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Future of Computer Vision

Future of Computer Vision. Horst Bischof Inst. for Computer Graphics and Vision Graz University of Technology. Motto of the talk. It is a fantastic time …. Motto of the talk. to do computer vision!. WHY?. Computer Vision. At least three goals Understand biological visual systems

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Future of Computer Vision

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  1. Future ofComputer Vision Horst Bischof Inst. for Computer Graphics and Vision Graz University of Technology

  2. Motto of the talk • It is a fantastic time …

  3. Motto of the talk • to do computer vision!

  4. WHY?

  5. Computer Vision • At least three goals • Understand biological visual systems • Build machines that see • What are the fundamental processes of seeing

  6. Computer Vision • The systems today are still exceedingly limited in their performance  considerable room for improvement Where are chairs? How many feet? Two interpretations?

  7. Holy Grails in Vision • Segmentation • 2. Correspondence • Recognition Problem

  8. Future of Computer Vision • Where do the innovations come from? • 1. Hardware • 2. Algorithms/Software

  9. HARDWARE

  10. Hardware • First time that HW is no longer a real limitation !! • Processing • Image Resolution • Storage • Internet • Mobile Devices • Networks of cameras

  11. Processing • Moore’s Law still holds! • Multi-core CPUs • Highly Parallel  GPUs (+ Software eg. Cuda) • DEMO

  12. Resolution • Ever growing resolution: • 1975: 100 x 100 = 0.01 MP • 2008: 9216 × 9216 = 85 MP • (BAE) • UltraCamx: 216 MP • New fantastic opportunities •  Computational Cameras 1900 Chicago & Alton Railroad Train (photograph a train), $5000

  13. Internet • Huge repository of images • Flickr: 3.Nov. 2008 ~3 Billion Photos On-line 1 Million added a day • YouTube: 65.000 new Videos a day 20% of Internet Traffic • What can we do with these images?

  14. Mobile Vision • Most of us have a mobile CV device with them • Small Cameras • Embedded Systems • Mobile CV next large application area • Place Recognition • Recognizing Tags • Shopping • Games • Augmented Reality 4,4mmx15mm

  15. ALGORITHMS

  16. (Some) New Developments

  17. Bayesian Methods Data Ill-posed • Lots of applications • Computationally heavy • Easily parallelizable • Energy minimization approaches Prior

  18. Energy minimization More to come Apapted f. D Cremers 2007

  19. Continous energy functional • Data term potentially non-convex  Global Optimal Solution • Defines domain of application • Denoising • Segmentation • Stereo Total Variation regularization Data term Pock et.al

  20. Vision & Learning • Combining Computer Vision with ML • Huge Success • We have good/stable features • SIFT • Boundary fragments • If enough data learning works • SVM • Boosting

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