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dc.contributor.advisorGovender, Poobalan
dc.contributor.authorLi, Zhi
dc.date.accessioned2010-02-04T07:18:12Z
dc.date.available2011-03-31T22:20:06Z
dc.date.issued2009
dc.identifier.other325555
dc.identifier.urihttp://hdl.handle.net/10321/494
dc.descriptionThesis submitted in compliance with the requirements for the Master's Degree in Technology: Industrial Engineering, Department of Industrial Engineering, Durban University of Technology, 2009.en_US
dc.description.abstractThis research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.en_US
dc.format.extent133 pen_US
dc.language.isoenen_US
dc.subjectComputer vision--Industrial applicationsen_US
dc.subjectSorting devicesen_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectArtificial intelligenceen_US
dc.subjectAlgorithmsen_US
dc.titleDesign and implementation of an intelligent vision and sorting systemen_US
dc.typeThesisen_US


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