Beyond of pixels - ICVSS 2011 Report (Part 2)

Finally I publish the second (and last) part of ICVSS 2011 Report... thanks for your patience ;)

The lectures were very interesting by high-experience speakers in the state-of-art of computer vision. The speakers and the lectures acording with the final program were:

Monday 11

• Steven Seitz - Landmarks in 3D Computer Vision
• Steven Seitz - A Trillion Photos
• Ramesh Raskar - Computational Light Transport and Computational Photography: Camera Culture Inverse problems (slides)

Tuesday 12

• Jiri Matas - Tracking, Learning, Detection, Modeling All Working Together
• Ivan Laptev - Human Action Recognition
• Shmuel Peleg - Image Rearrangement & Video Synopsis

Wednesday 13

• Martial Hebert - Scene Understanding
• Guillermo Sapiro - The Life of Structured Learned Dictionaries
• Kari Pulli - Mobile Computational Photography with FCam

Thursday 14

• Steve Zucker - Visual Cortex and Perceptual Organization: what neurobiology can teach us about visual information processing
• Josef Sivic - Large Scale Visual Search for Particular Objects and Places
• Lorenzo Torresani - Efficient Novel-Class Recognition and Search

Friday 15

• Vitorrio Murino - Socially Intelligent Survillance and Monitoring
• William T. Freeman - Photographing Events over Time (Slides) (Video)
• Andrew Fitzgibbon - Convex and Nonlinear Optimization for Computer Vision

Hot topics in Computer Vision

• Large scale image/video analysis
• Inverse problems
• Image and video understanding
• Photo tourism
• Pose recognition & Kinect (Shotton, Fitzgibbon, Cook, Blake CVPR2011 PDF, supplementary material, videos, project)
• Survilence

Impressive works

Among these lectures some of works are amazing and really look like 'magic' :D. For example:

(Building Rome in a day)

(Photo bios - Face Movies Picassa)

More details and videos:

• Photo tourism (e.g. Building Rome in a Day, Finding paths though World's photos, Photosynth, PhotoCity)
• Towards Internet-scale Multi-view Stereo (video)
• Photobios (Video)
• Being John Malkovich
• Reconstructing Building Interiors from Images
• 3D Stereo Panorama
• Video Synopsis and Indexing
• Dynamosaics: Video Mosaics with Non-Chronological Time
• Shift-Map Image Editing

Some ideas and papers to check

• Multiple kernel learning (Non-linear model + feature combination)
• Winning recipe: Many features +non -linear classifiers (e.g. [Gehler and Nowozin, CVPR’09])

• Represent each image x in terms of its “closeness” to a set of basis classes (“classemes”)
• Classemes: a compact descriptor for efficient recognition [Torresani et al., 2010]

Final remarks

• Most of poster of Ph.D students were about computer vision, few works were related with medical imaging. Just one poster had a part of work with histopathological images (75. MACHINE LEARNING FOR TARGET DETECTION Vink J.P.).
• Other poster shows an interesting relation between two kind of graphical models, LDA (latent dirichled allocation) and population structure ( 68. FROM LDA TO VISION VIA POPULATION STRUCTURE Sharmanska V., Lampert C.H.).
• To work in progress, compare against the state of the art methods that the source code publicly available.
• Do not forget next time to bring business cards. This lesson had already learned in the CIARP2009 and forgot :S.
• The awards were won by some end of doctoral work, completed and / or published. No need to bring something totally original or preliminary results, especially if you are interested in the prize, at least one of these was 700 euros (not bad).
• I need to improve English. I could defend, but I still lack a lot, sometimes one feels limited to express some ideas, especially outside the technical and academic environment, such as lunches.
• You must travel light. Better a bag that two (especially in the metro).
• My final comment about the course is that it is highly recommended. The winning combination of conferences in the state of the art, high-level speakers, experts from around the world in computer vision, excellent food and wine, in a quiet place next to a beach along the Mediterranean sea, what more you want? ICVSS2012 Coming soon...

Curiosities

• Other two Colombian guys were in the school!!. They are doing their Ph.D in France and Belgium. Santiago Velasco and Jorge Niño.
• The Italians are superstitious, Alitalia's planes jumped from positions 12 to Post 14.
• Many participants wore shirts geeks, many of the participants passed it connected to the laptop and smart-phone with the pool and the beach nearby, there are more nerds than us :D jeje.
• The hotel had a bad internet connection was slow or had no connection, especially when they had the breaks between talks.
• There was plenty of delicious food and not go hungry:) Quite the contrary (it was buffet). In fact we ate particular things as horse meat and octopus in Catania and Ragusa respectively.

References

Torresani, L., Szummer, M., & Fitzgibbon, A. (2010). Efficient object category recognition using classemes. Computer Vision–ECCV 2010, 776–789. Springer. Retrieved from http://www.springerlink.com/index/800852076P3467J2.pdf

Griffin, G., & Perona, P. (2008). Learning and using taxonomies for fast visual categorization. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1–8). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4587410

Bart, E., Porteous, I., Perona, P., & Welling, M. (2008). Unsupervised learning of visual taxonomies. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1–8). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4587620

Sivic, J., Russell, B. C., Zisserman, A., Freeman, W. T., & Efros, A. A. (2008). Unsupervised discovery of visual object class hierarchies. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1–8). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4587622

Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155(2), 945. Genetics Soc America. Retrieved from http://www.genetics.org/content/155/2/945.short