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

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cvpr 2006 highlights

Posters

Talks

World Cup

CVPR 2006 Highlights

Vaibhav Vaish

New York University, June 17-22

conference statistics
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
awards
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.
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 highlights
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!

scheduling
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

papers i liked
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
papers from stanford
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
papers from stanford1
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
papers i liked1
Papers 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 synopsis
Making 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 synopsis1
Making 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 synopsis2
Making a Long Video Short:Dynamic Video Synopsis
  • Find regions of “activity”
  • 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
some ranking results
Some Ranking Results

Error rate (snapshot vs professional): 24%

what makes a high quality photo1
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 collage
Picture Collage

J Wang, J Sun, L Quan, Xiaoou Tang, H Shum

picture collage1
Picture Collage
  • Maximize “informative regions”, minimize blank space
  • Optimize using random grid sampling (Bayesian framework)
papers i liked2
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
bilayer segmentation of live video

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
how it works
How 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 matting
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
other papers
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
papers i liked3
Papers 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 evaluation
Multi-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 evaluation1
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 deadlines
Upcoming Deadlines
  • December 3rd, 2006.
    • CVPR 2007, Minneapolis
  • March 2007
    • ICCV 2007, Rio de Janeiro
  • CVPR 2008 in Anchorage, Alaska