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Abstract
Improvements are described for a pattern recognition system for recognizing ship-building parts. This is achieved by using a new simple and accurate corner finder. The new system initially finds corners in an edge detected image of a ship’s part and uses that new information to extract Fourier descriptors to feed into a neural network to make decisions about shapes. Results show that the new corner finder was better at distinguishing between various ships’ parts than other corner finders and proved to be a valid approach. The new corner finding technique uses a bottom-up approach to find corners by sampling points in edge-detected images and calculating the distance between the endpoints of a window around each sampled point. The points with the minimum distance are then interpreted as corners. Using an all-or-nothing accuracy measure, the new corner finding technique achieved an improvement over other systems. The new corner finder was included as pre-processing before extracting Fourier descriptors and using the artificial neural networks to identify parts. The whole system recognized parts more quickly and more efficiently than the most recently published systems.
Original language | English |
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Pages (from-to) | 1217-1223 |
Journal | Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture |
Volume | 223 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2009 |
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Dive into the research topics of 'Pre-locating corners in images in order to improve the extraction of Fourier descriptors and subsequent recognition of shipbuilding parts'. Together they form a unique fingerprint.Projects
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Welding
Sanders, D. (PI), Rasol, Z. (CoI), Lambert, G. (CoI) & Tewkesbury, G. (CoI)
6/05/02 → 5/04/07
Project: Research