A brief introduction to computer vision

On this workshop, we are going to get acquainted with the basics of the computer vision theory, look into an open source computer vision library OpenCV, study algorithms of motion detection and implement our first application for the smart home.

Transcript

1. A Brief Introduction to Computer Vision
2. Computer vision is automation of tasks that the human visual system can do…
3. and overperforming in these tasks! e.g. using thresholding
4. … or deep learning =) human error - 4%
 the best machine result - 2.7% clickme
5. How is it being used today?
6. • Optical character recognition (a) • Machine inspection (b) • Retail (c) • 3D model building • Medical imaging (d) • Automotive safety (e) • Match move • Motion capture • Surveillance (f) • Fingerprint recognition and biometrics
7. Consumer-level Applications • Stitching (a) • Exposure bracketing (b) • Morphing (c) • 3D modeling (d) • Video match move and stabilization • Photo-based walkthroughs • Face detection • Visual authentication
8. A brief history
9. 1966: Marvin Minsky at MIT asked his undergraduate student to “spend the summer linking a camera to a computer and getting the computer to describe what it saw”.
10. –Johnny Appleseed “Type a quote here.” Shakey the Robot (1966 - 1972)
11. –Johnny Appleseed Some early (1970s) examples of computer vision algorithms (a) line labeling (b) pictorial structures (c) articulated body model (generalised cylinders) (d) intrinsic images (e) stereo correspondence (f) optical flow
12. –Johnny Appleseed Examples of computer vision algorithms from the 1980s (a) image blending (b) shape from shading (c) edge detection (d) physically based models (e) regularisation-based surface reconstruction (f) range data acquisition and merging
13. –Johnny Appleseed Examples of computer vision algorithms from the 1990s (a) factorisation-based structure from motion (b) dense stereo matching (c) multi-view reconstruction (d) face tracking (e) image segmentation (f) face recognition
14. –Johnny Appleseed Modern history (a) image-based rendering (b) image-based modelling (c) interactive tone mapping (d) texture synthesis (e) feature-based recognition (f) region-based recognition
15. –Johnny Appleseed Modern history Deep learning!
17. War Story
18. OpenCV
19. Back then… • launched in 1999 by Intel's research center in Nizhny Novgorod (Russia) • later supported by Willow Garage and is now maintained by Itseez • In August 2012, support for OpenCV was taken over by a non-profit foundation OpenCV.org
20. And now • BSD-licensed product • more than 2500 optimised algorithms (both classic and state-of-the-art computer vision and machine learning algorithms) • more than 47,000 people of user community • C++, C, Python, Java and MATLAB interfaces • supports Windows, Linux, Mac OS, Android and iOS • full-featured CUDA and OpenCL interfaces are being actively developed
21. Motion Detection
22. Approaches • Infrared (passive and active sensors) • Optics (video and camera systems) • Radio Frequency Energy (radar, microwave and tomographic motion detection) • Sound (microphones and acoustic sensors) • Vibration (triboelectric, seismic, and inertia- switch sensors) • Magnetism (magnetic sensors and magnetometers)
23. Optic Approaches
24. Bulk Motion calculate the middle mass in frame 1 wait X seconds calculate the middle mass in frame 2 if (mm_frame_1 - mm_frame_2) > threshold) then motion detected or calculate the average of a selected color in frame 1 wait X seconds calculate the average of a selected color in frame 2 if (abs(avg_frame_1 - avg_frame_2) > threshold) then motion detected
25. –Johnny Appleseed Bulk Motion. Problems • cannot detect very slow moving objects • cant handle a rotating object
26. Tracking calculate the middle mass in frame 1 wait X seconds calculate the middle mass in frame 2 speed = (mm_frame_1 - mm_frame_2) * distance/per_pixel
27. Tracking. Problems • might be difficult to determine the distance to pixel ratio if your camera is at an angle to the horizon or experiences the lens effect • when multiple objects cross over each other the algorithm gets confused which blob is which
28. Optical Flow It is 2D vector field where each vector is a displacement vector showing the movement of points from first frame to second
29. Optical Flow. Problems It works on such assumptions: • the pixel intensities of an object do not change between consecutive frames • neighbouring pixels have similar motion
30. Background Subtraction capture two frames compare the pixel colors on each frame if the colors are the same replace with the color white else keep the new pixel
31. Background Subtraction. Problems if the object stops moving, then it becomes invisible
33. –Johnny Appleseed “Type a quote here.”
34. • http://opencv.org/books.html • http://opencv.org/links.html • http://szeliski.org/Book/ • https://www.intorobotics.com/opencv-tutorials-best-of/ • https://classroom.udacity.com/courses/ud810 • https://cw.fel.cvut.cz/wiki/courses/ae4m33mpv/labs/start • http://www.pyimagesearch.com/free-opencv-crash-course • QUT robotics courses https://www.qut.edu.au/study/open-online-learning Good-reads
35. Thank you! Any questions?