Computational Vision Lecture 1: Overview + Biological Vision Jeremy Wyatt

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, 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

Syllabus

Lectures

1. Biological Vision2. Edge detection3. Hough transforms4. Motion/Depth5. Recognising objects6. Recognising events7. Recognising faces8. 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 380-760nm (1 nm = 10-9 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

1

f

150 0.02dioptres f m

f

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

1 1 1

f u v

1 1 150

0.02dioptres

f

1 1 151

1 0.02dioptres

f

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

tan( )h i

u v

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

OFF area

ON area

Light spotTime

LightON 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

Excitatory Inhibitory

Red on Green off Yellow 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,

pp142-157