Simulation using OpenCV

1. Simulation with Computer Vision
Andres Fernandez

2. Outline
● Computer Vision
– Algorithms
– Object Tracking Technique
● Robot Simulation

3. Computer Vision
● Hough Transform Circle
● Smallest-circle

4. Hough Transform Circle
● Detects circles
● Outputs the X and Y coordinates
● Outputs the radius

5. Smallest-circle
● Finds circles
● Outputs the X and Y coordinates
● Outputs the radius
● Finds the smallest circle out of all circles

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. 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. Changing the environment
Source: http://en.wikipedia.org/wiki/File:Hsl-hsv_models.svg

9. RGB vs. HSV

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. 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. Hough Transformer Circle

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. Simulation

15. Threading
● Three threads total
● GUI – Main thread
● Image Processing – Child Thread
● Robot Simulation – Child Thread

16. Scheduling
● Image thread

17. Scheduling
● Simulation thread

18. Improvements
● Background Subtraction
● OpenCL (Multicore)
● More Benchmarking Analysis
● Extending Simulation (more variables)
● Real Time Operating System (RTOS)

19. Questions

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. Extra - Reasons
● Why use actual sensors to connect to the
simulation?
● What framework to use to display the video and
animation?

22. Extra - Technical Specifications
● Intel Core i7
● 6 Gigabytes of Memory
● OpenCV 2.4.8
● Webcam: 15 MP
● Webcam: 30 FPS

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. Extra – Canny Edge Detection
● Color to Gray scale
● Gaussian filter
● Gradient Processing
● Non-maximum suppression
● Tracing edges
● Hysteresis Thresholding