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Computer Engineering Group

Computer Engineering Group. Norman E., Arash K. Rita M., Hector P. Advisor: Dr. Mahmoodi Student Mentor: Maral A. Modeling and Implementation of Brain-Inspired Neural Network for Edge Detection and Object Recognition. Outline. Internship Goals Biological Background

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Computer Engineering Group

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  1. Computer Engineering Group Norman E., Arash K. Rita M., Hector P. Advisor: Dr. Mahmoodi Student Mentor: Maral A. Modeling and Implementation of Brain-Inspired Neural Network for Edge Detection and Object Recognition

  2. Outline Internship Goals Biological Background Modeling and Implementation Validation by Static Images Validation by Live Stimulus Conclusion

  3. Outline Internship Goals Biological Background Modeling and Implementation Validation by Static Images Validation by Live Stimulus Conclusion

  4. Internship Goals Successfully create a software-based model of a brain-inspired neural network responsible for object orientation/edge detection. Validate the developed model. Establish a framework for further expansion of the design for incorporation of more visual processing tasks.

  5. Outline Internship Goals Biological Background Modeling and Implementation Validation by Static Images Validation by Live Stimulus Conclusion

  6. Biological Background Hubel and Wiesel: 1958 discovered cells in the visual cortex that had orientation selectivity. 1974 they wrote a paper about the geometry of orientation columns. Gary Blasdel: 1985 he used a 2D technique to visualize orientation columns. Confirmed Hubel and Wiesel’s studies.

  7. Output of Ganglion Cells • The firing rate of a ganglion cell is a measure of the illumination difference between the center and it’s surrounding fields. • The following image characterizes the firing rate action potentials from a ganglion cell.

  8. Simple Cell Output • A simple cell monitors a line or bar stimulus of a specific orientation in a specific location in the back of the eye.

  9. Complex Cell • A complex cell monitors a line or bar stimulus with a specific orientation in a general region in the back of the eye.

  10. Outline Internship Goals Biological Background Modeling and Implementation Validation by Static Images Validation by Live Stimulus Conclusion

  11. Modeling and Implementation • Neural networking is the science of creating computational solutions modeled after the brain. • Modeling approaches: • Hardware • Software

  12. Modeling Approach Software Hardware

  13. Ganglion Cell : Biological Vs. Digital

  14. Line Orientation on the Frame • Ganglion Cells are represented by 1 center cell (blue) and 8 surrounding cells (purple). • The cells that receive uniform light produce no response. • The cells with no uniform light produce a response.

  15. Ganglion Cell SUM = Center + Surroundings -3 = (127 * 8) + (255 * -3) +( 127 * -2) + (-3 * 0)

  16. Simple Cell GC1+GC2+GC3+GC4 = SUM If SUM > Threshold SUM  Simple Cell “45 Degree /offset b” output GC = Ganglion Cell

  17. Complex Cell • SC1 = GC1 + GC2 • SC2 = GC3+GC4+GC5 • SC3 = GC6 + GC7 • SC1+SC2+SC3 = SUM • IF SUM > Threshold • SUM Complex Cell 45 degree output. SC3 • SC = Simple Cell • GC = Ganglion Cell SC1 SC2

  18. Start Receive Camera Feed Run ganglion cell function MAIN LOOP ALGORITHM Run Simple cell function Run Complex Cell Function Output Data End

  19. Start Receive Camera Feed Run ganglion cell function MAIN LOOP ALGORITHM Run Simple cell function Run Complex Cell Function Output Data End

  20. Start Receive Camera Feed Run ganglion cell function MAIN LOOP ALGORITHM Run Simple cell function Run Complex Cell Function Output Data End

  21. Start Camera Output 2D array GANGLION CELL ALGORITHM Multiply center by 8 Multiply surround by -1 Place data into new 2D array Output smaller array to Simple Cell End

  22. Start Receive Camera Feed Run ganglion cell function MAIN LOOP ALGORITHM Run Simple cell function Run Complex Cell Function Output Data End

  23. Start SIMPLE CELL ALGORITHM Get Ganglion Cell 2D Array Set Slope Scan Ganglion Cell Array Value > Threshold False True Complex Cell Array = 0 Complex Cell Array = Value Reset Variables Increase Offset Offset > Array Size True False End

  24. Start Receive Camera Feed Run ganglion cell function MAIN LOOP ALGORITHM Run Simple cell function Run Complex Cell Function Output Data End

  25. Start Simple Cell Output (Simple Array) COMPLEX CELL ALGORITHM Add array Values If sum < 0 Make Positive True False If sum > threshold Output zero False True Output sum End

  26. Start Receive Camera Feed Run ganglion cell function MAIN LOOP ALGORITHM Run Simple cell function Run Complex Cell Function Output Data End

  27. Start Receive Camera Feed Run ganglion cell function MAIN LOOP ALGORITHM Run Simple cell function Run Complex Cell Function Output Data End

  28. Outline Internship Goals Biological Background Modeling and Implementation Validation by Static Images Validation by Live Stimulus Conclusion

  29. Validation by Static Image Part I Image: 900 by 900 pixel frame Program processed the data from one static frame Total data: 810000 pixel values

  30. Testing 0 Degree

  31. 0 Degree Complex Cell Output

  32. 45 Degrees 45°

  33. 45 Degree Complex Cell Output

  34. 90 Degrees

  35. 90 Degree Complex Cell Output

  36. 135 Degrees

  37. 135 Degree Complex Cell Output

  38. Outline Internship Goals Biological Background Modeling and Implementation Validation by Static Images Validation by Live Stimulus Conclusion

  39. Validation by Live Stimulus Part II • Image: 640 by 480 pixel frame • Program processed data from live webcam: 30FPS (Frames per Second) • Total data: 307,200 pixels per frame 9,216,000 pixel values per second!

  40. Live Camera Angle Detection

  41. 0 Degree Complex Cell Output

  42. 45 Degree Complex Cell Output

  43. 90 Degree Complex Cell Output

  44. 135 Degree Complex Cell Output

  45. Outline Internship Goals Biological Background Modeling and Implementation Validation by Static Images Validation by Live Stimulus Conclusion

  46. Conclusion Successfully recreated hardware model in software. Increased frame size and parameterized it for adaptability. Angle support is limited by the current algorithm. Static images provide more stable feedback. Live stream data collection fluctuates more than intended presumably due to low resolution input. This makes it hard to provide similar setting for data collection.

  47. Thank you! Any questions?

  48. References Chan, Michael, “Hardware Modeling and Implementation of Neural Network for Orientation Selectivity of the Eye”, 2012.  Hubel, H. David, “Vision and the Brain”, Bulletin of the American Academy of Arts and Sciences  Vol. 31 No. 7,  Internet: http://www.jstor.org/stable/3823436, 1978 [July 29, 2013]. Jessell, T., Kandel, E., Schwartz, J. “Principles of Neural Science”, 2000. Zeki, Semir, “The Ferrier Lecture 1995: Behind the Seen: The Functional Specialization of the Brain in Space and Time”, Philosophical Transactions: Biological Sciences, Vol. 360, No. 1458, Internet: http://www.jstor.org/stable/300413352005 [August 1, 2013]. http://hubel.med.harvard.edu/book/b17.htm

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