CS3IV: Image Analysis and Visual Intelligence

Module code: CS3IV

Module provider: Computer Science; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: Level 3 (Honours)

When you'll be taught: Semester 2

Module convenor: Professor James Ferryman, email: j.m.ferryman@reading.ac.uk

Module co-convenor: Dr Hong Wei, email: h.wei@reading.ac.uk

Pre-requisite module(s): Before taking this module, you must have knowledge of mathematics, such as matrix and vector manipulations, and skills of programming. (Open)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 21 May 2024

Overview

Module aims and purpose

The module aims to provide students with theoretical and practical knowledge of image analysis and computer vision, an appreciation of human cognitive abilities in visual perception, and example real-world applications of image analysis and computer vision. 

This module also encourages students to develop a set of professional skills, such as problem solving, team working, critical analysis of published literature, creativity, technical report writing for technical and non-technical audiences, self-reflection and effective use of commercial software. Programming skills can be improved from coursework assignments. 

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Demonstrate basic skills for image analysis and human perceptual concepts relating to vision and computer vision;
2. Address issues associated with image analysis including image transformation, histogram analysis and modification, image morphological operations, colour image manipulation, and image textual analysis;
3. Address issues relating to computer vision including pattern classification, knowledge of geometric-based vision and appearance-based vision;
4. Demonstrate skills to develop algorithms for image analysis and computer vision including, for example, image compression and object recognition.

Module content

This module covers the following topics:

• digital image fundamentals;
• image enhancement in the spatial domain;
• image enhancement in the frequency domain;
• colour image processing;
• mathematical morphology in image processing;
• image compression;
• symbolic feature extraction;
• image segmentation;
• introduction to natural vision (human perception);
• theory of image-based pattern classification;
• geometric-based vision;
• appearance-based vision;
• object recognition;
• applications of computer vision.

Structure

Teaching and learning methods

 The module is delivered via lectures, tutorials and lab sessions throughout the second semester. 

Study hours

At least 48 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

A set of exercises and questions is set each week to enable students to self-reflect on the defined learning outcomes.  

Reassessment

Additional costs