BREADTH Scholars Curriculum

AI Monitoring Through Computer Vision | Brown University, School of Engineering

This repository hosts a collection of code for hands-on tutorials of Brown SoE BREADTH program curriculum on AI monitoring through computer vision. Topics include: image processing basics, image features and feature correspondences, object tracking, action detection and recognition, etc.

Materials

We provide all the source code introduced in the lecture in conjunction with the lecture slides. Both python and matlab versions of the code are supported. You can access the python code through the following colab badges:

Image Processing Basics

• Image Pointwise Processing:
  
• Image Noise Removal by Convolution:

Image Features and Feature Correspondences

• Feature Detection:
  
• Feature Matching:

Background Subtraction and Fall Detection Through Gait Parameters

• Background Subtraction:
  
• Fall Detection Through Gait Parameters:

Lab Assignments

• Image processing basics:
  

TODO

• Add MATLAB code for image feature detection and feature correspondence construction
• Add MATLAB code for background subtraction and fall detection

Instructors

Prof. Benjamin Kimia (benjamin_kimia@brown.edu)
Chiang-Heng Chien (chiang-heng_chien@brown.edu)