1 / 5

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

anana
Download Presentation

Image feathering

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related