1. 1B50 – Visual System Daniel J Hulme

2. 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)

3. 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

4. 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

5. 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

6. Radio Frequency Spectrum 419 531 559 Cone Peak Responses

7. 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

8. Only cones in the fovea Extreme periphery of retina – only rods 126 million rods 4 million cones Retina Layout

9. Converting light into electricity γ Photon Rhodopsin molecule Photon absorbed Molecule changes shape Alters flow of current in molecule Electrical changes propagate to synapse Neurotransmitters affect the next neuron

10. 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

11. 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

12. Receptive Fields Ganglion Cell Positive Weight Negative Weight output : Rod or Cone

13. Finding Edges - Setup 0000000000 0000000000 0000000000 0001111000 0001111000 0001111000 0001111000 0000000000 0000000000 0000000000 8 Simple image Simple filter (kernel)

14. Finding Edges - Convolutions 0000000000 0000000000 0000000000 0001111000 0001111000 0001111000 0001111000 0000000000 0000000000 0000000000 8 000 011 011 000 08 0 5 ∑

15. Finding Edges - Convolutions 0000000000 0000000000 0000000000 0001111000 0001111000 0001111000 0001111000 0000000000 0000000000 0000000000 0

16. 0000000000 0000000000 0000000000 0001111000 0001111000 0001111000 0001111000 0000000000 0000000000 0000000000 00

17. Finding Edges – Convolutions 0000000000 0000000000 0000000000 000111000 0001111000 0001111000 0001111000 0000000000 0000000000 0000000000 00000000 0

18. Finding Edges – Convolutions 0000000000 0000000000 0000000000 000 11000 0001111000 0001111000 0001111000 0000000000 0000000000 0000000000 00000000 0 -2

19. Finding Edges – Convolutions 0000000000 0000000000 0000000000 000 1000 0001111000 0001111000 0001111000 0000000000 0000000000 0000000000 00000000 0 -2-3

20. Finding Edges – Convolutions 0000000000 0000000000 0000000000 0001111000 0001111000 0001111000 0001111000 0000000000 0000000000 0000000000 00000000 0-2-3 -20 0

21. Finding Edges – Convolutions 0000000000 0000000000 0000000000 000111000 000 111000 0001111000 0001111000 0000000000 0000000000 0000000000 00000000 0 -2-3 -20 0-2

22. Finding Edges – Convolutions 0000000000 0000000000 0000000000 000811000 000 11000 0001111000 0001111000 0000000000 0000000000 0000000000 00000000 0 -2-3 -20 0-25

23. Finding Edges – Convolutions 0000000000 0000000000 0000000000 0008 1000 000 1000 0001111000 0001111000 0000000000 0000000000 0000000000 00000000 0 -2-3 -20 0-253

24. Finding Edges – Convolutions 0000000000 0000000000 0000000000 00018 000 0001 000 0001111000 0001111000 0000000000 0000000000 0000000000 00000000 0 -2-3 -20 0-2533

25. Finding Edges – Convolutions 0000000000 0000000000 0000000000 000 1000 000 8 1000 000 1000 0001111000 0000000000 0000000000 0000000000 00000000 0 -2-3 -20 0-25335 0 0-33003 0 0 3003 0 0-25335 0 0-2-3 -20 00000000

26. Finding Edges – Convolutions 00000000 00000000 00111100 00100100 00100100 00111100 00000000 00000000 0000000000 0000000000 0000000000 0001111000 0001111000 0001111000 0001111000 0000000000 0000000000 0000000000

27. Edge Detection Example 0.00030.00090.00220.00380.00450.00380.00220.00090.0003 0.00090.00320.00710.01020.0110.01020.00710.00320.0009 0.00220.00710.01140.00650.00080.00650.01140.00710.0022 0.00380.01020.0065-0.0243-0.0478-0.02430.00650.01020.0038 0.00450.0110.0008-0.0478-0.0829-0.04780.00080.0110.0045 0.00380.01020.0065-0.0243-0.0478-0.02430.00650.01020.0038 0.00220.00710.01140.00650.00080.00650.01140.00710.0022 0.00090.00320.00710.01020.0110.01020.00710.00320.0009 0.00030.00090.00220.00380.00450.00380.00220.00090.0003 im = imread('Zebra.gif') ; % Laplacian of Gaussian filter f = fspecial('log',[9 9],1.4) ; im2 = conv2(im,f) ; imshow(im2) ;

28. Results

29. Questions