Human Visual System Neural Network

1. Human Visual System Neural Network Stanley Alphonso, Imran Afzal, Anand Phadake, Putta Reddy Shankar, and Charles Tappert

2. 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

3. 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

4. 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

5. 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

6. Introduction – Simple Feedforward Artificial Neural Network (ANN)

7. 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

8. 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

9. 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

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

11. Experiment 1 - ANN without line detectors

12. 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

13. Input patterns on the retinaE(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 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

14. Experiment 2 - ANN with line detectors

15. Simple horizontal and verticalline detectors Horizontal Vertical + --- -+- +++++ -+- --- -+- + 288 horizontal and 288 vertical line detectors for a total of 576 simple line detectors

16. 24 complex vertical line detectors and their feeding 12 simple line detectors

17. Results – No Line Detectors10 hidden-layer units

18. Results – Line Detectors 10 hidden-layer units

19. Line Detector Results50 hidden-layer units

20. Confusion Matrix Overall Accuracy of 77.1%

21. Conclusion - Efficiency • ANN with line detectors resulted in a significantly more efficient network • training time decreased by several orders of magnitude

22. Conclusion - Recognition Accuracy

23. Conclusion – EfficiencyCompare Fixed/Variable Weights

24. 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

25. Future WorkOther alphabetic input patterns * **** *** * * * * * * * * * * * * * **** * ***** * * * * * * * * * * * **** ***

26. Simple horizontal and verticaledge detectors --- +++ +++ --- -+ +- -+ +- -+ +-

27. Questions