OpenCV Training course

1. OpenCVTraining course By Theerayod Wiangtong

2. Goals • Develop a universal toolbox for research and development in the field of Computer Vision

3. Why use OpenCV? • Fast development time, more than 500 algorithms in OpenCV libraries • C/C++ based programming • Both Windows and Linux supported • Open and free, BSD license • Loads of developers using OpenCV • Loads of information and documents • Etc

4. History of OpenCV • Originally developed by Intel, currently maintained by Willow Garage

5. OpenCV - Features Table Courtesy Learning OpenCV: Computer Vision with the OpenCV Library Cross-platform and extremely portable Free! for both research and commercial use Targeted for real-time applications

6. OpenCV – Architecture & Modules • CvAux • Area for experimental algorithms: e.g. HMM, Stereo vision, 3D tracking, Bg/fg segmentation, camera calibration, Shape matching, Gesture recognition, ..

7. OpenCV Comparisons

8. Examples of Using OpenCV functions • Click here

9. OpenCV: Algorithmic Content

10. OpenCV Functionality • Basic structures and operations • Image Analysis • Structural Analysis • Object Recognition • Motion Analysis and Object Tracking • 3D Reconstruction (more than 500 algorithms!!)

11. Image Thresholding • Fixed threshold; • Adaptive threshold;

12. Statistics • min, max, mean value, standard deviation over the image • Multidimensional histograms • Norms C, L1, L2

13. Multidimensional Histograms • Histogram operations : calculation, normalization, comparison, back project

14. Histogram Equalization

15. Histograms comparison

16. Image Pyramids

17. Convolution in image • The source pixel and its surrounding pixels are all mathematically merged to produce a single destination pixel. The matrix slides across the surface of the source image, producing pixels for the destination image http://beej.us/blog/data/convolution-image-processing/

18. Image Pyramids • Gaussian and Laplacian

19. Morphological Operations • Two basic morphology operations using structuring element: • erosion • dilation

20. Distance Transform • Calculate the distance for all non-feature points to the closest feature point • Two-pass algorithm, 3x3 and 5x5 masks, various metrics predefined

21. Flood Filling • grayscale image, floating range • grayscale image, fixed range

22. Feature Detection • Fixed filters (Sobel operator, Canny operator, Laplacian, Scharr filter) • Hough transform (find lines and circles) http://www.stevens-tech.edu/wireless/klin/EdgeDetection/EdgeDetectionInfo.htm

23. Edge detection operators This means: pixel(i,j) = 2*pixel(i,j) - pixel(i,j+1) - pixel(i+1,j). • Simple • Cross Template 1: Template 2: pixel(i,j) = maximum(template 1, template 2)

24. Edge detection operators • Prewitt • Sobel X-axis Template: Y-axis Template: pixel(i,j) = sqrt((x-axis template)^2 + (y-axis template)^2)

25. Canny Edge Detector http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html

26. Hough Transform

27. Contour Retrieving • The contour representation: • Chain code (Freeman code) • Polygonal representation Initial Point Chain code for the curve: 34445670007654443 Contour representation

28. Hierarchical representation of contours Image Boundary (W1) (W2) (W3) (B2) (B3) (B4) (W5) (W6)

29. Contours Examples Source Picture (300x600 = 180000 pts total) Retrieved Contours (<1800 pts total) After Approximation (<180 pts total) And it is rather fast: ~70 FPS for 640x480 on complex scenes

30. Contour Processing • Approximation: • RLE algorithm (chain code) • Teh-Chin approximation (polygonal) • Douglas-Peucker approximation (polygonal); • Contour moments (central and normalized up to order 3) • Matching of contours

31. Contours matching • Matching based on hierarchical representation of contours

32. Object Recognition: Eigen Image

33. Object Recognition: HMM • One person – one HMM • Stage 1 – Train every HMM • Stage 2 – Recognition Pi - probability Choose max(Pi) 1 … n i

34. Motion Analysis and Object Tracking • Background subtraction • Motion templates • Optical flow • Active contours • Estimators

35. Background Subtraction • Background: any static or periodically moving parts of a scene that remain static or periodic over the period of interest. How about waving trees, light on/off..?!?

36. Background statistics functions • Average • Standard deviation • Connect component

37. Background Subtraction Example

38. Motion Templates • Object silhouette • Motion history images • Motion history gradients • Motion segmentation algorithm MHG silhouette MHI

39. Motion Templates Example • Motion templates allow to retrieve the dynamic characteristics of the moving object

40. Objecttracking • Mean-shift • Choose a search window (width and location) • Compute the mean of the data in the search window • Center the search window at the new mean location • Repeat until convergence • Cam-shift: Continuously Adaptive Mean SHIFT

41. Region of interest Mean shift Center of mass Mean Shift vector Slide by Y. Ukrainitz & B. Sarel

42. Region of interest Mean shift Center of mass Mean Shift vector Slide by Y. Ukrainitz & B. Sarel

43. Region of interest Mean shift Center of mass Mean Shift vector Slide by Y. Ukrainitz & B. Sarel

44. Region of interest Mean shift Center of mass Mean Shift vector Slide by Y. Ukrainitz & B. Sarel

45. Region of interest Mean shift Center of mass Mean Shift vector Slide by Y. Ukrainitz & B. Sarel

46. Region of interest Mean shift Center of mass Mean Shift vector Slide by Y. Ukrainitz & B. Sarel

47. Region of interest Mean shift Center of mass Slide by Y. Ukrainitz & B. Sarel

48. Optical Flow Velocity vectors Objecttracking • Optical flow, LK • Particle filter Optical flow is the relation of the motion field. It is a 2D projection of the physical movement of points relative to the observer

49. OpenCV shape classification capabilities • Contour approximation • Moments (image&contour) • Convexity analysis • Pair-wise geometrical histogram • Fitting functions (line, ellipse)

50. Using contours and geometry to classify shapes • Given the contour classify the geometrical figure shape (triangle, circle, etc)