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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– Mosaicing, panorama– Object recognition– Photo tourism, photosynth– Human detection
• Adaboost-based face detector
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
• 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
• 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
He et al. CVPR 09Jia CVPR 08
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.ht
ml
• 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!– Data structures– A good working knowledge of MATLAB,
C, and C++ programming– Linear algebra – Vector calculus– EECS 274 Computer Vision– EECS 274 Matrix Computation
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
• 30% Critiques• 10% Presentation• 20% Midterm report• 10% Final project presentation• 30% Term project
Term Project
• Open-ended project of your choosing• Oral presentation
– Midterm presentation– Final presentation and demo
• Publish your results