Coursework

2 - Project output (other than dissertation) - 70

Questionnaires

1 - Set exercises - 30

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<ul><li>The human visual system, and the pinhole camera model </li><li>Digital images, colour, and image pre-processing </li><li>Edge detection </li><li>Image segmentation </li><li>Feature extraction and description </li><li>Image matching through interest point detection </li><li>Image classification using machine learning </li><li>Object detection &amp; instance segmentation </li><li>Computer vision with deep learning approaches </li></ul>
In the coursework component of the module, you will learn how to manage image data with computationally efficient algorithms, and select different algorithmic components to design and assess a practical computer vision system embodying the taught principles. You will present your results with a written report and a short video demonstration.

Feedback is provided in a number of ways throughout your module. This includes feedback you receive during teaching from both peers and lecturers and that provided for formative assessment. Following an assessment, you will be given your marks and feedback in line with the assessment regulations and policy. More information on the timing and type of feedback that will be provided for each assessment will be available from the module leader. <a href='https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations'>https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations</a>

This module will support the development of all the graduate attributes. Your module leader will provide you with information about how your learning, teaching and assessment activities support your development of these attributes. The graduate attributes are: - Always learning - Engaged in the world - Technical and digital - Connected Professional - Creating Impact

Self-directed - 

In-Person - 

Deep learning with PyTorch: a practical approach to building neural network models using PyTorch

Programming computer vision with Python 

A practical introduction to computer vision with OpenCV

Computer vision: algorithms and applications

Deep learning

Pattern recognition and machine learning

IN3060 - Computer Vision

IN3060-PRD2-PER-ALV1

There will be 10 weekly sessions with each session material available on the Moodle learning environment. Each session will consist of a two hour lecture and a one hour tutorial.  The lecture will introduce new concepts, describing them both in theory and application. Materials for each session will be provided via the Moodle learning environment.   
In tutorials, you will program computer vision algorithms that solve image analysis problems. You will gain generalised, practical skills which allow you to work with industry-standard software and toolkits, in particular, Python, scikit-image, scikit-learn and OpenCV.  Laboratory exercises will reinforce concepts introduced in lecture and give you an opportunity to practice computer vision in solving real-world image analysis problems.  
In the independent study time, you are encouraged to consolidate your knowledge from the lectures and tutorials by reading around the subject and spending time working through sample exercises and questions. In addition, you will be preparing and undertaking your coursework assignments.

Computer Vision

Manage a software project that implements a computer vision technique in a practical working demonstration.   

Skills

Be motivated to implement image analysis algorithms in software.   

Values and attitudes

Implement image analysis algorithms using sound theoretical principles.  

Apply different approaches to image analysis.   

Knowledge

Adopt a constructive approach to evaluating computer vision algorithms.  

Evaluate the key components of a computer vision system.  

Evaluate feature extraction techniques, and their use in applications from segmentation to object detection.  

Implement software that solves practical computer vision problems.  