introduction to computer vision part00-1

introduction to computer vision

1. CV Lab 01 Lab introduction

2. Outline Course plan Computer vision overview Required software

3. Course Plan

4. Course Plan (Lab) Week Topic 1 Lab introduction 2 Image processing with OpenCV 3 Connected component labelling 4 Optical Mark Recognition (OMR) 5 Feature extraction (Histogram of chain code) and classification 6 Deep learning 7 More on deep learning: augmentation, pretrained models 8 Object detection (YOLO) 9 Face recognition 10 Object tracking (optical flow) 11 Generative models

5. Grading Lab attendance 5 marks Lab work 5 marks Final project 20 marks Total 30 marks

6. Computer vision overview

7. Computer vision Make the computer understand images and videos

8. Computer vision Computer vision is a field of artificial intelligence Artificial Intelligence Machine learning Natural language processing Computer vision

9. Computer vision Computer vision techniques Image processing + Static rules Feature descriptors + Machine learning Deep learning Simple, more predictable, requires less/no data but less flexible Complex, flexible, but unpredictable (decision is not clear), and requires more data

10. Computer vision Example: Image processing + static rules Counting number of shapes in an image Number of shapes = 2 Input image Binary image Connected components

11. Computer vision Example: Feature descriptor + Machine Learning Machine learning can’t work directly on images Input image Feature descriptor algorithm Features (numbers) Machine learning model This is a cat

12. Computer vision Example: Deep learning Deep learning can automatically learn the features based on the task Input image Deep learning model This is a cat

13. Computer vision fields Image classification Object detection Semantic segmentation Object tracking Face recognition Pose detection Optical character recognition Optical mark recognition Image retrieval Image captioning Generative models etc.

14. Computer vision fields

15. Computer vision fields Semantic segmentation Object tracking

16. Computer vision fields Face recognition Pose estimation

17. Computer vision fields Optical character recognition

18. Computer vision fields Optical mark recognition

19. Computer vision fields Image retrieval

20. Computer vision fields Image captioning

21. Computer vision fields Generative models

22. Required software

23. Anaconda distribution Download from here: https://www.anaconda.com/products/individual

24. Anaconda prompt

25. OpenCV Use the command “ pip install opencv-python ” to install OpenCV using anaconda prompt

26. Jupyter Notebook