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Computational Vision

Computational Vision. Lecture 1: Overview + Biological Vision Jeremy Wyatt. What you should be able to do. Make informed choices about which sort of algorithms to apply to solve specific problems. Use standard vision libraries or software to construct working vision systems.

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Computational Vision

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  1. Computational Vision Lecture 1: Overview + Biological Vision Jeremy Wyatt

  2. What you should be able to do • Make informed choices about which sort of algorithms to apply to solve specific problems. • Use standard vision libraries or software to construct working vision systems. • Apply algorithms to simplified problems by hand. • Discuss the advantages and drawbacks of different methods, explaining their working.

  3. Schedule • 1 lecture a week, Mondays @ 2pm, Muirhead • 1 lab/lecture a week, Thursdays @ 12pm (Robot Lab or Chem Eng) • I am currently away on Monday Oct 3 and Monday Nov 14, so there will be no lectures on those days

  4. Lectures Biological Vision Edge detection Hough transforms Motion/Depth Recognising objects Recognising events Recognising faces Visual attention Labs Matlab tutorials Edge detection Hough transforms Face recognition Object recognition Syllabus

  5. Assessment • 70% 1.5 hour unseen exam in May/June • 30% 3 page experimental write-up of one of your labs (in pairs) (due Dec 7 12 noon)

  6. Biological Vision • Light and image formation • Retinal Processing • Colour • Visual Pathway • Striate Cortex

  7. Visible spectrum • Humans perceive electromagnetic radiation with wavelengths 380-760nm (1 nm = 10-9 m) 0.1nm 10nm 1000nm

  8. Image Formation Light rays Lens Image plane • f is the focal length (in metres) • is the power of the lens (in dioptres) • Human eye has power ~59 dioptres f

  9. Image Formation • Most of the refractive power of the human eye comes from the air-cornea boundary(49 of 59 dioptres) • As an object moves closer the power of the lens must increase to accommodate • So if the object is infinitely far away • But if it is 1m away the lens must change shape to produce a sharp image u v

  10. Image Formation • As an object moves in world how does it move across the image plane? • If the image plane is curved then as q gets larger this becomes a worse and worse approximation h v q i u

  11. Retinal Processing • 120m rods, 6m cones

  12. Retinal Processing • Amacrine and horizontal cells integrate receptor outputs • More rods connect to each ganglion cells: less acuity, but greater sensitivity • Ganglions have receptive fields

  13. Types of Ganglion cell OFF Cell ON Cell • Centre surround cells Light OFF area ON area ON area OFF area Time Light spot

  14. Grid of ON cell receptive fields These ON cells fire most Perceptual effects

  15. Colour • Two theories/systems • Trichromatic (Young-Helmholtz) • Explains • How we discriminate wavelengths 2nm in difference • How we can match a mixture of wavelengths to a single colour • Some types of colour blindness

  16. Colour • Trichromatic theory can’t explain colour blending Bluey green Orange ? Greeny red? ? Yellowy blue?

  17. Opponent Colour Theory • Ganglion ON cells sensitive to outputs of cones OFF ON

  18. Opponent colour theory Red on Green off Yellow on Excitatory Inhibitory

  19. After images

  20. Visual pathway

  21. The striate cortex • Composed of hyper-columns • Within each are columns of cells tuned to features of a particular orientation

  22. Summary • Image formation • Very early visual processing • Filling in and perceptual effects • Colour perception • Eye-cortex mapping

  23. Reading • Vicki Bruce, Visual Perception, pp1-60 • Neil Carlson, Physiology of Behavior, pp142-157

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