This slide is a part of Introduction to Computer Vision workshop by Code Heroku. Here is the recorded version of our Introduction to Computer Vision workshop: https://www.youtube.com/watch?v=FdalRh9CBSY You can watch all our upcoming and past workshops here: http://www.codeheroku.com

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2. Mihir Thakkar
Founder and Instructor
hello@codeheroku.com

3. SESSION
OBJECTIVES
● What is Computer Vision?
● Why you should learn it?
● How you should go about
learning it?
● Basics of Image Processing
● Try simple algorithms using
Python and OpenCV

4. Let’s Do A
Visual Rapid
Fire

10. Computer Vision
● What’s in the scene?
● Where? Where is it?
● How to manipulate the object or move around the
scene. (Environmental understanding for robotic
navigation or self-driving vehicles)

11. Let’s Look At Some
Applications

12. Challenges in
building a
Computer Vision
System
● Real world data is
noisy
● Scale / Orientation /
Lighting variations
● There is always a lack
of annotated /
meaningful data

13. Images And Pixels
(0,0) x
y

14. Color Images

15. How Do I Learn Computer Vision?
Image / Video
Capture
Sampling, Quantization, etc..
Pre-processing
Filtering, Thresholding etc..
Feature
Extraction
Contour Detection, Edge
Detection, PCA etc..
ML Model
Supervised
/Unsupervised
Learning
Real World
Manipulation
Localization,
Object
Tracking
Path Planning

16. Thresholding

17. Histogram of a Sample Image

18. Downloads
https://www.codeheroku.com/static/workshop/code/cv/image.jpg
https://www.codeheroku.com/static/workshop/code/cv/sudoku.jpg
https://www.codeheroku.com/static/workshop/code/cv/video.mp4

19. Thank you!
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