1b50 – visual system daniel j hulme. errata phylogenetic – genetic history of the species...
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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
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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∑
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
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
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
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 -2
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 -2 -3
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
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0 0 0 0 0 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
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
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0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 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
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 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
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 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
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 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
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 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