GR Semicolon EN

Show simple item record

dc.contributor.author
Mantela, Aikaterini
el
dc.date.accessioned
2020-06-17T12:36:32Z
dc.date.available
2020-06-18T00:00:32Z
dc.date.issued
2020-06-17
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29653
dc.rights
Default License
dc.subject
Rock image classification
en
dc.subject
Image processing
en
dc.subject
Mining
en
dc.subject
Ore
en
dc.subject
Waste
en
dc.subject
Cut-off grade
en
dc.subject
Underground mine
en
dc.subject
Geology
en
dc.subject
Face mapping
en
dc.subject
Autonomous rock classification
en
dc.subject
Image grading
en
dc.subject
Rock image analysis
en
dc.subject
Machine learning
en
dc.subject
Artificial intelligence
en
dc.title
Rock Image Classification Ore/Waste – Cut Off Identification For Decision Making
el
heal.type
masterThesis
en_US
heal.creatorID.email
a.mantela@ihu.edu.gr
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2020-05-13
heal.abstract
For the identification complexity of rock image classification, based on a certain cut-off grade, an automatic classification recognition of rock images method is proposed in this dissertation, which is a part of the MSc in Data Science at the International Hellenic University. The main topic of this research was the identification of Rock image Classification through images in order to use the results for further decision making. Basis of specific approach in face mapping images taken by mine geologists for traditional rock-mass characterization. Digital grey image processing of rock face mapping images is used for features extraction. Then features contains the process of knowledge extraction from images in Matlab and furthermore as the classification procedure in Weka, in which different classifiers have been trained and tested in order to classify Ore or Waste. Finally, the model output is the rock image classification. Hellas Gold Company, subsidiary of Eldorado Gold provided a sample of 600 images for the case study. Specifically face mapping images are from Olympias mine located in North-East Chalcidice Prefecture, Greece. For the experiment, the dataset is divided into 300 images as a training dataset in order the algorithm to classify the >15% Ore and 300 images for classify the <15% of Waste. As an outcome the optimum classifier reached 98.5% accuracy for automatic identification of rock face mapping image. Therefore, the proposed method for improving geological pattern is effective and can result in accepted identification performance for rock image classification quickly and accurately. Nevertheless, the target is to reach an autonomous level such that no human intervention will be necessary.
en
heal.advisorName
Kotsakis, Rigas
en
heal.committeeMemberName
Kotsakis, Rigas
en
heal.committeeMemberName
Berberidis, Christos
en
heal.committeeMemberName
Baltatzis, Dimitrios
el
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


This item appears in the following Collection(s)

Show simple item record

Related Items