The University of Southampton
Courses

COMP6206 Advanced Computer Vision

Module Overview

To capitalise on image processing and computer vision skills presented in part 3, to ready students for practical implementation in commerce or in research. Computer Science, Electrical and Electronic Engineering students are all welcome on this course (if you hear a prerequisite of Signal Processing is stated, this is not true). This course has always been hugely enjoyed by the students (and the staff!) as it is a mix of theory and implementation and capitalises on presentation and group project work.

Aims and Objectives

Module Aims

To impart advanced computer vision skills, to ready students for practical implementation in commerce or in research

Learning Outcomes

Knowledge and Understanding

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

  • Appreciate the stock of technique available for computer vision
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • To practice (and perfect!) presentation techniques and group coursework
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Build working computer vision systems
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Learn the principles of developing and applying computer vision

Syllabus

Feature Extraction - Further techniques in parametric and non-parametric feature extraction including advance Hough transform techniques and active contour models. Feature Description - How to describe extracted features for purposes of further analysis and in feature recognition. Image Interpretation - Syntactic and symbolic image interpretation and analysis. Image Restoration - Beyond the Weiner filter. Least mean squares and extensions and maximum entropy restoration. 3D Imaging - Calibration, epipolar constraint, coordinate systems. Active and passive ranging systems. Morphology - Binary image processing and image geometry.

Learning and Teaching

TypeHours
Follow-up work10.5
Completion of assessment task74
Lecture21
Wider reading or practice34
Preparation for scheduled sessions10.5
Total study time150

Resources & Reading list

Simon Prince (2011). Computer Vision: Models, Learning, and Inference. 

Nixon, M S and Aguado, A S (2012). Feature Extraction and Image Processing in Computer Vision. 

Baggio et al (2012). Mastering OpenCV with Practical Computer Vision Project. 

Roy Davies (2012). Computer anmd Machine Vision: Theory, Algorithms, Practicalities. 

Gary Bradski and Adrian Kaehler (2008). Learning OpenCV: Computer Vision with the OpenCV Library. 

Assessment

Summative

MethodPercentage contribution
Coursework 40%
Coursework 60%

Referral

MethodPercentage contribution
Examination 100%

Repeat Information

Repeat type: Internal & External

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