Notes
Slide Show
Outline
1
Image feathering
  • Weight each image proportional to its distance from the edge
     (distance map [Danielsson, CVGIP 1980]



  • 1. Generate weight map for each image
  • 2. Sum up all of the weights and divide by sum:
    weights sum up to 1:  wi’ = wi / ( ∑i wi)
2
Pyramid Blending
3
 
4
Laplacian image blend
  • Compute Laplacian pyramid
  • Compute Gaussian pyramid on weight image (can put this in A channel)
  • Blend Laplacians using Gaussian blurred weights
  • Reconstruct the final image
  • Q: How do we compute the original weights?
  • A: For horizontal panorama, use mid-lines
  • Q: How about for a general “3D” panorama?
5
Weight selection (3D panorama)
  • Idea: use original feather weights to select
    strongest contributing image





  • Can be implemented using L-∞ norm: (p = 10)
  • wi’ = [wip / ( ∑i wip)]1/p