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Machine Vision and Human Machine Interaction - ELE00142M

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  • Department: Electronic Engineering
  • Module co-ordinator: Dr. Jihong Zhu
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2024-25
    • See module specification for other years: 2023-24

Module summary

This module will introduce students to the fundamentals of machine vision and human-machine interfaces (HMI). The content focuses on an understanding and application of the most common machine vision algorithms to practical perception, classification, and identification problems, and also how they can be used to facilitate effective interaction between humans and machines. Common modes of human-machine interaction and the challenges and opportunities in this field are also explored. The advantages and disadvantages of these algorithms and modes of interaction are compared and critically evaluated in the context of modern robots and autonomous systems. Students will have an opportunity to implement, test, and combine these algorithms in the laboratory to see for themselves how these systems operate and perform in real-world conditions, and also to create a software system for perception and HMI for their module assessment.

Module will run

Occurrence Teaching period
A Semester 1 2024-25

Module aims

Subject content aims:

  • To introduce the fields of machine vision and human-machine interaction, and how they work together to help humans and machines achieve synergy

  • To describe the main types and uses of machine vision algorithms for allowing machines to perceive objects and humans around them

  • To introduce different types of Human-Machine Interaction (HMI) systems and principles in HMI systems design, and appraise fundamental HMI theory

  • To explore the strengths and weaknesses of visual and other modes of human-machine interaction in the context of practical applications

  • To enable students to experiment with machine vision algorithms and human-machine interaction techniques to solve basic challenges

  • To explore the ethics and safety requirements of machines with vision and human-machine interfaces

Graduate skills aims:

  • To explain the applications of machine vision and how techniques and algorithms are used in robotics and autonomous systems

  • To illustrate the challenges and solutions used to facilitate effective and safe human-machine interaction

Module learning outcomes

Subject content learning outcomes:

After successful completion of this module, students will:

  • Describe the fundamental principles of machine vision systems

  • Explain the principles of effective human-machine interaction

  • Have working knowledge of the most common machine vision algorithms and how to use them for simple perception tasks

  • Have working knowledge of the most common human-machine interface modes and implementations and how to use them to facilitate human-machine synergies

  • Describe how machine vision and perception can be leveraged for the purpose of human-machine interfacing

  • Discuss issues involved in robot interaction and perception as well as human-robot issues including ethics

Graduate skills learning outcomes:

After successful completion of this module, students will be able to:

  • Design and Implement machine vision systems for specific perception tasks and perception of humans

  • Design and implement basic human-machine interaction systems

  • Design systems that are aware, safe, and capable of interaction with the world around them


Task Length % of module mark
N/A 60
Laboratory 1 : Completion of lab tasks
N/A 10
Laboratory 2 : Completion of lab tasks
N/A 10
Laboratory 3 : Completion of lab tasks
N/A 10
Laboratory 4 : Completion of lab tasks
N/A 10

Special assessment rules


Additional assessment information

Students are tasked with implementing an algorithm that applies machine vision and HMI principles to solve a challenging problem that involves perception of and reaction to the outside world. They must produce and critically analyse experimental results from implementation and testing of this algorithm, and present what they have produced and found in a formal paper of suggested 6-10 pages length


Task Length % of module mark
N/A 60

Module feedback

Formative Feedback:

Lab work with spoken feedback and problem-solving, and immediate help given by lab demonstrators during lab sessions.

Workshops held every week that allow students to ask questions and get immediate feedback on their progress in lecture study and coursework.

Summative Feedback:

Feedback forms with a detailed breakdown of grades provided at the assessment of coursework which occurs at the end of term, returned to the students within standard university guidelines with grades

Indicative reading

Richard Szeliski. “Computer vision: algorithms and applications”. Springer Science & Business Media, 2010.

E. R. Davies. “Computer Vision: Principles, Algorithms, Applications, Learning” 5th edition. Academic Press, 2017

Richard Hartley and Andrew Zisserman. “Multiple View Geometry in Computer Vision”, Second Edition. Cambridge University Press, March 2004.

Alan Dix, Janet Finlay, Gregory D. Abowd, and Russell Beale. “Human-Computer Interaction”. Pearson Education, 2004.

Christoph Bartneck, Tony Belpaeme, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Šabanovic. “Human-robot interaction: An introduction”. Cambridge University Press, 2020

The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.