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

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

 STEP 1 image alignment  STEP2 image stitching

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

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

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

n=2 c=3c=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

 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.

 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

 Visual quality measure

 Scene extent measure 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

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

 Activity synopsis synthesis Detect Discard Select and composite into scene

 YouTube Travel and Events category – West Lake ◦ size 320 x 240

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

Notre 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.

 [31] H. Tong, M. Li, H. Zhang, and C. Zhang. Blur detection for digital images using wavelet transform.In IEEE ICME,  [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): ,2006.  Original Videos: