ELEN0016 Computer Vision

Information

• All information about the course, including the slides, can be found on the University website.

• Note that specific slides will be added for certain lectures, please check the class schedule for updated slides.

Class schedule

• Session 1 (17/09): General Information. 

  • • • • Part 1. Introduction, color spaces, acquisition.  Following the course slides

        • Part 2. Filtering + hands-on with a Jupyter notebook. Download resources

• Session 2 (24/09): Camera models, edge extraction, and Hough transform.

  • • • • Part 1. Camera models. Download slides

        • Part 2. Edge extraction and Hough transform. Following the course slides

• Session 3 (01/10): Camera calibration, object description and analysis, and feature detection and tracking

  • • • • Part 1. Camera calibration + hand-on with a Jupyter notebook. Download slides and download resources

        • Part 2. Object description and analysis + Feature detection and tracking. Following the course slides

• Session 4 (08/10): Mathematical morphology, grayscale morphology, and non-linear filtering.

  • • • • Part 1. Following the course slides. Download slides. 

        • Part 2. Hands-on. Download resources.

• Session 5 (15/10): 3D reconstruction (part 1), segmentation, and motion detection.

  • • • • Part 1. 3D reconstruction (slides) and segmentation. Following the course slides

        • Part 2. Motion detection. + TP motion detection. Following the course slides +  exercises

• Session 6 (22/10): 3D reconstruction (part 2), introduction to machine learning for computer vision.

  • • • • Part 1. 3D reconstruction +hands-on with a Jupyter notebook. Download slides and download resources 

        • Part 2. Introduction to machine learning for computer vision. Download slides 

• Session 7 (05/11): Deep learning for computer vision

  • • • • Part 1. Computer vision and basics of artificial intelligence. Download slides

        • Part 2. Deep learning building blocks and architectures. Download slides

• Session 8 (12/11): Performance analysis, TP on fundamentals of deep learning for computer vision

  • • • • Part 1. Performance analysis. Download slides

        • Part 2. TP on fundamentals of deep learning for computer vision. Download resources

• Session 9 (19/11): Human pose estimation, project Q&A

  • • • • Part 1. Human pose estimation Download slides

        • Part 2. Project Q&A