1. ACM Multimedia 2008 Feng Liu 1, Yuhen-Hu 1,2 and Michael Gleicher 1

2.  Introduction  Video analysis  Discovering panoramas  Panorama synthesis  Experiments  Conclusion

3.  STEP 1 image alignment  STEP2 image stitching

4.  Not all video has appropriate sources ◦ Not cover a wide field-of-view of a scene ◦ Motion may be randomly ◦ Image quality

5.  Three parts ◦ video analysis ◦ panorama source selection ◦ panorama synthesis

6. transformation

7.  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

8. n=2 c=3c=15 …………………

9.  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

10.  Visual quality measures Method of [31]Tong et al 04 Method of [35]Wang et al 02 Average differences across block boundaries. Average differences across block boundaries.

11.  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, vistual quality, extent of the scene

12.  Visual quality measure

13.  Scene extent measure Reference

14.  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

15. shot boundary segments video divide segments that have too penalty Repeat until done Discard those extent with too little coverage <

16.  Scene panorama synthesis ◦ blending – feathering ◦ median-bilateral filtering

17.  Activity synopsis synthesis Detect Discard Select and composite into scene

18.  YouTube Travel and Events category – West Lake http://www.youtube.com/watch?v=6FKCHLfTns8&feature=player_embedded#! http://www.youtube.com/watch?v=6FKCHLfTns8&feature=player_embedded#! ◦ size 320 x 240

19.  Query panorama from YouTube  6 query, top 10 videos  86.7% contain panoramas

22. Notre Dame, Paris

23.  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.

24.  [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