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Shape Recognition

Shape Recognition . March Program Review Team Tillamook. The Team. Team Members: Kim Tabac (Spring Team Lead) Bethany Nemeth (Fall Team Lead) Hailee Kenney (Web Master) Ross Hallauer ( VIP ) Advisors: Aziz Inan (Faculty) Walt Harrison (Industry). Inspiration .

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Shape Recognition

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  1. Shape Recognition March Program Review Team Tillamook

  2. The Team Team Members: • Kim Tabac (Spring Team Lead) • Bethany Nemeth (Fall Team Lead) • Hailee Kenney (Web Master) • Ross Hallauer (VIP) Advisors: • Aziz Inan (Faculty) • Walt Harrison (Industry)

  3. Inspiration • Image processing = AWESOME! • Analysis and manipulation of digital images • Digital photography • Face recognition technology • Computer graphics (CG) • Industrial applications • Our project: • Manipulate image to simplify analysis • Analyze updated image and interpret

  4. Background • Recognizes and counts the number of shapes (circle, triangle, or square) that pass under the camera

  5. Vision • Increase efficiency and organization • Automation makes device more relatable to real-life applications • User-friendly operation

  6. Parameters • Shapes • Triangle, Circle, Square • Color • Black and White • Orientation

  7. Approach • Sense-Process-Display • Determined components • Arduino MEGA • EEPROM – MOSIS • Camera • ArduCAM/LCD Screen • Stepper Motor

  8. Architecture • Hardware Components

  9. Architecture • Software Components 320x240 8x6

  10. Architecture

  11. Architecture 1000000001 0110110110 1111111111 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1

  12. Architecture • Mechanical Components

  13. Results • No quantitative data • Data represented by comparing expected shape with shape that was recognized

  14. Hardware Challenges • MOSIS • Large schematic • B^2 Logic Limitations • LCD Display • ArduCAM shield • Power supply • Hardware Placement

  15. Software Challenges • Arduino memory limitations • Image data • Large numbers • Poor Documentation • Coordinating Hardware Components • Track, LCD, Camera, On/Off Switch

  16. Demonstration https://www.dropbox.com/s/uy1qmxnhpcagp3t/00001.MTS

  17. Future Enhancements • Recognize other shapes • modify lookup tables • Incorporate color to identify shapes • use RGB values rather than converting averaged RGB pixel values to a black or white value • Reduce image processing time

  18. Conclusion • Project overview • Architecture (hardware, software, mechanical components) • Results • Hardware and software challenges • Demonstration

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