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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

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
Outline

Internship Goals

Biological Background

Modeling and Implementation

Validation by Static Images

Validation by Live Stimulus

Conclusion


Outline1
Outline

Internship Goals

Biological Background

Modeling and Implementation

Validation by Static Images

Validation by Live Stimulus

Conclusion


Internship goals
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.


Outline2
Outline

Internship Goals

Biological Background

Modeling and Implementation

Validation by Static Images

Validation by Live Stimulus

Conclusion


Biological background
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.


Output of ganglion cells
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.


Simple cell output
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.


Complex cell
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.


Outline3
Outline

Internship Goals

Biological Background

Modeling and Implementation

Validation by Static Images

Validation by Live Stimulus

Conclusion


Modeling and implementation
Modeling and Implementation

  • Neural networking is the science of creating computational solutions modeled after the brain.

  • Modeling approaches:

    • Hardware

    • Software


Modeling Approach

Software

Hardware



Line orientation on the frame
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.


Ganglion Cell

SUM = Center + Surroundings

-3 = (127 * 8) + (255 * -3) +( 127 * -2) + (-3 * 0)


Simple cell
Simple Cell

GC1+GC2+GC3+GC4 = SUM

If SUM > Threshold

SUM  Simple Cell “45 Degree /offset b” output

GC = Ganglion Cell


Complex cell1
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


Start

Receive Camera Feed

Run ganglion cell function

MAIN LOOP

ALGORITHM

Run Simple cell function

Run Complex Cell Function

Output

Data

End


Start

Receive Camera Feed

Run ganglion cell function

MAIN LOOP

ALGORITHM

Run Simple cell function

Run Complex Cell Function

Output

Data

End


Start

Receive Camera Feed

Run ganglion cell function

MAIN LOOP

ALGORITHM

Run Simple cell function

Run Complex Cell Function

Output

Data

End


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


Start

Receive Camera Feed

Run ganglion cell function

MAIN LOOP

ALGORITHM

Run Simple cell function

Run Complex Cell Function

Output

Data

End


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


Start

Receive Camera Feed

Run ganglion cell function

MAIN LOOP

ALGORITHM

Run Simple cell function

Run Complex Cell Function

Output

Data

End


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


Start

Receive Camera Feed

Run ganglion cell function

MAIN LOOP

ALGORITHM

Run Simple cell function

Run Complex Cell Function

Output

Data

End


Start

Receive Camera Feed

Run ganglion cell function

MAIN LOOP

ALGORITHM

Run Simple cell function

Run Complex Cell Function

Output

Data

End


Outline4
Outline

Internship Goals

Biological Background

Modeling and Implementation

Validation by Static Images

Validation by Live Stimulus

Conclusion


Validation by static image
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










Outline5
Outline

Internship Goals

Biological Background

Modeling and Implementation

Validation by Static Images

Validation by Live Stimulus

Conclusion


Validation by live stimulus
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!




45 degree complex cell output1
45 Degree Complex Cell Output


90 degree complex cell output1
90 Degree Complex Cell Output


135 degree complex cell output1
135 Degree Complex Cell Output


Outline6
Outline

Internship Goals

Biological Background

Modeling and Implementation

Validation by Static Images

Validation by Live Stimulus

Conclusion


Conclusion
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.


Thank you
Thank you!

Any questions?


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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