Human Visual System
Neural Network
Stanley Alphonso, Imran Afzal,
Anand Phadake, Putta Reddy
Shankar, and Charles Tappert
Agenda
• Introduction – make a case for the study
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The Visual System
Biological Simulations of the Visual System
Machine Learning and Artificial Neural Networks (ANNs)
ANNs Using Line and/or Edge Detectors
Current Study
Methodology
Experimental Results
Conclusions
Future Work
Introduction - The Visual System
• The Visual System Pathway
– Eye, optic nerve, lateral geniculate nucleus, visual cortex
• Hubel and Wiesel
– 1981 Nobel Prize for work in early 1960s
– Cat’s visual cortex
• cats anesthetized, eyes open with controlling muscles paralyzed
to fix the stare in a specific direction
• thin microelectrodes measure activity in individual cells
• cells specifically sensitive to line of light at specific orientation
– Key discovery – line and edge detectors
Introduction - Computational Neuroscience
Biological Simulations of the Visual System
• Hubel-Wiesel discoveries instrumental in the creation
of what is now called computational neuroscience
• Which studies brain function in terms of information
processing properties of structures that make up the
nervous system
• Creates biologically detailed models of the brain
• 18 November 2009 – IBM announced they created
the largest brain simulation to date on the Blue Gene
supercomputer – millions of neurons and billions of
synapses exceeding those in the cat’s brain
Introduction –
Artificial Neural Networks (ANNs)
• Machine learning scientists have taken a
different approach using simpler neural
network models called ANNs
• Commonest type used in pattern recognition is
a feedforward ANN
• Typically consists of 3 layers of neurons
– Input layer
– Hidden layer
– Output layer
Introduction – Simple Feedforward
Artificial Neural Network (ANN)
Introduction - Literature review of
ANNs using line/edge detectors
• GIS images/maps – line and edge detectors in
four orientations – 0°, 45°, 90°, and 135°
• Synthetic Aperture Radar (SAR) images – line
detectors constructed from edge detectors
• Line detection can be done using edge
techniques such as Sobel, Prewitt, Laplacian
Gaussian, Zero Crossing and Canny edge
detector
Introduction - Current Study
• Use ANNs to simulate line and edge detectors
known to exist in the human visual cortex
• Construct two feedforward ANNs – one with
line detectors and one without – and compare
their accuracy and efficiency on a character
recognition task
• Demonstrate superior performance using prewired line and edge detectors
Methodology
• Character recognition task - classify straight
line uppercase alphabetic characters
• Experiment 1 – ANN without line detectors
• Experiment 2 – ANN with line detectors
• Compare
– Recognition accuracy
– Efficiency – training time
Alphabetic Input Patterns
Six Straight Line Characters
(5 x 7 bit patterns)
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Experiment 1 - ANN
without line detectors
Experiment 1 - ANN
without line detectors
• Alphabet character can be placed in any position
inside the 20x20 retina not adjacent to an edge –
168 (12*14) possible positions
• Training – choose 40 random non-identical positions
for each of the 6 characters (~25% of patterns)
– Total of 240 (40 x 6) input patterns
– Cycle through the sequence E, F, H, I, L, T forty times for
one pass (epoch) of the 240 patterns
• Testing – choose another 40 random non-identical
positions for each character for total 240
Input patterns on the retina
E(2,2) and E(12,5)
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Experiment 2 - ANN
with line detectors
Simple horizontal and vertical
line detectors
Horizontal
--+++++
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Vertical
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288 horizontal and 288 vertical line detectors
for a total of 576 simple line detectors
24 complex vertical line detectors and
their feeding 12 simple line detectors
Results – No Line Detectors
10 hidden-layer units
Epochs
Training
Time
Training
Accuracy
Testing
Accuracy
~2.5 hr
100%
26.7%
100
~4 hr
100%
28.3%
200
~8 hr
100%
28.8%
400
~16 hr
100%
30.4%
800
~30 hr
100%
28.3%
~2 days
100%
23.8%
100%
27.7%
50
1600
Average
Results – Line Detectors
10 hidden-layer units
Epochs
Training
Time
Training
Accuracy
Testing
Accuracy
50
0:37 min
47.5%
37.5%
100
0:26 min
100.0%
63.3%
200
0:51 min
100.0%
68.8%
400
2:28 min
71.3%
50.8%
800
3:37 min
100.0%
67.9%
1600
8:42 min
95.8%
56.7%
85.8%
57.5%
Average
Line Detector Results
50 hidden-layer units
Epochs Set/
Attained
Training
Time
Training
Accuracy
Testing
Accuracy
50/8
41 sec
100%
70.0%
100/9
45 sec
100%
69.8%
200/10
48 sec
100%
71.9%
400/10
49 sec
100%
77.1%
800/8
41 sec
100%
72.5%
1600/9
45 sec
100%
71.3%
100%
72.1%
Average
Confusion Matrix
Overall Accuracy of 77.1%
Out
In
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Conclusion - Efficiency
• ANN with line detectors resulted in a
significantly more efficient network
– training time decreased by several orders
of magnitude
Conclusion - Recognition Accuracy
100
90
Line detectors
50 hidden units
80
Line detectors
10 hidden units
70
60
50
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No line detectors
10 hidden units
Conclusion – Efficiency
Compare Fixed/Variable Weights
Experiment
Fixed
Weights
Variable
Weights
Total
Weights
1 No Line
Detectors
0
20,300
20,300
2 Line
Detectors
6,912
2,700
9,612
Conclusion
• The strength of the study was its simplicity
• The weakness was also it simplicity and that
the line detectors appear to be designed
specifically for the patterns to be classified
• Weakness can be corrected in future work
Future Work
Other alphabetic input patterns
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Simple horizontal and vertical
edge detectors
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Questions