Introduction

Prologue

This guide is a culmination of whatever knowledge I have acquired of this fascinating topic over the course of three weeks and developing An Experimental computer vision library in C.

I am neither a CS Grad nor a maths major, all info I have is sourced from the web and a Computer Vision: Algorithms and Applications, 2nd ed. by Szeliski (great read), Please don't hesitate to critique any of this work or provide some feedback. Thanks!

What is Computer Vision anyway?

Computer vision is an umbrella term that encapsulates several complex topics, but to distil it simply, it is about "how a computer sees the world" . Let's try to further understand the medium in which this computer does so.

Any visual data, whether from an image or a video, is essentially a series of pixels, where each pixel represents an RGB value (or HSL, RGBA), and each integer holds a value between 0 and 255, describing the intensity of the colour channel it represents. So with this, it would not be wrong to say that an image is an array or list of integer values, and computer vision involves processing and analysing this list to make sense of the visual information it contains.

Image {
  Data  	= 255, 0, 128
          	0, 128, 255
          	128, 255, 0
          	64, 192, 128
          	128, 64, 192
  Width 	= 30
  Height	= 30
  Channels  = 3
}

Through this abstract representation we can essentially describe any image possible (in this case videos would basically be a series of images). So then Computer vision essentially becomes application of mathematical transformations on an array of integers.

CV has its own cool and useful application; From the clever filters in Photoshop to complex real time "spatial" computing (AR/VR), the key to it all is a little bit of maths and a bit of wizardry. As early as the 60s humans have been trying to figure out how they can apply computation to visual data. In my opinion the field really took off around 2009 on Google's introduction of robot cars, which ultimately led to the self-driving car master-race which truly tested our capabilities of image processing and machine learning.

Throughout this guide I will stick to the theme of KISS (Keep it simple, stupid!) each section is small since building is the best way to learn, so I urge you to do so as well.

Follow along

Throughout this module I would be referencing certain code. I highly advise as you follow this guide you have a copy of aadv1k/cv.c locally installed. This will help you mess around with the examples and break things.

Build (Windows)

git clone [e-mail]:aadv1k/cv.c
cd cv.c/
.\build.bat

All the commands provided along with the code are runnable provided the build was successful.