Download
cvpr 2006 highlights n.
Skip this Video
Loading SlideShow in 5 Seconds..
CVPR 2006 Highlights PowerPoint Presentation
Download Presentation
CVPR 2006 Highlights

CVPR 2006 Highlights

218 Views Download Presentation
Download Presentation

CVPR 2006 Highlights

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Posters Talks World Cup CVPR 2006 Highlights Vaibhav Vaish New York University, June 17-22

  2. Conference Statistics • 318 papers (28% acceptance) • 54 oral presentations (4.7%) • 1136 submissions • 30 area chairs, 560 reviewers • ≈ 1200 attendees (30% increase) • Free dinner on last day

  3. Awards • Honored 5 “champion reviewers” • Best Paper: Putting Objects in Perspective D. Hoiem, A. Efros, M. Herbert Honorable mention: Incremental Learning of Object Detectors Using a Visual Shape Alphabet A. Opelt, A. Pinz, A. Zisserman • Best Poster: TBA.

  4. Longuet-Higgins Prize (CVPR 96) Neural Network-Based Face Detection H. Rowley, S. Baluja, T. Kanade Combining Greyvalue Invariants with Local Constraints for Object Recognition C. Schmid, R. Mohr

  5. Workshop Highlights • 25 Years of RANSAC • Keynote: Robert Bolles (co-inventor of RANSAC) • 2 Keynotes by Shree Nayar (PROCAMS, Medical Imaging workshop) • Projector defocus • Separating direct and indirect illumination Do NOT miss this at SIGGRAPH!

  6. Scheduling • Oral presentations recorded, broadcast live • To be put online (somewhere, sometime) Orals I 90 min Posters I 210 min Posters 2 210 min Time Orals 2 90 min

  7. Papers I Liked • Papers from Stanford • Fun with digital photos and video • Computational imaging and sensors • Why Bill Gates is rich • Obituary: 3D Reconstruction • “Visual words” for recognition

  8. Papers from Stanford • A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image • E. Delage, H. Lee, Andrew Ng • Learning Object Shape: From Drawings to Images • G. Elidan, Geremy Heitz, Daphne Koller • Object Pose Detection in Range Scan Data • Jim Rodgers, Dragomir Anguelov, H Pang, Daphne Koller • A Comparison and Evaluation of Multi-View Stereo Algorithms • S. Seitz, B. Curless, J. Diebel, D. Scharstein, R. Szeliski • Reconstructing Occluded Surfaces … blah

  9. Papers from Stanford • A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image • E. Delage, H. Lee, Andrew Ng • Learning Object Shape: From Drawings to Images • G. Elidan, Geremy Heitz, Daphne Koller • Object Pose Detection in Range Scan Data • Jim Rodgers, Dragomir Anguelov, H Pang, Daphne Koller • A Comparison and Evaluation of Multi-View Stereo Algorithms • S. Seitz, B. Curless, J. Diebel, D. Scharstein, R. Szeliski • Reconstructing Occluded Surfaces … blah

  10. Papers I Liked • Papers from Stanford • Fun with digital photos and video • Computational imaging and sensors • Obituary: 3D Reconstruction • “Visual words” for recognition

  11. Making a Long Video Short:Dynamic Video Synopsis A. Rav-Acha, Yael Pritch, Shmuel Peleg. • Video Summary • Short • Informative • Accurate • Seamless

  12. Making a Long Video Short:Dynamic Video Synopsis A. Rav-Acha, Yael Pritch, Shmuel Peleg. Input Video Summary Video More demos …

  13. Making a Long Video Short:Dynamic Video Synopsis • Find regions of “activity” • Compute summary using MRF optimization

  14. What Makes A High Quality Photo ? • The Design of High-Level Features for Photo Quality Assessment • Yan Ke, Xiaoou Tang, Feng Jing

  15. Some Ranking Results Error rate (snapshot vs professional): 24%

  16. What Makes A High Quality Photo ? • Pros vs Point-and-shooters • Simplicity • (Sur)realism • Basic Technique • Features (a subset) • Lack of blur • Spatial edge distribution • Color, brightness, contrast, hue count • Learn from http://DPChallenge.com

  17. Picture Collage J Wang, J Sun, L Quan, Xiaoou Tang, H Shum

  18. Picture Collage • Maximize “informative regions”, minimize blank space • Optimize using random grid sampling (Bayesian framework)

  19. Papers I Liked • Papers from Stanford • Fun with digital photos and video • Computational imaging and sensors • Why Bill Gates is rich • Obituary: 3D Reconstruction • “Visual words” for recognition

  20. CVPR 2005 System Bilayer Segmentation of Live Video A. Criminisi, G. Cross, A. Blake, V. Kolmogorov Link • Goals: • Single camera • Real-time (no optic flow!) • Good looking results

  21. How it works • Priors, priors, priors and priors • Temporal continuity • Spatial coherence • Color likelihood • Motion likelihood • Learning • Fast approximate binary graph cut

  22. A Closed Form Solution to Natural Image Matting Anat Levin, Dani Lischinski, Yair Weiss • Idea: in a small window, colors lie on a line in color space • Find alpha by minimizing αT L α • Eigenvectors of L suggest good scribbles

  23. Lensless Imaging with a Controllable Aperture Assaf Zomet, Shree Nayar

  24. Other Papers • Instant 3Descatter • Tali Treibitz, Yoav Schechner • Blind Haze Separation • S Shwartz, E Namer, Yoav Schechner • Space-time Video Montage • H Kang, Y Matsuhita, Xiaoou Tang, Xue-Quan Chen

  25. Papers I Liked • Papers from Stanford • Fun with digital photos and video • Computational imaging and sensors • Obituary: 3D Reconstruction • “Visual words” for recognition

  26. Multi-View Stereo Evaluation S. Seitz, B. Curless, J Diebel, D Scharstein, R Szeliski http://vision.middlebury.edu/mview

  27. Multi-View Stereo Taxonomy • Scene representation • Photo-consistency measure • Visibility model • Shape prior • Reconstruction algorithm • Initialization

  28. Multi-View Stereo Evaluation • Metrics • Accuracy • Completeness • Running time • Renderings • Conclusions • Most work pretty well • Having lots of views enables simpler algorithms [Multi-view Stereo Revisited, Goesele et al]

  29. Upcoming Deadlines • December 3rd, 2006. • CVPR 2007, Minneapolis • March 2007 • ICCV 2007, Rio de Janeiro • CVPR 2008 in Anchorage, Alaska