Introductory description

The course will enable computer science undergraduates to apply their mathematical knowledge and understanding of algorithms to problems in image and video processing: from preprocessing, to quantitation, video compression and video interpretation. The methods have numerous applications e.g. in medicine, biology, robotics (computer vision), surveillance, security, biometrics, database searching, TV and entertainment.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

• Introduction to Human visual perception;
• Image sampling and quantization;
• Filtering by convolution and correlation: blurring, sharpening, edge detection;
• Colour models and contrast processing;
• Histograms and basic statistical models of images;
• Information and entropy;
• Discrete Fourier transforms and Discrete Cosine Transforms for filtering, coding, correlation and motion estimation;
• Image Pyramids for analysis and image compression;
• Image and video segmentation and texture models;
• Algorithms for boundary and region segmentation from images and video;
• Optical flow techniques for motion estimation and tracking;
• Pinhole camera models and stereo and 3D reconstruction, including the principles image stitching and augmented reality;
• Feature detection and object recognition in images;
• Neural-networks for image analysis and end-to-end learning: from fully-connected networks to convolutional neural networks;
• Programming image and video analysis methods in Python + associated libraries, OpenCV and Keras.

Learning outcomes

By the end of the module, students should be able to:

• Understand and know how to apply state-of-the-art machine learning techniques (convolutional neural networks) to solving problems in image and video analysis
• Understand and describe the fundamental principles of image and video analysis and have an idea of their application.
• Perform written communication in lab report and examination. Use specialist toolboxes in Python programming.
• Assimilate mathematical and algorithmic material on image processing fundamentals and appropriately apply and program solutions to real problems in image and video analysis.
• Critically analyse multimedia applications.

Indicative reading list

Please see Talis Aspire link for most up to date list.

View reading list on Talis Aspire

Subject specific skills

• Fundamentals of digital image processing, image and video analysis, computer vision including camera calibration, feature matching and object detection and recognition.
• Practical programming of image and video analysis, processing, machine learning for image analysis.

Transferable skills

• Written communications skills
• Formulate and test hypothesis, experimental design, evaluation and critical analysis
• Problem solving