1. Where computer vision needs help from computer science (and machine learning) Bill Freeman Electrical Engineering and Computer Science Dept. Massachusetts Institute of Technology September 9, 2009

2. Outline My background Computer vision applications Computer vision techniques and problems: – Low-level vision: underdetermined problems – High-level vision: combinatorial problems – Miscellaneous problems

3. At Photokina, in Cologne, Germany

4. Me (Foreign Expert) and my wife (English teacher), riding from the Foreigners’ Cafeteria at the Taiyuan University of Technology, Shanxi, China

5. While in China, I read this book (to be re-issued by MIT Press this year), and got very excited about computer vision. Studied for PhD at MIT.

6. Worked for 9 years at Mitsubishi Electric Research Labs, an industrial research lab doing fundamental research across the street from MIT.

7. 2001 – present, MIT

8. Infinite images Joint work with: Biliana Kaneva Josef Sivic Shai Avidan Antonio Torralba

9. A computer graphics application of belief propagation for optimal seam finding

10. The image database We have collected ~6 million images from Flickr based on keyword and group searches – typical image size is 500x375 pixels – 720GB of disk space (jpeg compressed)

11. Image representation Color layout GIST [Oliva and Torralba’01] Original image

12. Obtaining semantically coherent themes We further break-up the collection into themes of semantically coherent scenes: Train SVM-based classifiers from 1-2k training images [Oliva and Torralba, 2001]

13. Basic camera motions Forward motionCamera rotation Camera pan Starting from a single image, find a sequence of images to simulate a camera motion:

14. 3. Find a match to fill the missing pixels Scene matching with camera view transformations: Translation 1. Move camera 2. View from the virtual camera 4. Locally align images 5. Find a seam 6. Blend in the gradient domain

15. 4. Stitched rotation Scene matching with camera view transformations: Camera rotation 1. Rotate camera 2. View from the virtual camera 3. Find a match to fill-in the missing pixels 5. Display on a cylinder

16. More “infinite” images – camera translation

20. Virtual space as an image graph Forward Rotate (left/right) Pan (left/right) Nodes represent Images Edges represent particular motions: Edge cost is given by the cost of the image match under the particular transformation Image graph Kaneva, Sivic, Torralba, Avidan, and Freeman, Infinite Images, to appear in Proceedings of IEEE.

21. Virtual image space laid out in 3D Kaneva, Sivic, Torralba, Avidan, and Freeman, Infinite Images, to appear in Proceedings of IEEE.