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

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.

http://www.cs.cmu.edu/~dhoiem/projects/pop/

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

http://www.ri.cmu.edu/projects/project_271.html

http://lear.inrialpes.fr/people/schid/publications.html




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

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

http://www.vision.huji.ac.il/synopsis


A. Rav-Acha, Yael Pritch, Shmuel Peleg.

Input Video Summary Video

More demos …




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

Some Ranking ResultsSome Ranking Results

Error rate (snapshot vs professional): 24%

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

http://dpchallenge.com/

http://dpchallenge.com/

Picture CollagePicture Collage

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

Picture CollagePicture Collage

1. Maximize “informative regions”, minimize blank space

2. Optimize using random grid sampling (Bayesian framework)





– Why Bill Gates is rich



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

http://research.microsoft.com/vision/cambridge/i2i

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

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

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

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