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

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Presentation transcript:

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. Apply algorithms to simplified problems by hand. Discuss the advantages and drawbacks of different methods, explaining their working.

Schedule 1 lecture a week, 2pm, Muirhead 1 lab/lecture a week, 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

Syllabus Lectures 1. Biological Vision 2. Edge detection 3. Hough transforms 4. Motion/Depth 5. Recognising objects 6. Recognising events 7. Recognising faces 8. Visual attention Labs 1. Matlab tutorials 2. Edge detection 3. Hough transforms 4. Face recognition 5. Object recognition

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)

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

Visible spectrum Humans perceive electromagnetic radiation with wavelengths nm (1 nm = m) 0.1nm 10nm 1000nm

Image Formation f is the focal length (in metres) is the power of the lens (in dioptres) Human eye has power ~59 dioptres f Image planeLensLight rays

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 uv

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

Retinal Processing 120m rods, 6m cones

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

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

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

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

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

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

Opponent colour theory ExcitatoryInhibitory Red on Green offYellow on

After images

Visual pathway

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

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

Reading Vicki Bruce, Visual Perception, pp1-60 Neil Carlson, Physiology of Behavior, pp