Download Full Module Description, 2006/07

Full Module Description, 2006/07
Module Title Computational Vision and Language
Module Provider (AoU): Computing
Level: 2
Module Co-ordinator: Dr Andrew Hippisley
Subject (3 letters): COM
Number of Credits: 10
Module Availability
Spring 2006
Assessment Pattern
Unit(s) of Assessment
Two hour exam
Computational vision coursework
Computational language coursework
Weighting Towards
Module Mark( %)
50
35
15
Qualifying Condition(s)
Overall pass
Pre-requisite/Co-requisites
Artificial Intelligence (level 2, Autumn semester)
Module Overview
This module will take students from a general understanding of the fundamental
concepts and techniques in Artificial Intelligence introduced in the previous semester
to a more specific understanding of their application to two highly important areas of
intelligent computing, the processing of vision and natural language.
Module Aims
For the computational vision component, the module will provide an introduction to
the computational perception domain for second level students and focus on
fundamental computation problems in visual perception
For the computational language component, the module aims to the distinguish
features of natural language systems within the general context of artificial intelligent
systems
Learning Outcomes
Computational vision
By the end of the module, students will be able to conduct scientific analysis on
problems and issues in visual perception, identify the essential computational
components, and be able to convert these into computational models and algorithms.
Computational Language
By the end of the module students will be able to demonstrate a detailed
understanding of the basics of word-based and sentence-based systems; and
distinguish rule based approaches from statistics based approaches.
For both components students will be furnished with a good generic knowledge of
information extraction techniques, and will have an opportunity to develop analytical
and practical skills in programming and systems development.
Module Content
Introduction to the module
 re-cap on AI fundamentals
 introduction to natural language understanding
 introduction to visual perception and processing
Visual perception and processing
 Vision representation
o Human vision and machine vision
o Vision system architecture
 Image processing and feature extraction
o Convolution and kernels
o Histogram
o Colour and texture analysis
 Segmentation
o Region based segmentation
o thresholding
 Classification
o Classifiers
o Similarity measure
Natural language understanding
 Finite State Automata and Regular Expressions
o Deterministic and non-deterministic and Finite State Automata for string
analysis
o Finite State Transducers and morphological parsing
o Regular expressions and string processing

Computational Grammars
o Sentence structure
o Phrase Structure Rules and Context Free Grammars
o Generalised Phrase Structure Grammar and features

Content analysis
o electronic corpora for content analysis
o types and lemmas, tokens and word-forms
o frequency distributions and Zipf’s law
o tagging corpora
Methods of Teaching/Learning
The module will be taught by a combination of lectures and practical sessions
Selected Texts/Journals
Essential reading
Nick Efford, Digital Image Processing, A Practical Introduction using Java (2000),
Addison Wesley, ISBN 0201596237.
Jurafsky, Daniel; and James H. Martin (2000). Speech and Language Processing.
New Jersey: Prentice Hall
Recommended
R Gonzalez, R E Woods, S. L Eddins, Digital Image Processing Using Matlab, ISBN
0-13-008519-7
Coleman, John (2005). Introducing Speech and Language Processing. Cambridge:
CUP.
Date Last Revised: 8.05.06