================== Rotation : 0.71220.70190.0130 Rotation : 0.0386-0.0207-0.9990 Rotation : -0.70100.7120-0.0418 Translation : 16.6342 Translation : 32.6633 Translation : 181.5649 ==================

  25. Expect Result After Combine Multiple View

  26. A Relaxation Method for Shape from Contours • Input Contour Images: • Geodesics Contours Only • Developable Surface (No Folds/Twists) • Non-Accidental View • Place Grid Points in X and Y Direction to have Smooth • Draw Horizontal and Vertical Lines through Grid to Form a Regular Square Texture • Use Shape from Texture to Obtained Surface Normals

  27. Experiment 2 Original Step 1 Step 2 Step 3

  28. Shape from Contour

  29. Shape from Contour

  30. Shape from Contour

  31. Shape from Contour

  32. Shape from Contour

  33. Shape from Contour

  34. Shape from Contour

  35. Shape from Contour

  36. Contour Search

  37. Contour Search

  38. Contour Search

  39. Contour Matching

  40. Paper Inspection

  41. Multi-Layer Stereo

  42. Multi-Layer Stereo

  43. Multi-View Stereo

  44. Multi-View Stereo

  45. Rubber Sheet Inspection

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