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– 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 NeuroscienceBiological 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 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

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

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 PatternsSix Straight Line Characters

(5 x 7 bit patterns)

***** ***** * * * * ***** * * * * * * * * * * * * * * **** **** ***** * * * * * * * * * * * * * * * * * ***** * * * * ***** *

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)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 00 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 00 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Experiment 2 - ANN with line detectors

Simple horizontal and verticalline detectors

Horizontal 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 Detectors10 hidden-layer units

Epochs TrainingTime

TrainingAccuracy

TestingAccuracy

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 100% 27.7%

Results – Line Detectors 10 hidden-layer units

Epochs TrainingTime

TrainingAccuracy

TestingAccuracy

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%

Average 85.8% 57.5%

Line Detector Results50 hidden-layer units

Epochs Set/ Attained

TrainingTime

TrainingAccuracy

TestingAccuracy

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 100% 72.1%

Confusion Matrix Overall Accuracy of 77.1% OutIn

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

Conclusion - Efficiency

• ANN with line detectors resulted in a significantly more efficient network– training time decreased by several orders

of magnitude

Conclusion - Recognition Accuracy

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90

100

No line detectors 10 hidden units

Line detectors 10 hidden units

Line detectors 50 hidden units

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

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 WorkOther alphabetic input patterns

* **** *** * * * * * * * * * * * * * **** * ***** * * * * * * * * * * * **** ***

Simple horizontal and verticaledge detectors

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Questions