1 / 31

Introduction

Introduction. Vision. ``to know what is where, by looking.’’ (Marr). Where What. Why is Vision Interesting?. Psychology ~ 35% of cerebral cortex is for vision. Vision is how we experience the world. Engineering Want machines to interact with world. Digital images are everywhere.

juliette
Télécharger la présentation

Introduction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction Computer Vision

  2. Vision • ``to know what is where, by looking.’’ (Marr). • Where • What Computer Vision

  3. Why is Vision Interesting? • Psychology • ~ 35% of cerebral cortex is for vision. • Vision is how we experience the world. • Engineering • Want machines to interact with world. • Digital images are everywhere. Computer Vision

  4. Vision is inferential: Light (http://www-bcs.mit.edu/people/adelson/checkershadow_illusion.html) Computer Vision

  5. Vision is Inferential: Prior Knowledge Computer Vision

  6. Computer Vision

  7. Computer Vision • Inference  Computation • Building machines that see • Modeling biological perception Computer Vision

  8. A Quick Tour of Computer Vision Computer Vision

  9. Boundary Detection http://www.robots.ox.ac.uk/~vdg/dynamics.html Computer Vision

  10. Computer Vision

  11. Boundary Detection Finding the Corpus Callosum (G. Hamarneh, T. McInerney, D. Terzopoulos) Computer Vision

  12. Tracking Computer Vision

  13. Tracking Computer Vision

  14. Tracking Computer Vision

  15. Tracking Computer Vision

  16. Tracking Computer Vision

  17. Stereo Computer Vision

  18. Stereo http://www.magiceye.com/ Computer Vision

  19. Motion http://www.ai.mit.edu/courses/6.801/lect/lect01_darrell.pdf Computer Vision

  20. Motion - Application (www.realviz.com) Computer Vision

  21. Pose Determination Visually guided surgery Computer Vision

  22. Recognition - Shading Lighting affects appearance Computer Vision

  23. Classification (Funkhauser, Min, Kazhdan, Chen, Halderman, Dobkin, Jacobs) Computer Vision

  24. Vision depends on: • Geometry • Physics • The nature of objects in the world (This is the hardest part). Computer Vision

  25. Approaches to Vision Computer Vision

  26. Modeling + Algorithms • Build a simple model of the world (eg., flat, uniform intensity). • Find provably good algorithms. • Experiment on real world. • Update model. Problem: Too often models are simplistic or intractable. Computer Vision

  27. Bayesian inference • Bayes law: P(A|B) = P(B|A)*P(A)/P(B). • P(world|image) = P(image|world)*P(world)/P(image) • P(image|world) is computer graphics • Geometry of projection. • Physics of light and reflection. • P(world) means modeling objects in world. Leads to statistical/learning approaches. Problem: Too often probabilities can’t be known and are invented. Computer Vision

  28. Engineering • Focus on definite tasks with clear requirements. • Try ideas based on theory and get experience about what works. • Try to build reusable modules. Problem: Solutions that work under specific conditions may not generalize. Computer Vision

  29. The State of Computer Vision • Science • Study of intelligence seems to be hard. • Some interesting fundamental theory about specific problems. • Limited insight into how these interact. Computer Vision

  30. The State of Computer Vision • Technology • Interesting applications: inspection, graphics, security, internet…. • Some successful companies. Largest ~100-200 million in revenues. Many in-house applications. • Future: growth in digital images exciting. Computer Vision

  31. Related Fields • Graphics. “Vision is inverse graphics”. • Visual perception. • Neuroscience. • AI • Learning • Math: eg., geometry, stochastic processes. • Optimization. Computer Vision

More Related