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Image feathering

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: w i ’ = w i / ( ∑ i w i ). Pyramid Blending. Laplacian

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Image feathering

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  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 / ( ∑iwi) CSE 576 (Spring 2005): Computer Vision

  2. Pyramid Blending CSE 576 (Spring 2005): Computer Vision

  3. Laplacian level 4 Laplacian level 2 Laplacian level 0 CSE 576 (Spring 2005): Computer Vision left pyramid right pyramid blended pyramid

  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? CSE 576 (Spring 2005): Computer Vision

  5. Weight selection (3D panorama) • Idea: use original feather weights to selectstrongest contributing image • Can be implemented using L-∞ norm: (p = 10) • wi’ = [wip / ( ∑iwip)]1/p CSE 576 (Spring 2005): Computer Vision

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