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Design and implementation of an intelligent vision and sorting system

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dc.contributor.advisor Govender, Poobalan
dc.contributor.author Li, Zhi
dc.date.accessioned 2010-02-04T07:18:12Z
dc.date.available 2011-03-31T22:20:06Z
dc.date.issued 2009
dc.identifier.other 325555
dc.identifier.uri http://hdl.handle.net/10321/494
dc.description Thesis 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.abstract This 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.extent 133 p en_US
dc.language.iso en en_US
dc.subject Computer vision--Industrial applications en_US
dc.subject Sorting devices en_US
dc.subject Neural networks (Computer science) en_US
dc.subject Artificial intelligence en_US
dc.subject Algorithms en_US
dc.title Design and implementation of an intelligent vision and sorting system en_US
dc.type Thesis en_US


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