Transcript Introduction - faculty.ucmerced.edu

EECS 286
Advanced Topics in Computer Vision
Ming-Hsuan Yang
Computer vision
• Holly grail – tell a story from an image
History
• “In the 1960s, almost no one realized that
machine vision was difficult.” – David Marr,
1982
• Marvin Minsky asked Gerald Jay Sussman
to “spend the summer linking a camera to
a computer and getting the computer to
describe what it saw” – Crevier, 1993
• 40+ years later, we are still working on this
1970s
1980s
1990s
• Face detection
• Particle filter
• Pfinder
• Normalized cut
2000s
• SIFT
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Mosaicing, panorama
Object recognition
Photo tourism, photosynth
Human detection
• Adaboost-based face detector
2010s
• Renaissance of Artificial Intelligence and
Neural Network
• Deep learning
– Unsupervised: Reducing the Dimensionality of
Data with Neural Networks, Hinton and
Salakhutdinov, Science 2006
– Supervised: ImageNet Classification with
Deep Convolutional Neural Network,
Krizhevsky, Sutskever, and Hinton, NIPS
2012
Frontiers in computer vision
• NSF sponsored workshop at MIT CSAIL, August
21 to 24, 2011
– identify the future impact of computer vision on the
economic, social, and security needs of the nation
– outline the scientific and technological challenges to
address
– draft a roadmap to address those challenges and
realize the benefits
• Read the current white papers
• Read the 1991 workshop final reports
Related topics
Conferences
• CVPR – Computer Vision and Pattern
Recognition, since 1983
– Annual, held in US
• ICCV – International Conference on
Computer Vision, since 1987
– Every other year, alternate in 3 continents
• ECCV – European Conference on
Computer Vision, since 1990
– Every other year, held in Europe
Conferences (cont’d)
• ACCV – Asian Conference on Computer
Vision
• BMVC – British Machine Vision
Conference
• ICPR – International Conference on
Pattern Recognition
• SIGGRAPH
• NIPS – Neural Information Processing
Systems
Conferences (cont’d)
• MICCAI – Medical Image Computing and
Computer-Assisted Intervention
• ISBI – International Symposium on Biomedical
Imaging
• FG – IEEE Conference on Automatic Face and
Gesture Recognition
• ICCP, ICDR, ICVS, DAGM, CAIP, MVA, AAAI,
IJCAI, ICML, ICRA, ICASSP, ICIP, SPIE, DCC,
WACV, 3DPVT, ACM Multimedia, ICME, …
Journals
• PAMI – IEEE Transactions on Pattern
Analysis and Machine Intelligence, since
1979 (impact factor: 5.96, #1 in all engineering
and AI, top-ranked IEEE and CS journal)
• IJCV – International Journal on Computer
Vision, since 1988 (impact factor: 5.36, #2 in
all engineering and AI)
• CVIU – Computer Vision and Image
Understanding, since 1972 (impact factor:
2.20)
Journals (cont’d)
• IVC – Image and Vision Computing
• IEEE Transactions on Medical Imaging
• TIP – IEEE Transactions on Image
Processing
• MVA – Machine Vision and Applications
• PR – Pattern Recognition
• TM – IEEE Transactions on Multimedia
• …
Tools
• Google scholar, citeseer,
• h-index
• Software: publish or perish
• Disclaimer:
– h index = significance?
– # of citation = significance?
Challenging issues
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Large scale
Unconstrained
Real-time
Robustness
Recover from failure – graceful dead
Recent topics
• Object detection, segmentation,
recognition, categorization
• Deep learning
• Internet scale image search
• Video search
• 3D human pose estimation
• Computational photography
• Scene understanding
Some tools
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Prior
Context
Sparse representation
Multiple instance learning
Online learning
Convex optimization
Constraint
Hashing
Prior
Torralba and Sinha ICCV 01
Prior
Heitz and Koller ECCV 08
Prior
Jia CVPR 08
He et al. CVPR 09
Scene understanding
Leibe et al. CVPR 07
Computational photography
Johnson and Adelson CVPR 09
Computational photography
• Gelsight:
– http://www.mit.edu/~kimo/gelsight/
• Lytro:
– http://www.lytro.com/
Image and video search
• Google image search
– http://images.google.com/
• Videosurf
– http://www.videosurf.com/
Current state of the art
• You just saw examples of current systems.
– Many of these are less than 5 years old
• This is a very active research area, and rapidly
changing
– Many new applications in the next 5 years
• To learn more about vision applications and companies
– David Lowe maintains an excellent overview
of vision companies
• http://www.cs.ubc.ca/spider/lowe/vision.html
• Confluence of vision, graphics, learning,
sensing and signal processing
Software and hardware
• Algorithms: processing images and videos
• Camera: acquiring images/videos
• Embedded system
Class mechanics
• Papers will be assigned weekly
• One student needs to present 2 or 3
papers in details
• All students need to read and write
critiques
• Presentation and discussion
Prerequisites
• Prerequisites—these are essential!
– EECS 274 Computer Vision
– EECS 274 Matrix Computation
– Linear algebra
– Vector calculus
– A good working knowledge of MATLAB, C,
and C++ programming
Topics
• Low-level vision: feature, edge, texture, deblurring, visual
saliency
• Mid-level vision: segmentation, superpixels
• High-level vision: object detection, object recognition,
visual tracking, super resolution
• Learning algorithms: Markov random field, conditional
random field, graphical model, belief propagation, active
learning, multi-view learning
Textbooks and references
• Textbook
– Computer Vision: A Modern Approach, David Forsyth and Jean Ponce
– Computer Vision: Algorithms and Applications , Richard Szeliski
– Elements of Statistical Learning, Hastie, Tibshirani, Friedman
• Reference for background study:
– Introductory Techniques for 3-D Computer Vision, Emanuele Trucco and
Alessandro Verri
– Multiple View Geometry in Computer Vision, Richard Hartley and Andrew
Zisserman
– Robot Vision, Berthold Horn
– Learning OpenCV: Computer Vision with OpenCV Library, Gary Bradski and
Adrian Kaehler
• Reading assignments will be from the text and additional material
that will be handed out or made available on the web page
• All lecture slides will be available on the course website
http://faculty.ucmerced.edu/mhyang/course/eecs286/index.htm
Grading
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20% Critiques
10% Presentation
20% Midterm report
10% Final project presentation
40% Term project
Term Project
• Open-ended project of your choosing
• Oral presentation
– Midterm presentation
– Final presentation and demo
• Publish your results