human visual system neural network stanley alphonso, imran afzal, anand phadake, putta reddy...

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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10

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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