The document outlines a computer vision workshop led by Mihir Thakkar, covering its definition, importance, and learning strategies. Key topics include basics of image processing, challenges in building computer vision systems, and practical applications. It also provides resources for further exploration, such as example images and videos.

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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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