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ELEC97112 (EE4-93) Computer Vision and Pattern Recognition

Lecturer(s): Prof Krystian Mikolajczyk; Dr Carlo Ciliberto; Dr Adam Spiers


Upon successful completion of the programme a typical student will be able to:
- Apply fundamental concepts and theoretical principles of computer vision and pattern recognition for building signal and data representations and modelling target functions
- Develop insight into the problems involved in applying a variety of computer vision and pattern recognition techniques to deal with practical scenarios
- Understand and apply the concepts of visual geometry in selected computer vision applications
- Analyse and compare the strengths and weaknesses of popular approaches
- Implement various algorithms in a range of CVPR applications through specific programming environments (Matlab, python)

Learning Outcomes

"The module is based on lectures and coursework.
Tutorials and Q&A sessions with demonstrators will be followed by individual works according to CW instructions."


Pinhole camera model, Homogeneous coordinates, Planar transformations and parameters, Perspective projection, 3D reconstruction
Epipolar geometry, Image Matching, Hough Transform, RANSAC

Dimensionality Reduction, Principle Component Analysis (PCA), Linear discriminant functions, Discriminant Analysis, Data representation, Distances Metrics, Clustering algorithms, Bagging and boosting, Ensemble Learning, Committee Machine, Sequential (Time Series) Data Analysis
Exam Duration: N/A
Coursework contribution: 100%

Term: Spring

Closed or Open Book (end of year exam): N/A

Coursework Requirement:
         Coursework only module

Oral Exam Required (as final assessment): N/A

Prerequisite module(s): None required

Course Homepage:

Book List: