Unit name | Machine Vision (UWE, UFMFRR-15-M) |
---|---|
Unit code | EMATM0056 |
Credit points | 15 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Zhang |
Open unit status | Not open |
Pre-requisites |
None |
Co-requisites |
None |
School/department | School of Engineering Mathematics and Technology |
Faculty | Faculty of Engineering |
The definition and scope of what is meant by the term ‘machine vision’ is changing rapidly as, via increasing capabilities often enabled through innovation in machine learning, new and exciting contributions are being made in applications across a wide variety of disciplines - such as robot navigation, human-robot interaction, healthcare technologies and in precision agriculture. Given the ubiquity of camera equipped smartphones and the wide availability and variety of alternative imaging devices (e.g. thermal and RGB-D cameras), one should not be surprised to notice that machine vision technology is increasingly becoming a part of everyday life. Just as how a visual sense is important to human beings, it is arguably just as important to new forms of AI enabled systems. Therefore, the ability to “observe” the world with visual sensors, to “describe” the world from pictures or sequences of pictures, and to use this information to make useful decisions, is core to machine vision applications today.
This module provides an introduction to machine vision including the fundamentals of image formation and image processing as well as state-of-the-art feature extraction and image-based machine learning techniques. The course content is research-informed and practice-led, and as such, aims to provide students with the key skills that meet the needs of industry. The core syllabus is outlined below (note this is by no means an exhaustive list), where all elements are, where possible, supported using example case study materials drawn from current research and practical application.
AIMS
Basic concepts:
Image formation and representation
Basic image processing techniques
Feature extraction
3D imaging
Machine learning (deep learning) in machine vision
Generating machine vision code
Refer to UWE unit level guidance.
Refer to UWE unit level guidance.
Refer to UWE unit level guidance.
https://rl.talis.com/3/uwe/lists/A1B547D8-85D2-BFCD-9029-443E63D59E22.html