Introduction to

Computer Vision-

Beginners' Session

By - Brihi Joshi and Shravika Mittal

WiFi

AP - 91springboard

 

user - Guest@del.okhla

pass - 91okhla

About Us

@brihijoshi

brihi16142@iiitd.ac.in

+91-8812012300

@mittalshravika

shravika16093@iiitd.ac.in

+91-9650357613

•Students – Brihi and Shravika

•Mentor – Kelsey

•Coaches –

  * Dana

  * Jigyasa

  * Umair

  * Sanchit

  * Divam

  * Diksha

•Supervisor – Vaishali

The Team

The Project

Tessel is a robust IoT and robotics development platform. 

This year

20 teams

40 students

 

16 Sponsored Teams + 4 Volunteer Teams

....all over the world!

Some Projects

  • Tessel
  • Coala
  • OpenFarm
  • Susi AI Server
  • NextCloud
  • Babel
  • Servo
  • Foodsaving and Foodsharing

Let's check this out!

Computer Generated Cats

Real Cats

AI to rule the world!

...you're here too!

How to make a computer SEE?

MATH!

Activity Time

Describe a sunset to a computer!

Vision is HARD.

Vision is an amazing feat of natural intelligence

– Visual cortex occupies about 50% of Macaque brain

– More human brain devoted to vision than anything else

History time 🙌🏼

  • 1966: Minsky assigns computer vision as an undergrad summer project

  • 1960’s: interpretation of synthetic worlds

  • 1970’s: some progress on interpreting selected images

  • 1980’s: ANNs come and go; shift toward geometry and increased mathematical rigor

  • 1990’s: face recognition; statistical analysis in vogue

  • 2000’s: broader recognition; large annotated datasets available; video processing starts

Anyone Remembers this picture?

Workflow

Image Processing 

Low-level image processing algorithms include:
1. Edge detection
2. Segmentation
3. Classification
4. Feature detection and matching

Image Processing 

Computer Vision

 Determines if the actual analysis of the data, which will allow the decision making.
High-level algorithms are applied, using both the image data and the low-level information computed in previous steps.

 

Examples of high-level image analysis are:
1. 3D scene mapping
2. Object recognition
3. Object tracking

Computer Vision

Determines if the actual analysis of the data, which will allow the decision making.
High-level algorithms are applied, using both the image data and the low-level information computed in previous steps.

 

Examples of high-level image analysis are:
1. 3D scene mapping
2. Object recognition
3. Object tracking

Computer Vision

Let's Begin!

Installations!

https://github.com/brihijoshi/Intro-to-CV

How are Images represented in a computer?

Python Libraries 🤘🏼

OpenCV

 

  • Most common tool
  • Focuses on implementation of ML algorithms on Images
  • Python Wrapper over a C/C++ compiler!

PIL

  • Now known as Pillow, which is a famous fork of PIL (Python Imaging Library)
  • Focuses on low-level image processing

scikit-image

  • Part of the famous Scikit-learn family
  • Similar to OpenCV in terms of use case
  • Simpler Syntax

Basics of GrayScale Images

Gray scale doesn't necessarily mean only black and white pixels. Any two colours, whose pixel values vary in Intensity.

Binary images - Only two colours + solid colours

 

Gray Scale images - Two colours + different intensity of images

Intensity \in [0,255]
Intensity∈[0,255]Intensity \in [0,255]

How would we represent a Gray Scale Image?

Basics of Colour 🎨

RGB

RGB is an additive color model. It means that different proportions of Red, Blue and Green light can be used to produce color. The RGB color model was created specifically for display purposes (display screens, projectors etc).

A key concept one should understand is that RGB is simply an additive combination of narrow band waves.

OpenCV follows BGR model!

HSV

  • The hue (H) of a color refers to which pure color it resembles. All tints, tones and shades of red have the same hue.

    Hues are described by a number that specifies the position of the corresponding pure color on the color wheel, as a fraction between 0 and 1. Value 0 refers to red; 1/6 is yellow; 1/3 is green; and so forth around the color wheel.

  • The saturation (S) of a color describes how white the color is. A pure red is fully saturated, with a saturation of 1; tints of red have saturations less than 1; and white has a saturation of 0.

  • The value (V) of a color, also called its lightness, describes how dark the color is. A value of 0 is black, with increasing lightness moving away from black.

How to represent coloured images?

Each pixel has an R,G,B value. OpenCV follows BGR pattern

 

For Gray Scale Images?

 

 

http://math.hws.edu/graphicsbook/demos/c2/rgb-hsv.html

Let's get a little Math!

AND/OR/XOR Operations

Bitwise operations?

Ooooh..

 

Matrices!

Transformation Matrices?

Translation

Rotation

Let's think a little

 

What if a computer has to make edges?

 

....to be continued.

Thanks to - 

 

  • http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html
  • https://dsp.stackexchange.com/questions/2687/why-do-we-use-the-hsv-colour-space-so-often-in-vision-and-image-processing

Some cool resources :)

  • http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html
  • http://cs.nyu.edu/~fergus/teaching/vision/index.html
  • http://crcv.ucf.edu/courses/CAP5415/Fall2014/index.php
  • https://courses.engr.illinois.edu/cs543/sp2015/
  • https://github.com/jbhuang0604/awesome-computer-vision

fin.

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