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Presentation sim opencv

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Simulation using OpenCV

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Presentation sim opencv

  1. 1. Simulation with Computer Vision Andres Fernandez
  2. 2. Outline ● Computer Vision – Algorithms – Object Tracking Technique ● Robot Simulation
  3. 3. Computer Vision ● Hough Transform Circle ● Smallest-circle
  4. 4. Hough Transform Circle ● Detects circles ● Outputs the X and Y coordinates ● Outputs the radius
  5. 5. Smallest-circle ● Finds circles ● Outputs the X and Y coordinates ● Outputs the radius ● Finds the smallest circle out of all circles
  6. 6. Computer Vision Object Tracking ● Changing the environment to make calculations easier ● Detecting circles using Hough Transform Circle ● Using the output to feed into simulation
  7. 7. Changing the environment ● Environment color change from RGB to HSV ● HSV stands for Hue Saturation Value ● Hue – Color shown ● Saturation – Color intensity ● Value – Color brightness
  8. 8. Changing the environment Source: http://en.wikipedia.org/wiki/File:Hsl-hsv_models.svg
  9. 9. RGB vs. HSV
  10. 10. Hough Transformer Circle ● Needs three parameters (x, y, r) ● ● Changed the equation to find x and y as the center (x−a) 2 +( y−b) 2 =r 2 x=a+R cos(θ) y=b+R sin(θ)
  11. 11. Hough Transformer Circle ● An 2-dim accumulation array keeps track of the number of intersections ● The pixel with highest count is considered to be center of the circle ● w = width, h = height O(wh(δ R)) δ R=(MaximumRadius−MinimumRadius)
  12. 12. Hough Transformer Circle
  13. 13. Simulation ● A simple 2D animation of a box ● The box represents a robot which follows an object of interest ● The velocity is fixed ● The turning velocity can be changed in the GUI
  14. 14. Simulation
  15. 15. Threading ● Three threads total ● GUI – Main thread ● Image Processing – Child Thread ● Robot Simulation – Child Thread
  16. 16. Scheduling ● Image thread
  17. 17. Scheduling ● Simulation thread
  18. 18. Improvements ● Background Subtraction ● OpenCL (Multicore) ● More Benchmarking Analysis ● Extending Simulation (more variables) ● Real Time Operating System (RTOS)
  19. 19. Questions
  20. 20. References ● Alper Yilmaz, Omar Javed, and Mubarak Shah. 2006. Object tracking: A survey. ACM Comput. Surv. 38, 4, Article 13 (December 2006) ● Canny, John, "A Computational Approach to Edge Detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.PAMI-8, no.6, pp.679,698, Nov. 1986 ● H. K. Yuen, J. Princen, J. Illingworth, and J. Kittler. 1990. Comparative study of Hough transform methods for circle finding. Image Vision Comput. 8, 1 (February 1990), 71-77 ● Jasmin Blanchette and Mark Summerfield , “C++ GUI Programming with Qt 4”, 2nd ed., Prentice Hall, 2008
  21. 21. Extra - Reasons ● Why use actual sensors to connect to the simulation? ● What framework to use to display the video and animation?
  22. 22. Extra - Technical Specifications ● Intel Core i7 ● 6 Gigabytes of Memory ● OpenCV 2.4.8 ● Webcam: 15 MP ● Webcam: 30 FPS
  23. 23. Extra – Edge Detection ● Edge Detection is used to simplify the image ● The Hough Transform Circle function uses Canny and Sobel to detect edges ● Sobel is a predecessor of Canny ● Canny and Sobel is a multi step process
  24. 24. Extra – Canny Edge Detection ● Color to Gray scale ● Gaussian filter ● Gradient Processing ● Non-maximum suppression ● Tracing edges ● Hysteresis Thresholding

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