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)

Module Leader

MLyudmila

Dr Lyudmila Mihaylova
Email: L.S.Mihaylova@sheffield.ac.uk
Amy Johnson Building

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.

Learning Outcomes

Learning Outcomes

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];
2. Describe and explain the main components, architectures and design issues in  developing machine vision systems and future technological challenges and opportunities [EA2m, D3p];
3. Explain and apply the various methodologies and be able to compare them with conventional machine vision tools in a wide range of industrial and research applications [EA3m, EA2fl];  
4. Select and use appropriate architectures, algorithmic, computational and experimental tools (including those from the research literature) to provide innovative solutions to complex, open-ended machine vision systems subject to a variety of technological constraints [EA3m, EA3i];
5. Demonstrate creative and critical thinking in providing and evaluating solutions to machine vision methods and effectively communicate and analyse such solutions [EA4p, D3m];
6. Effectively present appropriate design methodologies, analyses and critical evaluation of solutions and any limitations and constraints of such solutions in the form of a technical report to a standard that a suitably qualified person could follow and use to obtain similar findings. [D3p, D3i];

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.

Syllabus

Syllabus 

Teaching Methods

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).

Teaching Materials

Learning and Teaching Materials

All teaching materials will be available via Blackboard (MOLE).

Assessment

Assessment

Coursework (4000 words) 100%

No resit examination is available for this module.

Feedback

Feedback

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

Student Evaluation

Student Evaluation

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.

Recommended Reading

Recommended Reading