ACS61012 Machine Vision
Module Description (subject to change)
Machine vision enables systems to be fully autonomous and has many applications including in robotics, security, manufacturing, transportation and medical systems. Machine vision aims to develop computer based systems that are able to perform the same functions as the human eyes. In this module students will be able to learn the main machine vision principles which are currently being used in real-world systems. For instance, students will learn models and methods for detection, tracking and recognition of objects of interest and apply these models and methods to solving real problems. The area is very multidisciplinary and requires knowledge from several fields, including image and video processing, artificial intelligence, neurobiology and inference methods. Students will learn how to analyse and evaluate the performance of the studied approaches.
Credits: 15 (Spring Semester)
If you have any questions about the module please talk to me during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask.
Outside of lectures please contact me via email, or drop in to see me.
At the end of the module, the student should be able to:
1. Demonstrate knowledge of the principles of for machine vision systems, understand their advantages and limitations compared with the state-of-the-art solutions [EA1i, EA1p];
This module satisfies the AHEP3 (Accreditation of Higher Education Programmes, Third Edition) Learning Outcomes that are listed in brackets after each learning outcome above. For further details on AHEP3 Learning Outcomes, see the downloads section of our accreditation webpage.
Learning and Teaching Methods
NOTE: This summary of teaching methods is representative of a normal Semester. Owing to the ongoing disruption from Covid-19, the exact method of delivery will be different in 2020/21.
The module will be taught through a combination of lectures, tutorial classes and laboratory sessions. The lectures will be used to teach the main research and industrial motivations, theoretical background and technical concepts of topics of machine vision systems (learning outcomes 1-3, 5). Tutorial and laboratory classes will be used to develop the students’ confidence and skills in using appropriate computational and experimental tools for solving problems of machine vision with applications to autonomous systems. This includes understanding the limitations of such solutions (learning outcomes 3, 4, 6). Students will undertake two open ended design tasks to provide solutions to machine vision problems from industry and/or research (learning outcomes 3-6).
Learning and Teaching Materials
All teaching materials will be available via Blackboard (MOLE).
Coursework (4000 words) 100%
No resit examination is available for this module.
The feedback will be formative (given during lectures and practical sessions) and summative (formal assessment of the assignment and exam). The formative feedback will be written and will address every task from the assignment
Students are encouraged to provide feedback during the module direct to the lecturer. Students will also have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of each semester.
You can view the latest Department response to the survey feedback here.