1b50 – visual system daniel j hulme. errata phylogenetic – genetic history of the species...

1B50 – Visual System

Daniel J Hulme

Errata

• Phylogenetic – genetic history of the species

• Ontogenetic – experience of the individual

• It was Kepler who first realised the true function of the retina (1604)

Outline

• Cognitive Vision– Why do we want computers to see?– Why can’t computers see?– Introducing percepts and concepts

• Visual System– The Eye and Brain– Early visual processes– Edge Detection

• Percepts and Concepts– Late Visual Processes– Concepts

Human Visual System

• The cornea and lens together focus images on the retina

• The retina is part of the central nervous system

• Fovea – 40 minutes in size – little less than 1 degree

Retina

• Grows out of neural ectoderm embryology, which is the same embryological substrate that the nervous system and brain grows out of

• Five types of neurons in the retina:– Photoreceptors– Bipolar cells– Ganglion cells– Horizontal cells– Amacrine

Radio Frequency Spectrum

419 531 559Cone Peak Responses

Rods and Cones (transducers)

• Two types of photoreceptors

• Rods– Extremely sensitive to light– Provide achromatic vision– Work at low level (scotopic) illumination

• Cones: – Less sensitive to light– Provide colour vision– Work at high level (photopic) illumination

• Only cones in the fovea

• Extreme periphery of retina – only rods

• 126 million rods

• 4 million cones

Retina Layout

Converting light into electricity

γ

Phot

onR

hodo

psin

mol

ecul

ePh

oton

abs

orbe

dM

olec

ule

chan

ges

shap

e

Alte

rs fl

ow o

f

curre

nt in

mol

ecul

e El

ectri

cal c

hang

es

prop

agat

e to

syna

pse

Neu

rotra

nsm

itter

s

affe

ct th

e ne

xt

neur

on

Information Flow

• Each photoreceptor (rod or cone) does not feed directly to the visual cortex

• A number of photoreceptors are connected to a ganglion cell whose axon forms part of the optical nerve

• The collection of photoreceptors connected to a particular ganglion cell forms that cell’s receptive field

• A photoreceptor may be connected to more than one ganglion cell

Retina• photoreceptor bipolar cell ganglion cell

• 130million receptors

• 1million optic nerve fibers

• For every 3 foveal cones, there are only 2 bipolar cells, to 3 ganglion cells

• Therefore each foveal cone has its own optic nerve fiber

• Many 100s rods for each nerve fiber in the periphery

Receptive Fields

GanglionCell

Positive Weight

Negative Weight

output

: Rod or Cone

Finding Edges - Setup0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 -1 -1 -1

-1 8 -1

-1 -1 -1

• Simple image

• Simple filter (kernel)

Finding Edges - Convolutions0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

-1 -1 -1

-1 8 -1

-1 -1 -1

0 0 0

0 1 1

0 1 1

0 0 0

0 8 -1

0 -1 -1

5∑


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0

Finding Edges – Convolutions0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 -1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 -1


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 -1 -1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 -1 -2


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 -1 -1 -1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 -1 -2 -3


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 -1 -2 -3 -3 -2 -1 0

0


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 -1 1 1 1 0 0 0

0 0 0 -1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 -1 -2 -3 -3 -2 -1 0

0 -2


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 8 -1 1 1 0 0 0

0 0 0 -1 -1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 -1 -2 -3 -3 -2 -1 0

0 -2 5


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 -1 8 -1 1 0 0 0

0 0 0 -1 -1 -1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 -1 -2 -3 -3 -2 -1 0

0 -2 5 3


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 1 -1 8 -1 0 0 0

0 0 0 1 -1 -1 -1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 -1 -2 -3 -3 -2 -1 0

0 -2 5 3 3


0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 -1 -1 -1 1 0 0 0

0 0 0 -1 8 -1 1 0 0 0

0 0 0 -1 -1 -1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 -1 -2 -3 -3 -2 -1 0

0 -2 5 3 3 5 -2 0

0 -3 3 0 0 3 -3 0

0 -3 3 0 0 3 -3 0

0 -2 5 3 3 5 -2 0

0 -1 -2 -3 -3 -2 -1 0

0 0 0 0 0 0 0 0

Finding Edges – Convolutions

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 1 1 1 1 0 0

0 0 1 0 0 1 0 0

0 0 1 0 0 1 0 0

0 0 1 1 1 1 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

Edge Detection Example0.0003 0.0009 0.0022 0.0038 0.0045 0.0038 0.0022 0.0009 0.0003

0.0009 0.0032 0.0071 0.0102 0.011 0.0102 0.0071 0.0032 0.0009

0.0022 0.0071 0.0114 0.0065 0.0008 0.0065 0.0114 0.0071 0.0022

0.0038 0.0102 0.0065 -0.0243 -0.0478 -0.0243 0.0065 0.0102 0.0038

0.0045 0.011 0.0008 -0.0478 -0.0829 -0.0478 0.0008 0.011 0.0045

0.0038 0.0102 0.0065 -0.0243 -0.0478 -0.0243 0.0065 0.0102 0.0038

0.0022 0.0071 0.0114 0.0065 0.0008 0.0065 0.0114 0.0071 0.0022

0.0009 0.0032 0.0071 0.0102 0.011 0.0102 0.0071 0.0032 0.0009

0.0003 0.0009 0.0022 0.0038 0.0045 0.0038 0.0022 0.0009 0.0003

im = imread('Zebra.gif') ;

% Laplacian of Gaussian filter f = fspecial('log',[9 9],1.4) ;

im2 = conv2(im,f) ;

imshow(im2) ;

Results

Questions

1b50 – visual system daniel j hulme. errata phylogenetic – genetic history of the species...

Documents

particular ganglion

ganglion cellstherefore

foveal cones

retinathe retina

optic nerve fibermany

conesconverting light

embryological substrate

cone peak responses