COMP3204 Computer Vision

Module Overview

The challenge of computer vision is to develop a computer based system with the capabilities of the human eye-brain system. It is therefore primarily concerned with the problem of capturing and making sense of digital images. The field draws heavily on many subjects including digital image processing, artificial intelligence, computer graphics and psychology. This course will explore some of the basic principles and techniques from these areas which are currently being used in real-world computer vision systems and the research and development of new systems.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Human and computer vision systems
Current approaches to image formation and image modelling
Current approaches to basic image processing and computer vision

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Analyse and design a range of algorithms for image processing and computer vision
Develop and evaluate solutions to problems in computer vision

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

Implement basic image processing algorithms

Syllabus

- The human eye-brain system as a model for computer vision - Image formation: sampling theorem, Fourier transform and Fourier analysis - Image models - Basic image processing: Sampling and quantisation, Brightness and colour, Histogram operations, Filters and convolution, Frequency domain processing - Edge detection - Boundary and line extraction - Building machines that see: constraints, robustness, invariance and repeatability - Fundamentals of machine-learning: classification and clustering - Understanding covariance, eigendecomposition and PCA - Feature extraction - Interest point detection - Segmentation - 2-D Shape representation - Local features - Image matching - Large-scale image search and feature indexing - Understanding image data and performing classification and recognition - 3D vision systems - Recovering depth from multiple views - Practical examples, including: biometric systems (recognising people), industrial computer vision, etc.

Learning and Teaching

Type	Hours
Completion of assessment task	25
Lecture	24
Preparation for scheduled sessions	12
Revision	10
Follow-up work	12
Wider reading or practice	55
Tutorial	12
Total study time	150

Resources & Reading list

Gonzalez et al (2008). Digital Image Processing.

Nixon, M.S. and Aguado, A.S. (2012). Feature Extraction & Image Processing.

Stockman and Shapiro (2001). Computer Vision.

Sonka, Hlavac & Boyle (2008). Image Processing, Analysis and Machine Vision.

Assessment

Summative

Method	Percentage contribution
Continuous Assessment	40%
Final Assessment	60%

Repeat

Method	Percentage contribution
Set Task	100%

Referral

Method	Percentage contribution
Set Task	100%

Repeat Information

Repeat type: Internal & External