2023/24 | Engineering, Mathematics and Physical Sciences Intranet

MODULE TITLE	Machine Vision	CREDIT VALUE	15
MODULE CODE	ECMM441	MODULE CONVENER	Dr Anjan Dutta (Coordinator)

DURATION: TERM	1	2	3
DURATION: WEEKS

Number of Students Taking Module (anticipated)	90

DESCRIPTION - summary of the module content

***DATA SCIENCE AND DATA SCIENCE WITH BUSINESS STUDENTS ONLY***

How do we recognise objects and people? How can we catch a ball? How do we navigate our way from our desk to the coffee machine, without bumping into each other? These seemingly simple tasks have represented a challenge for AI scientists for decades. Recent developments in machine vision have seen significant improvement in important applications (face detection cameras, body tracking, and autonomous cars).

Pre-requisites: ECMM456 Fundamentals of Data Science (Professional), ECMM457 Learning from Data.

Co-requisites: None.

AIMS - intentions of the module

This module will provide you with the fundamentals of machine vision and image processing, covering the essential challenges and key algorithms for solving a variety of problems related to automated processing of visual data. The course will provide both theoretical grounding and a practical introduction to classical and state-of-the-art approaches. Theory and methods will be taught through practical applications of machine vision and will cover a broad range of problems, from low-level image processing to object recognition, motion tracking and 3D vision.

Content will be delivered in an intensive one-week teaching block consisting of lectures and practical work. Self-study and coursework will complete the module teaching activities.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Explain key computer vision and image processing problems and their mathematical formulation.
2. Design and implement vision and image processing algorithms in a high-level language.

Discipline Specific Skills and Knowledge

3. Analyse and propose solutions for computer vision and image processing problems.
4. Select appropriate statistical representations, features and algorithms to suit problem specificities.

Personal and Key Transferable / Employment Skills and Knowledge

5. Understand and appreciate the limitations of different methods.
6. Effectively communicate to a technical audience using reports and documentation.

7. Critically read and report on research papers.

SYLLABUS PLAN - summary of the structure and academic content of the module

Topics will include:

Image processing: convolution, linear filters, image gradients
Feature extraction & matching: edge & corner detection, multi-scale analysis, feature descriptors, feature matching and tracking
Geometric Image formation: geometric transformations, pinhole camera and perspective effects
Multi-view geometry and structure from motion: 3D reconstruction
Recognition in Computer Vision: classical approaches for image classification, and object detection, k-nearest neighbours and support vector machine classifiers.
Deep learning for computer vision: neural networks, convolutional neural networks, object detection, semantic and instance segmentation

.

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	32.00	Guided Independent Study	40.00	Placement / Study Abroad	0.00

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled Learning and Teaching	16	Lectures
Scheduled Learning and Teaching	16	Practicals
Guided independent study	20	Coursework preparation
Guided independent study	20	Background reading and self study

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Practical work	16 hours	All	Oral

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Coursework	100	20 hours, code submission	All	Written feedback and model answers

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment	Form of Re-assessment	ILOs Re-assessed	Time Scale for Re-assessment
Summative assessment	Coursework (100%)	All	August re-assessment period

RE-ASSESSMENT NOTES

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e., a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

CVOnline: an online compendium of computer vision techniques: http://homepages.inf.ed.ac.uk/rbf/CVonline/

Website of R. Szelinski’s Computer Vision book (including a free electronic version of the book): http://szeliski.org/Book

Getting Started with MATLAB: http://uk.mathworks.com/help/matlab/getting-started-with-matlab.html

Computer Vision System Toolbox of MATLAB: https://uk.mathworks.com/products/computer-vision.html

Keras: The Python Deep Learning library: https://keras.io/

Other Resources:

Reading list for this module:

Type	Author	Title	Edition	Publisher	Year	Search
Set	Bishop, John C.	Pattern recognition and machine learning		Springer	2006	[Library]
Set	Forsyth, David & Jean Ponce	Computer vision: a modern approach	2nd	Pearson	2011	[Library]
Set	Jan Erik Solem	Programming Computer Vision with Python: Tools and algorithms for analyzing images		O'Reiley	2012	[Library]
Set	Szeliski, Richard	Computer vision: algorithms and applications	2nd	Springer	2021	[Library]

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	ECMM456, ECMM457
CO-REQUISITE MODULES

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Thursday 06 July 2017	LAST REVISION DATE	Wednesday 18 January 2023

KEY WORDS SEARCH	Computer vision, image processing, object recognition, object detection and segmentation, tracking, pattern recognition.

Computer Science

ECMM441 - Machine Vision (2023)