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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