A Brief Introduction to Computer Vision 

Computer vision is automation of tasks that the human visual system can do… 

and overperforming in these tasks! e.g. using thresholding 

… or deep learning =) human error - 4%
 the best machine result - 2.7% clickme 

How is it being used today? 

• 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 

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 

A brief history 

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”. 

–Johnny Appleseed “Type a quote here.” Shakey the Robot (1966 - 1972) 

–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 

–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 

–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 

–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 

–Johnny Appleseed Modern history Deep learning! 

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War Story 

OpenCV 

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 

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 

Motion Detection 

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) 

Optic Approaches 

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 

–Johnny Appleseed Bulk Motion. Problems • cannot detect very slow moving objects • cant handle a rotating object 

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 

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 

Optical Flow It is 2D vector field where each vector is a displacement vector showing the movement of points from first frame to second 

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 

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 

Background Subtraction. Problems if the object stops moving, then it becomes invisible 

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–Johnny Appleseed “Type a quote here.” 

• 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 

Thank you! Any questions?