Please use this identifier to cite or link to this item:
Title: Design and implementation of an intelligent vision and sorting system
Authors: Li, Zhi 
Keywords: Computer vision--Industrial applications;Sorting devices;Neural networks (Computer science);Artificial intelligence;Algorithms
Issue Date: 2009
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.
Thesis submitted in compliance with the requirements for the Master's Degree in Technology: Industrial Engineering, Durban University of Technology, Durban, South Africa, 2009.
Appears in Collections:Theses and dissertations (Engineering and Built Environment)

Files in This Item:
File Description SizeFormat
Li_2009.pdf4.1 MBAdobe PDFThumbnail
Show full item record

Page view(s) 10

checked on Jul 15, 2024

Download(s) 20

checked on Jul 15, 2024

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.