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Automatic Panoramic Image Stitching using Local Features

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makaio
61 diapositives · 1K Views · Aug 17, 2014

Automatic Panoramic Image Stitching using Local Features. Matthew Brown and David Lowe, University of British Columbia. Introduction. Are you getting the whole picture? Compact Camera FOV = 50 x 35°. Introduction. Are you getting the whole picture? Compact Camera FOV = 50 x 35°

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  1. Automatic Panoramic Image Stitching using Local Features Matthew Brown and David Lowe, University of British Columbia

  2. Introduction • Are you getting the whole picture? • Compact Camera FOV = 50 x 35°

  3. Introduction • Are you getting the whole picture? • Compact Camera FOV = 50 x 35° • Human FOV = 200 x 135°

  4. Introduction • Are you getting the whole picture? • Compact Camera FOV = 50 x 35° • Human FOV = 200 x 135° • Panoramic Mosaic = 360 x 180°

  5. Why Use Local Features?

  6. Why Use Local Features? • 1D Rotations (q) • Ordering  matching images

  7. Why Use Local Features? • 1D Rotations (q) • Ordering  matching images

  8. Why Use Local Features? • 1D Rotations (q) • Ordering  matching images

  9. 2D Rotations (q, f) • Ordering  matching images Why Use Local Features? • 1D Rotations (q) • Ordering  matching images

  10. 2D Rotations (q, f) • Ordering  matching images Why Use Local Features? • 1D Rotations (q) • Ordering  matching images

  11. 2D Rotations (q, f) • Ordering  matching images Why Use Local Features? • 1D Rotations (q) • Ordering  matching images

  12. Recognising Panoramas [ Brown and Lowe ICCV 2003, IJCV 2007 ]

  13. Automatic Stitching • Feature Matching • Image Matching • Image Alignment • Rendering • Results • Conclusions

  14. Automatic Stitching • Feature Matching • SIFT Features • Nearest Neighbour Matching • Image Matching • Image Alignment • Rendering • Results • Conclusions

  15. Invariant Features • Schmid & Mohr 1997, Lowe 1999, Baumberg 2000, Tuytelaars & Van Gool 2000, Mikolajczyk & Schmid 2001, Brown & Lowe 2002, Matas et. al. 2002, Schaffalitzky & Zisserman 2002

  16. SIFT Features • Invariant Features • Establish invariant frame • Maxima/minima of scale-space DOG  x, y, s • Maximum of distribution of local gradients q • Form descriptor vector • Histogram of smoothed local gradients • 128 dimensions • SIFT features are… • Geometrically invariant to similarity transforms, • some robustness to affine change • Photometrically invariant to affine changes in intensity

  17. Automatic Stitching • Feature Matching • SIFT Features • Nearest Neighbour Matching • Image Matching • Image Alignment • Rendering • Results • Conclusions

  18. Nearest Neighbour Matching • Nearest neighbour matching • Use k-d tree • k-d tree recursively bi-partitions data at mean in the dimension of maximum variance • Approximate nearest neighbours found in O(n log n) • Find k-NN for each feature • k  number of overlapping images (we use k = 4) [ Beis Lowe 1997, Nene Nayar 1997, Gray Moore 2000, Shakhnarovich 2003 ]

  19. K-d tree

  20. K-d tree

  21. Automatic Stitching • Feature Matching • Image Matching • Motion Model • RANSAC • Image Alignment • Rendering • Results • Conclusions

  22. 2D Motion Models • Linear (affine) • Homography

  23. Homography for Rotation • Projection equation • → pairwise homographies set t = 0 for a pair where

  24. Automatic Stitching • Feature Matching • Image Matching • Motion Model • RANSAC • Image Alignment • Rendering • Results • Conclusions

  25. RANSAC for Homography

  26. RANSAC for Homography

  27. RANSAC for Homography

  28. least squares line RANSAC: 1D Line Fitting

  29. RANSAC: 1D Line Fitting

  30. RANSAC line RANSAC: 1D Line Fitting

  31. Match Verification

  32. Finding the panoramas

  33. Finding the panoramas

  34. Finding the panoramas

  35. Finding the panoramas

  36. Automatic Stitching • Feature Matching • Image Matching • Image Alignment • Bundle Adjustment • Automatic Straightening • Rendering • Results • Conclusions

  37. Motion Model Revisited • Recall our image motion model • Parameterise each camera by rotation and focal length

  38. Bundle Adjustment • Sum of squared projection errors • n = #images • I(i) = set of image matches to image i • F(i, j) = set of feature matches between images i,j • rijk = residual of kth feature match between images i,j • Huber (robust) error function

  39. Bundle Adjustment • Adjust rotation, focal length of each image to minimise error in matched features

  40. Bundle Adjustment • Adjust rotation, focal length of each image to minimise error in matched features

  41. Automatic Stitching • Feature Matching • Image Matching • Image Alignment • Bundle Adjustment • Automatic Straightening • Rendering • Results • Conclusions

  42. Automatic Straightening

  43. Automatic Straightening • Heuristic: user does not twist camera relative to horizon • Up-vector perpendicular to plane of camera x vectors

  44. Automatic Straightening

  45. Automatic Stitching • Feature Matching • Image Matching • Image Alignment • Rendering • Results • Conclusions

  46. Gain Compensation • No gain compensation

  47. Gain Compensation • Gain compensation • Single gain parameter gi for each image ( Better solution = HDR [ Debevec 1997 ] )

  48. Multi-band Blending • No blending

  49. Multi-band Blending • Linear blending • Each pixel is a weighted sum

  50. Multi-band Blending • Multi-band blending • Each pixel is a weighted sum (for each band)