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Computer vision – 3D perspective (online)

This online short course introduces both traditional and modern approaches to Computer Vision (CV), with a strong focus on 3D aspects of CV – from camera anatomy and calibration, depth estimation and structure from motion (SfM) through to powerful modern-day Deep Learning approaches to 3D Computer Vision.

About this course

Computer Vision is a key application of Artificial Intelligence and Machine Learning. CV concerns techniques to help computers “see” and “understand” the content of still images (photos) and sequences of images (videos).

In the modern context, visual information is often combined with other media, including text, sound or even brain signals.

This course covers fundamental knowledge and practical skills in 3D computer vision – OpenCV and TensorFlow 2.0 – and will guide participants to use some leading computer vision tools to build CV models.

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Course structure

This course is held across two online sessions, covering the following topics:

Session 1: Classical 3D Computer Vision

  • Computer Vision basics and terminology
  • 3D camera anatomy and calibration techniques
  • Introduction to OpenCV programming
  • Classical 3D CV examples using OpenCV


Session 2: Deep Learning 3D CV

  • Introduction to Deep Learning
  • State-of-the-art Deep Learning based 3D CV research
  • Introduction to TensorFlow 2.0
  • An illustrative example of Deep Learning 3D CV using TensorFlow


Learning outcomes

By the end of the course you will be able to:

  • Explain the key concepts and terminology of 3D computer vision to a friend, colleague or peer
  • Identify typical 3D tasks pursued in computer vision, and some of the key challenges in 3D CV
  • Explain the role Deep Learning plays in 3D CV
  • Use OpenCV and TensorFlow 2.0 to build a CV model

Who is this course for?

This course is intended for professionals and academics with some background in computer science or programming who are interested in entering the field of Computer Vision.

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