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CM20220: Fundamentals of pattern analysis

Follow this link for further information on academic years Academic Year: 2014/5
Further information on owning departmentsOwning Department/School: Department of Computer Science
Further information on credits Credits: 6
Further information on unit levels Level: Intermediate (FHEQ level 5)
Further information on teaching periods Period: Semester 2
Further information on unit assessment Assessment Summary: CW 25%, EX 75%
Further information on unit assessment Assessment Detail:
  • Coursework (CW 25%)
  • Examination (EX 75%)
Further information on supplementary assessment Supplementary Assessment: CM20220A Mandatory Extra Work (where allowed by programme regulations)
Further information on requisites Requisites: Before taking this unit you must take CM10197
Further information on descriptions Description: Aims:
To provide mathematical foundations underpinning concepts of digital signal processing (DSP), probabilistic methods and search/optimization algorithms demanded by Pattern Recognition, Computer Vision and Sound.

Learning Outcomes:
On completion of this unit students will be able to:
1. understand and apply common signal processing operations, understanding their effect in terms of spatial and frequency domain.
2. understand and apply basic probabilistic methods to infer meaning from images.
3. understand and apply appropriate search strategies and optimization technique to solve Computer Vision problems.

Skills:
Problem Solving (T/F, A), Application of Number (T/F, A).

Content:
FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING (DSP)
* Frequency space
- Continuous and Discrete Fourier transform (1D and 2D).
- Discrete signal and image representation. Aliasing.
- Taylor expansion
* Digital signal processing (1D and 2D)
- Convolution theorem
- Low pass filters and antialiasing. Overview of high pass filters.
- Image transformation, interpolation
PROBABILITY AND PATTERN CLASSIFICATION
* Means, standard deviation and variance.
* Bayes theorem. Bayesian Inference. Priors.
* Principal Component Analysis. Eigenmodels.
* Classification
- Concept of a feature space
- Distance metrics
- Supervised vs. Unsupervised learning.
* Gaussian Mixture Models
SEARCH AND OPTIMIZATION
* Deterministic methods
- Least squares
- Best first, Gradient descent
* Stochastic methods
- k-means
- Simulated annealing
- Evolutionary search e.g. genetic algorithms
Further information on programme availabilityProgramme availability:

CM20220 is Compulsory on the following programmes:

Department of Computer Science
  • USCM-AFB06 : BSc(Hons) Computer Science (Year 2)
  • USCM-AAB07 : BSc(Hons) Computer Science with Study year abroad (Year 2)
  • USCM-AKB07 : BSc(Hons) Computer Science with Year long work placement (Year 2)
  • USCM-AFM01 : MComp(Hons) Computer Science (Year 2)
  • USCM-AAM02 : MComp(Hons) Computer Science with Study year abroad (Year 2)
  • USCM-AKM02 : MComp(Hons) Computer Science with Year long work placement (Year 2)
  • USCM-AFB20 : BSc(Hons) Computer Science and Mathematics (Year 2)
  • USCM-AAB20 : BSc(Hons) Computer Science and Mathematics with Study year abroad (Year 2)
  • USCM-AKB20 : BSc(Hons) Computer Science and Mathematics with Year long work placement (Year 2)
  • USCM-AFM14 : MComp(Hons) Computer Science and Mathematics (Year 2)
  • USCM-AAM14 : MComp(Hons) Computer Science and Mathematics with Study year abroad (Year 2)
  • USCM-AKM14 : MComp(Hons) Computer Science and Mathematics with Year long work placement (Year 2)

Notes:
* This unit catalogue is applicable for the 2014/15 academic year only. Students continuing their studies into 2015/16 and beyond should not assume that this unit will be available in future years in the format displayed here for 2014/15.
* Programmes and units are subject to change at any time, in accordance with normal University procedures.
* Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.