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EECS 286 Advanced Topics in Computer Vision Ming-Hsuan Yang.

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Presentation on theme: "EECS 286 Advanced Topics in Computer Vision Ming-Hsuan Yang."— Presentation transcript:

1 EECS 286 Advanced Topics in Computer Vision Ming-Hsuan Yang

2 Computer vision Holly grail – tell a story from an image

3 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, years later, we are still working on this

4 1970s

5 1980s

6 1990s Face detection Particle filter Pfinder Normalized cut

7 2000s SIFT –Mosaicing, panorama –Object recognition –Photo tourism, photosynth –Human detection Adaboost-based face detector

8 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

9 Related topics

10 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

11 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

12 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, …

13 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)

14 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 …

15 Tools Google scholar, citeseer, h-index Software: publish or perish Disclaimer: –h index = significance? –# of citation = significance?

16 Challenging issues Large scale Unconstrained Real-time Robustness Recover from failure – graceful dead

17 Recent topics Object detection, segmentation, recognition, categorization Deep learning Internet scale image search Video search 3D human pose estimation Computational photography Scene understanding

18 Some tools Prior Context Sparse representation Multiple instance learning Online learning Convex optimization Constraint Hashing

19 Prior Torralba and Sinha ICCV 01

20 Prior Heitz and Koller ECCV 08

21 Prior He et al. CVPR 09 Jia CVPR 08

22 Scene understanding Leibe et al. CVPR 07

23 Computational photography Johnson and Adelson CVPR 09

24 Computational photography Gelsight: –http://www.mit.edu/~kimo/gelsight/ Lytro: –http://www.lytro.com/

25 Image and video search Google image search –http://images.google.com/ Videosurf –http://www.videosurf.com/

26 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 companiesDavid Lowe Confluence of vision, graphics, learning, sensing and signal processing

27 Software and hardware Algorithms: processing images and videos Camera: acquiring images/videos Embedded system

28 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

29 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

30 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

31 Textbooks and references Textbook –Computer Vision: A Modern Approach, David Forsyth and Jean PonceComputer Vision: A Modern Approach –Computer Vision: Algorithms and Applications, Richard SzeliskiComputer Vision: Algorithms and Applications –Elements of Statistical Learning, Hastie, Tibshirani, FriedmanElements of Statistical Learning 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

32 Grading 30% Critiques 10% Presentation 20% Midterm report 10% Final project presentation 30% Term project

33 Term Project Open-ended project of your choosing Oral presentation –Midterm presentation –Final presentation and demo Publish your results


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