Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619043
Title: Development of a symbol recognition system using evolutionary computing methods
Author: Tann, Phillip Leslie
Awarding Body: University of Sunderland
Current Institution: University of Sunderland
Date of Award: 2005
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Abstract:
This thesis provides details relating to the developments of work by the author in partial fulfilment of degree of Doctor of Philosophy within the University of Sunderland's laboratories. The author discusses efficient alternative methods that are employed to detect two dimensional symbols embodied within an image. The symbols of particular interest represent telecom network components from a paper map. British Telecom store network component maps in scanned digital format taken from paper maps which have been subjected to updates over many years with respect to new and modified equipment. The aim of this project is to create a 'netlist' of components and their circuit connection. This 'netlist' offers a descriptive circuit topology that can be interfaced and employed in the creation of network schematic for inclusion into other applications such as planning software. The author has adopted an approach to follow fundamental human qualitative methods of image recognition within the novel design of quantitative machine recognition methods. Principles of evolution are employed within the development of an application which include a number of novel algorithms based on soft computing. Colour frequency analysis and morphologic processing are also employed as methods to implement recognition evaluation. These algorithms are encompassed within an Evolutionary Image Recognition Algorithm (EIRA). A series of experiments have been carried out to determine the efficiency of the employed methods for the symbol extraction and results have been obtained that show the approach adopted by the author provides a rapid symbol recognition system.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.619043  DOI: Not available
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