| Summary: | 碩士 === 中原大學 === 工業工程研究所 === 82 === The machine vision system apply in the production line is the
solution for the dificulty of the automatic production process.
The merchandize of the machine vision system is used statistics
paptern recognition as classifier method. For statistics and
structure ayalysis method need a complex analysis for each
pattern of data. So, in this study, we will use neural network
to classify the defects of the oil-lid. In this study, image
substraction, image matrix transfer and neural network are
employed to model the machine vision system. Adaptive
Resonance Theory(ART) and Back-Pagation(BP) network are
used which learning and training through five defect
patterns and a good one images, then used such neural networks
to classify the oil-lid which is a good production or one of
the five kind defect of production. The result of the
experiment is: First, without any refined method: CGNN:65%,
ART1: 80%, ART2:87.3%, BP:90.67%. Second, used one refined
method: CGNN:75%, BP:95%. Through refined method, the BP
network has been proved that machine vision system has a good
result in the pattern recognition. The major dificulties
in refining process of the classfication system. First,
the choice of refining system methods depend on the
result of the experiment. Second, it would not know the
reason after the result of refined is bad. Third, it would
not know to stop refining under the unknow refined value.
In this study, it is fucus to solve the troubles of using
refined methods, we can make a bettre result for neural
network in the pattern recognition.
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