Run by School of Computer Science and Electronic Engineering

20.000 Credits or 10.000 ECTS Credits

Semester 1

This module aims to describe the concepts of image processing and computer vision. It will also enable students to process images and implement simple digital image processing and computer vision systems.

Indicative content includes:

• Image formation, image representation, fundamentals of human luminance and color vision, segmentation, re-sampling. Solving problems using a modern library such as OpenCV,
• Point operations, convolution, linear filters, morphological operators, image histograms, and histogram equalization, 2D image transformations, image pyramids.
• Edge detectors, Hough transform, segmentation, feature detectors, feature descriptors, feature matching.
• Digital camera, display devices. colour calibration, gamma correction, limitations of human visual perception.
• More advanced computer vision topics, e.g. person tracking, trajectory, surveillance, security, controlling processes (e.g., robots), navigation, comp-human interaction (e.g., gestures), automatic inspection. Ethical considerations incl. data collection/management, informed consent, privacy, surveillance.

excellent

Equivalent to the range 70%+. Assemble critically evaluated, relevent areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

good

Equivalent to the range 60%-69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

threshold

Equivalent to 40%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

1. Effectively use image representations to solve computer vision problems

2. Use computer vision techniques to implement feature detection and tracking.

3. Construct software to process image data, both in a general sense and for Computer Vision.

4. Apply image processing filters and operators to achieve given goals of an imaging system.

5. Make informed decisions on the selection of imaging and display technologies for a task at hand.

6. Give examples of computer vision applications, associate them with CV algorithms.

Type	Name	Description	Weight
	Image Processing Application		25.00
	Computer Vision Application		25.00
	Final exam		50.00

		Hours
Lecture	2 hours per tutorial/lecture per week	24
Laboratory	1x2 hours a week	24
Private study	72 hours of laboratory preparation and reports 80 hours of self-study (inc. revision after the lectures; preparation for the assessed and non-assessed labs and the exam).	152

• Numeracy - Proficiency in using numbers at appropriate levels of accuracy
• Computer Literacy - Proficiency in using a varied range of computer software
• Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
• Exploring - Able to investigate, research and consider alternatives
• Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.

• Identify emerging technologies and technology trends;
• Solve problems logically and systematically;
• Analyse and display data using appropriate methods and mathematical techniques;
• Demonstrate familiarity with relevant subject specific and general computer software packages.
• Problem solving strategies
• Deploy theory in design, implementation and evaluation of systems
• Knowledge and/or understanding of appropriate scientific and engineering principles
• Knowledge and understanding of mathematical principles
• Knowledge and understanding of computational modelling

Optional in courses:

• G400: BSC Computer Science year 3 (BSC/CS)
• G40B: BSc Computer Science (4 year with Incorporated Foundation) year 3 (BSC/CS1)
• G40F: BSc Computer Science year 3 (BSC/CSF)
• H117: MComp Computer Science year 3 (MCOMP/CS)