module specification

The module aims to prepare students in analysing, designing and developing image processing algorithms routinely used in commercial computer vision systems (e.g. Robots). This module covers fundamental principles, underlying mathematics, algorithmic implementations and practical configurations of computer vision systems. After successful completion of this module, students are expected to professionally evaluate elements of computer vision systems and work with real-world computer vision systems.

MA4005 completed and demonstrable proficiency in programming

Introduction to human, machine and computer vision LO1
Image formation, representation and properties
image file formats
Image pre-processing and mathematical operations
Image segmentation
Shapes, objects and texture for computer vision
Multiple images and stereo vision

Critical reflection on a set of computer vision algorithms, tools, techniques and/or applications LO2

Programming environment or IDE (Integrated Development Environment)  (e.g. Matlab, Java, Python) and associated accessories for computer vision LO3

Coding skills and documentation LO4

Students will develop thorough understanding of fundamental theory and underlying mathematics behind the image processing algorithms of computer vision systems through schedule lectures and guided independent learning aided with recommended reading list.

The laboratory sessions/ supervised workshops, in particular, are provided to support students in gaining practical experience in effective use of professional programming environment or IDE (Integrated Development Environment) and associated accessories in implementing algorithmic solutions for computer vision systems.

Appropriate blended learning approaches and technologies such as the University’s VLE (WebLearn), library resources, CPED (Centre for Professional and Educational Development) and specialised laboratory resources will be used to facilitate and support student learning, in particular, to:

• deliver content;
• encourage active learning;
• impart soft skills
• provide formative and summative assessments with appropriate and timely feedback;
• enhance student engagement and learning experience.

Students are encouraged to keep reflective commentaries on their weekly learning activities and tasks carried out during scheduled learning and teaching as well as during guided independent learning.

Students are expected and encouraged to work individually as well as in groups to complete their laboratory exercises and group essay.
Students have opportunity of i) quick consultation with the module leader during his/her office hours; ii) booking one to one session with academic mentor(s)

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

LO1: Demonstrate thorough understanding of fundamental theory and underlying mathematics behind the image processing algorithms of computer vision systems through formal examination;
LO2: Work in a team and critically reflect and analyse a set of selected algorithms, tools, techniques and/or applications that govern computer vision system by means of groups essay;
LO3: Use professional programming environment or IDE (Integrated Development Environment) and associated accessories in implementing algorithmic solutions for computer vision systems;
LO4: Demonstrate commitment to the profession in a capacity similar to an entry-level systems engineering graduate in context to resource optimised coding with clear understanding of risks and hazards, quality documentation and ethical computer practices

The module is assessed by:

Final exam (2 hours) assesses fundamental theory and underlying mathematics behind image processing algorithms used in computer vision systems. Students are expected to read the recommended core and other texts for this assessment [LO1].

Laboratory Report assesses individual student’s ability to use professional programming environment or IDE (Integrated Development Environment) and associated accessories in implementing algorithmic solutions for computer vision systems. Students are expected to keep logbook (in soft or hard form) and record their design, flow charts, pseudo-codes, actual codes/scripts, and analysis of results for the laboratory exercises throughout the semester. Students are encouraged to use open sources tools (e.g. GitHub, Repo, OpenProject, Zotero) widely used by software professionals for coding, project management and documentation [ LO3, LO4].

Research Essay assesses students’ ability to critically reflect and analyse a set of selected algorithms, techniques, tools and/or applications that govern computer vision system. Each student has an opportunity to take a personalised and exploratory learning and develop research skills within the scope of the module syllabus in consultation with the module leader [LO2 and LO4].

Students have opportunity for one to one interaction and feedback on their practical work during scheduled laboratory sessions. Consistent with University policy, formative and summative feedback will be provided at appropriate points during the semester.

Core Text:

Reinhard Klette (2014), Concise Computer Vision: An Introduction into Theory and
Algorithms, Springer, ISBN-10: 1447163192

E. R. Davies (2017), Computer Vision: Principles, Algorithms, Applications, Learning, 
Academic Press, ISBN-10: 012809284X

Scott E Umbaugh (2018), Digital Image Processing and Analysis: Applications with
MATLAB and CVIPtools ( 3 ed), CRC Press, ISBN-13: 978-1498766029

Stan Birchfield (2017), Image Processing and Analysis, CL Engineering, ISBN-13: 978-
1285179520

Other Texts:

David Forsyth and Jean Ponce (2012), Computer Vision: A Modern Approach, Pearson
Education, ISBN-10: 0273764144

Carsten Steger, Markus Ulrich and Christian Wiedemann (2018), Machine Vision
Algorithms and Applications, Wiley, ISBN-10: 3527413650

Wesley E. Snyder, Hairong Qi (2017), Fundamentals of computer vision, Cambridge
University Press, ISBN: 9781316882641 - http://catalogue.londonmet.ac.uk/record=b1890694~S1

Oge Marques (2011), Practical image and video processing using MATLAB, Wiley-
IEEE Press, ISBN: 9781118093467 –
http://catalogue.londonmet.ac.uk/record=b1672029~S1

Alasdair McAndrew (2016), A computational introduction to digital image processing,
CRC Press, ISBN: 1482247321 –
http://catalogue.londonmet.ac.uk/record=b1757053~S1

Chris Solomon, Toby Breckon (2011), Fundamentals of digital image processing : a
practical approach with examples in Matlab,  Wiley-Blackwell, ISBN: 9780470689783 
http://catalogue.londonmet.ac.uk/record=b1681274~S1

Milan Sonka et al (2014), Image processing, analysis, and machine vision, Cengage Learning, ISBN: 1133593690 - http://catalogue.londonmet.ac.uk/record=b1677110~S1

Rafael C. Gonzalez and Richard E. Woods (2017), Digital Image Processing (4 ed or
later), Pearson, ISBN-10: 1292223049

R C Gonzalez et al (2010), Digital Image Processing Using MATLAB (2nd ed), Mcgraw Hill, ISBN-10: 9780070702622 –
http://catalogue.londonmet.ac.uk/record=b1556624~S1

Brian H. Hahn, Daniel T. Valentine (2016), Essential MATLAB for engineers and
Scientists, Academic Press, ISBN-10: 9780081008775 –
http://catalogue.londonmet.ac.uk/record=b1603014~S1

Tim Morris (2003), Computer vision and image processing, Palgrave Macmillan, ISBN: 0333994515 - http://catalogue.londonmet.ac.uk/record=b1434025~S1

Richard J. Radke (2012), Computer Vision for Visual Effects, Cambridge University Press, ISBN: 9781139019682 - http://catalogue.londonmet.ac.uk/record=b1762839~S1

Sandipan Dey (2018), Hands-On Image Processing with Python, Packt Publishing, ISBN-10: 1789343739

Ashwin Pajankar (2017), Raspberry Pi Image Processing Programming: Develop Real-Life Examples with Python, Pillow, and SciPy, Apress, ISBN-10: 1484227301

Abhinav Dadhich (2018), Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV, Packt Publishing, ISBN-10: 1788297687 –

Jason M. Kinser (2018), Image Operators: Image Processing in Python, CRC Press,
ISBN-10: 1498796184

Journals:
IET image processing - http://catalogue.londonmet.ac.uk/record=b1940052~S2
IET computer vision - http://catalogue.londonmet.ac.uk/record=b1934049~S2
IEEE transactions on image processing - http://catalogue.londonmet.ac.uk/record=b1927586~S2
ELSEVIER Computer vision and image understanding Journal - http://catalogue.londonmet.ac.uk/record=b1920438~S2

Websites: University Library website- https://student.londonmet.ac.uk/library/

Electronic Databases: 
IEEE Xplore / IET Digital Library (IEL) - https://ieeexplore.ieee.org/Xplore/home.jsp
ACDM Digital Library - https://0-dl-acm-org.emu.londonmet.ac.uk/dl.cfm
Wiley Online Library - https://0-www-onlinelibrary-wiley-com.emu.londonmet.ac.uk/

Other: www.Lynda.com