Details, supporting materials and links for courses I have taught that are directly related to my primary area of research activity and expertise. Whilst at Durham University, I have additionally taught topics across software engineering, computer networking and logic programming within computer science.
The low-level and mid-level visual understanding achievable using various digital image processing techniques allow us to tackle the classical Artificial Intelligence problem of artificial visual sensing – computer vision (also termed 'robot vision'). By developing these techniques further we can apply image processing to a number of different visual inspection and understanding tasks within the realm of science and engineering. In this course we investigate the rapidly developing area of computer vision – the automated interpretation and understanding of visual information.
Currently, at Durham our courses in this area cover the following topics:
Earlier versions of this courses covered the following additional topics:
This course is now taught at Durham University as part of Level 3 (third year) Computer Science with teaching resources available via the On-line Learning Platform.
Machine learning - "A set of methods for the automated analysis of structure in data. There two main strands of work, (i) unsupervised learning or descriptive modeling, where the goal is to find interesting patterns or structure in the data, and (ii) supervised learning or predictive modeling, where the goal is to predict the value of one or more variables given some others. These goals are similar to those of data mining , but in machine learning the focus is more on autonomous machine performance, rather than enabling humans to learn from the data."
[ Fisher, Breckon et al., 2014 ]
Most recently, at Durham this course has covered the following topics:
Earlier versions of this courses covered the following additional topics:
This course was taught at Durham University as part of Level 3 (third year) and Level 4 (forth year) Contemporary Computer Science module.
Summary resources, derived from those used on this course, covering most contemporary topics are available from the machine learning and deep learning tutorials listed on this page.
The most powerful method of sensing available to humans is vision. In computing, visual information is represented as a digital image. In order to process visual information in computer systems we need to know about processing digital images. This course focuses upon the task of low-level visual processing spanning the following topics:
A version of this course is now taught at Durham University as part of Level 2 (second year) Computer Science with teaching resources available via the On-line Learning Platform.
Digital image processing allows us to process visual information in computer systems. By processing visual information we can develop automated visual interpretation and understanding – artificial vision, itself a large part of wider field of the Artificial Intelligence. In order to achieve this we must be able to extract high-level visual information such as edges and regions from images and additionally allow for the efficient storage of large amounts of visual data. In this course we concentrate on mid-level visual interpretation and image compression.
A comprehensive visualisation course spanning all aspects of multi-dimensional data visualisation, with supporting practical exercises in VTK, covering the following topics:
A version of this course is now taught at Durham University as part of Level 4 (final year) Computer Science with teaching resources availablevia the On-line Learning Platform.
Invited tutorials spanning computer vision and machine learning.
a whirlwind tour of key principles
A "whirlwind" overview considering contemporary approaches in deep machine learning for computer vision (via Convolutional Neural Networks, 2018+) with an emphasis on key fundamentals and understanding.
Originally developed for the BMVA Computer Vision Summer School (2016 - 2019)
a whirlwind tour of key concepts for the uninitiated
A brief (and somewhat "whirlwind") overview to the use of Machine Learning techniques in computer vision. Aimed at the uninitiated (with little or no background) whilst still presenting relevant material close to the current state of the art. Concentrates on four specific thematic areas:
Further insight into common machine learning tasks in computer vision, best experimental practice and offer a range of tangible exemplar applications from the research literature. Given the limitations of time a number of topics are overlooked or given a fleeting mention - this notably includes probabilistic approaches which are a tutorial topic in their own right. It is not intended as an all encompassing overview but instead a mere introduction.
This tutorial was originally developed for the BMVA Computer Vision Summer School (updated and presented annually 2010 - 2016).
An extended version, presented at the EPSRC Vision & Language Network Workshop (June, 2015) additionally presents a swift overview of (what are now considered to be) traditional gradient derived features in computer vision (feature points, descriptors and histograms), together with feature similarity measures and feature distributions.
Slides Extra Material - Features Extra Material - ML
An extension (NATO SET-163 / RTG-90 meeting, October 2013) explores the full-circle relationship between the simple decision tree approaches (1980s) and their subsequent re-use (decades later) in state-of-the-art approaches such as Decision Forests and Ferns.
a beginners tutorial for the uninitiated
A Whirlwind Tour of where we are in Computational Binocular Stereo Vision covering all key concepts spanning problem formulation, calibration, sparse feature matching, dense pixel matching, optimization and 3D projection.
Presented in at the Vision Image Interpretation in Humans and Machine workshop on stereo vision (2015) as part of the EPSRC Network for Biological and Computer Vision.
A conference talk in 20 minutes ...
A combined set of exemplar, instructional and do's/don't's advice on presenting a short talk at a scientific conference on a technical subject. Equally applicable to thesis project presentations at both graduate and undergraduate level.