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Discovering Panoramas in Web Videos
ACM Multimedia 2008Feng Liu1, Yuhen-Hu1,2 and Michael Gleicher1
Introduction Video analysis Discovering panoramas Panorama synthesis Experiments Conclusion
Outline
STEP 1 image alignment
STEP2 image stitching
Creating panoramic imagery
Not all video has appropriate sources◦ Not cover a wide field-of-view of a scene◦ Motion may be randomly◦ Image quality
Image sources in a video
Three parts◦ video analysis◦ panorama source selection◦ panorama synthesis
Purpose-Discover Panoramas in Video
Background-image homography
transformation
Feature matching – SIFT Compute homography parameters – RANSAC
algo◦ Run k times:
(1)draw n samples randomly (2) fit parameters Θ with these n samples (3) for each of other N-n points, calculate its
distance to the fitted model, count the number
of inlier points, c◦ Output Θ with the largest c
Background-compute homography
Example:line fittingn=2
c=3 c=15
…………………
Image homography◦ Points should match◦ Measure error distance and give penalty
Moving object detect◦ For activity synopsis◦ examining the discrepancy between its local
motion vector and the global motion
Video analysis (1)
Visual quality measures
Video analysis(2)
Method of [31]Tong et al 04
Method of [35]Wang et al 02
Average differencesacross block boundaries.
Good panoramas◦ Good homography between frames◦ Video have high image quality◦ Cover a wild field view
Collision◦ More frame more wild field of view◦ More frame more accumulate error to degrade
quality
Discovering panoramas(1)
, vistual quality, extent of the scene
Visual quality measure
Discovering panoramas(2)
Scene extent measure
Discovering panoramas(3)
Reference Reference
An Approximate Solution Steps◦ 1.Fetch a segment Sk from pool Sp◦ 2.Find the scene extent of Sk and corresponding
reference frame.◦ 3.Append the panorama set according to
equation(2). until .
◦ 4.If the scene meet , , add remainder to pool Sp.
◦ 5.If pool Sp != Ο , go to loop 1
Discovering panoramas(4)
Discovering panoramas(4)shot boundary
segments
video
divide segments that have too penalty
Repeat until done
Discard those extent with too little coverage <
Scene panorama synthesis◦ blending – feathering
◦ median-bilateral filtering
Panorama synthesis(1)
Panorama synthesis(2) Activity synopsis synthesis
Detect
Discard
Select and composite into scene
YouTube Travel and Events category – West Lake http://www.youtube.com/watch?v=6FKCHLfTns8&feature=player_embedded#!
◦ size 320 x 240
Experiments (1)
Query panorama from YouTube 6 query , top 10 videos 86.7% contain panoramas
Experiments (2)
Example
Example
ExampleNotre Dame, Paris
In this paper, we presented an automatic method to discover panorama sources from casual videos.
“Query panoramas from YouTube”supports our proposal of using web videos as panorama source.
More importantly, this method contribute to presenting or summarizing imagery databases using panoramic imageries by mining the possible sources to synthesize the representations.
Conclusion
[31] H. Tong, M. Li, H. Zhang, and C. Zhang. Blur detection for digital images using wavelet transform.In IEEE ICME, 2004.
[35] Z. Wang, G. Wu, H. Sheikh, E. Simoncelli, E.-H.Yang, and A. Bovik. Quality-aware images. IEEE Transactions on Image Processing, 15(6):1680 -1689,2006.
Original Videos: http://pages.cs.wisc.edu/~fliu/project/discover-pano.htm
Reference