Human Visual System Neural Network

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Human Visual System Neural Network

• Stanley Alphonso, Imran Afzal, Anand Phadake,
  Putta Reddy Shankar, and Charles Tappert

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Agenda

• Introduction make a case for the study
• 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

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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
• Cats 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

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Introduction - Computational NeuroscienceBiologic
al 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 cats brain

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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

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Introduction Simple Feedforward Artificial
Neural Network (ANN)
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Introduction - Literature review ofANNs 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

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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 pre-wired
  line and edge detectors

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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

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Alphabetic Input PatternsSix Straight Line
Characters(5 x 7 bit patterns)

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Experiment 1 - ANN without line detectors
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Experiment 1 - ANN without line detectors

• Alphabet character can be placed in any position
  inside the 20x20 retina not adjacent to an edge
  168 (1214) 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

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Input patterns on the retina E(2,2) and E(12,5)

• 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 1 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 1 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 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 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
• 0 1 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 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 1 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 1 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 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 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
• 0 0 0 0 1 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

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Experiment 2 - ANN with line detectors
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Simple horizontal and verticalline detectors

• Horizontal Vertical
• --- --
• --
• --- --

288 horizontal and 288 vertical line detectors
for a total of 576 simple line detectors
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24 complex vertical line detectors and their
feeding 12 simple line detectors
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Results No Line Detectors10 hidden-layer units
Epochs Training Time Training Accuracy Testing Accuracy
50 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
1600 2 days 100 23.8
Average Average 100 27.7
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Results Line Detectors 10 hidden-layer units
Epochs Training Time Training Accuracy Testing Accuracy
50 037 min 47.5 37.5
100 026 min 100.0 63.3
200 051 min 100.0 68.8
400 228 min 71.3 50.8
800 337 min 100.0 67.9
1600 842 min 95.8 56.7
Average Average 85.8 57.5
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Line Detector Results50 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
Average Average 100 72.1
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Confusion Matrix Overall Accuracy of 77.1
Out In E F H I L T
E 62.5 20 0 0 5 12.5
F 12.5 80 0 0 2.5 5
H 0 7.5 85 0 7.5 0
I 0 5 0 95 0 0
L 0 15 2.5 5 72.5 5
T 2.5 20 0 10 0 67.5
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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

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Conclusion - Recognition Accuracy
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Conclusion EfficiencyCompare 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
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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

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Future WorkOther alphabetic input patterns




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Simple horizontal and verticaledge detectors

• ---
• ---
• - -
• - -
• - -

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Questions