Classification of Aggregates Using Basic Shape Parameters Through Neural Networks

In this paper, the aim is to classify natural or crushed aggregates by using concrete and asphalt mixes through Artificial Neural Networks. For classification, it was a used the feature vector which was calculated by using digital image processing techniques. Of the five different type coarse aggreg...

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Main Authors: Mahmut SİNECEN, Metehan MAKİNACI
Format: Article
Language:English
Published: Pamukkale University 2010-02-01
Series:Pamukkale University Journal of Engineering Sciences
Subjects:
Online Access:http://dergipark.ulakbim.gov.tr/pajes/article/view/5000088727
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author Mahmut SİNECEN
Metehan MAKİNACI
author_facet Mahmut SİNECEN
Metehan MAKİNACI
author_sort Mahmut SİNECEN
collection DOAJ
description In this paper, the aim is to classify natural or crushed aggregates by using concrete and asphalt mixes through Artificial Neural Networks. For classification, it was a used the feature vector which was calculated by using digital image processing techniques. Of the five different type coarse aggregates images were taken with 45o and 90o by a 10 Mp (Sony DSC-R1) and 7.1 Mp (Canon EOS 350D) camera. Aggregates images were processed and analyzed by using MATLAB Image Processing and Neural Network Toolbox. Classification process was made with totally 18 feature vectors, which is 9 vectors each angles, by neural network. Results showed image processing and neural networks which are important methods for founding shape parameters and classification of aggregates, and performance, cost and time consuming factors of automation systems in aggregate sources will be effective with these methods.
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spelling doaj.art-42e486c64827493082854fbf1e998a632023-02-15T16:09:09ZengPamukkale UniversityPamukkale University Journal of Engineering Sciences1300-70092147-58812010-02-011621491535000082796Classification of Aggregates Using Basic Shape Parameters Through Neural NetworksMahmut SİNECENMetehan MAKİNACIIn this paper, the aim is to classify natural or crushed aggregates by using concrete and asphalt mixes through Artificial Neural Networks. For classification, it was a used the feature vector which was calculated by using digital image processing techniques. Of the five different type coarse aggregates images were taken with 45o and 90o by a 10 Mp (Sony DSC-R1) and 7.1 Mp (Canon EOS 350D) camera. Aggregates images were processed and analyzed by using MATLAB Image Processing and Neural Network Toolbox. Classification process was made with totally 18 feature vectors, which is 9 vectors each angles, by neural network. Results showed image processing and neural networks which are important methods for founding shape parameters and classification of aggregates, and performance, cost and time consuming factors of automation systems in aggregate sources will be effective with these methods.http://dergipark.ulakbim.gov.tr/pajes/article/view/5000088727Agrega, Görüntü işleme, Yapay sinir ağları.
spellingShingle Mahmut SİNECEN
Metehan MAKİNACI
Classification of Aggregates Using Basic Shape Parameters Through Neural Networks
Pamukkale University Journal of Engineering Sciences
Agrega, Görüntü işleme, Yapay sinir ağları.
title Classification of Aggregates Using Basic Shape Parameters Through Neural Networks
title_full Classification of Aggregates Using Basic Shape Parameters Through Neural Networks
title_fullStr Classification of Aggregates Using Basic Shape Parameters Through Neural Networks
title_full_unstemmed Classification of Aggregates Using Basic Shape Parameters Through Neural Networks
title_short Classification of Aggregates Using Basic Shape Parameters Through Neural Networks
title_sort classification of aggregates using basic shape parameters through neural networks
topic Agrega, Görüntü işleme, Yapay sinir ağları.
url http://dergipark.ulakbim.gov.tr/pajes/article/view/5000088727
work_keys_str_mv AT mahmutsinecen classificationofaggregatesusingbasicshapeparametersthroughneuralnetworks
AT metehanmakinaci classificationofaggregatesusingbasicshapeparametersthroughneuralnetworks