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CVPR 2006 HighlightsCVPR 2006 Highlights
Vaibhav Vaish
New York University, June 17-22
Posters
TalksWorld Cup
Conference StatisticsConference 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
AwardsAwards
• 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.
Longuet-Higgins Prize (CVPR 96)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
Workshop HighlightsWorkshop 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!
SchedulingScheduling
• Oral presentations recorded, broadcast live
• To be put online (somewhere, sometime)
Orals I
90 min
Orals 2
90 min
Posters I
210 min
Posters 2
210 minTime
Papers I LikedPapers 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
Papers from StanfordPapers 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
Papers from StanfordPapers 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
Papers I LikedPapers I Liked
• Papers from Stanford
• Fun with digital photos and video
• Computational imaging and sensors
• Obituary: 3D Reconstruction
• “Visual words” for recognition
Making a Long Video Short:Dynamic Video SynopsisMaking a Long Video Short:Dynamic Video Synopsis
A. Rav-Acha, Yael Pritch, Shmuel Peleg.
Video Summary
• Short
• Informative
• Accurate
• Seamless
Making a Long Video Short:Dynamic Video SynopsisMaking a Long Video Short:Dynamic Video Synopsis
A. Rav-Acha, Yael Pritch, Shmuel Peleg.
Input Video Summary Video
More demos …
Making a Long Video Short:Dynamic Video SynopsisMaking a Long Video Short:Dynamic Video Synopsis
1. Find regions of “activity”
2. Compute summary using MRF optimization
What Makes A High Quality Photo ?What Makes A High Quality Photo ?
• The Design of High-Level Features for Photo Quality Assessment
– Yan Ke, Xiaoou Tang, Feng Jing
What Makes A High Quality Photo ?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
Picture CollagePicture Collage
1. Maximize “informative regions”, minimize blank space
2. Optimize using random grid sampling (Bayesian framework)
Papers I LikedPapers 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
Bilayer Segmentation of Live VideoBilayer Segmentation of Live Video
A. Criminisi, G. Cross, A. Blake, V. Kolmogorov Link
CVPR 2005 System
Goals:
• Single camera
• Real-time (no optic flow!)
• Good looking results
How it worksHow it works
• Priors, priors, priors and priors
– Temporal continuity
– Spatial coherence
– Color likelihood
– Motion likelihood
• Learning
• Fast approximate binary graph cut
A Closed Form Solution to Natural Image MattingA 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
Lensless Imaging with a Controllable ApertureLensless Imaging with a Controllable ApertureAssaf Zomet, Shree Nayar
Other PapersOther 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
Papers I LikedPapers I Liked
• Papers from Stanford
• Fun with digital photos and video
• Computational imaging and sensors
• Obituary: 3D Reconstruction
• “Visual words” for recognition
Multi-View Stereo EvaluationMulti-View Stereo Evaluation
S. Seitz, B. Curless, J Diebel, D Scharstein, R Szeliski
http://vision.middlebury.edu/mview
Multi-View Stereo Taxonomy Multi-View Stereo Taxonomy
• Scene representation
• Photo-consistency measure
• Visibility model
• Shape prior
• Reconstruction algorithm
• Initialization
Multi-View Stereo Evaluation 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]
Upcoming DeadlinesUpcoming Deadlines
• December 3rd, 2006.
– CVPR 2007, Minneapolis
• March 2007
– ICCV 2007, Rio de Janeiro
• CVPR 2008 in Anchorage, Alaska