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Guide to convolutional neural networks: a practical application to traffic-sign detection and classification

Detalles Bibliográficos
Autores principales: Habibi Aghdam, Hamed, Jahani Heravi, Elnaz
Lenguaje:eng
Publicado: Springer 2017
Materias:
Acceso en línea:http://cds.cern.ch/record/2297588
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author Habibi Aghdam, Hamed
Jahani Heravi, Elnaz
author_facet Habibi Aghdam, Hamed
Jahani Heravi, Elnaz
author_sort Habibi Aghdam, Hamed
collection CERN
id cern-2297588
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
publisher Springer
record_format invenio
spelling cern-22975882021-04-21T18:58:40Zhttp://cds.cern.ch/record/2297588engHabibi Aghdam, HamedJahani Heravi, ElnazGuide to convolutional neural networks: a practical application to traffic-sign detection and classificationComputing and ComputersSpringeroai:cds.cern.ch:22975882017
spellingShingle Computing and Computers
Habibi Aghdam, Hamed
Jahani Heravi, Elnaz
Guide to convolutional neural networks: a practical application to traffic-sign detection and classification
title Guide to convolutional neural networks: a practical application to traffic-sign detection and classification
title_full Guide to convolutional neural networks: a practical application to traffic-sign detection and classification
title_fullStr Guide to convolutional neural networks: a practical application to traffic-sign detection and classification
title_full_unstemmed Guide to convolutional neural networks: a practical application to traffic-sign detection and classification
title_short Guide to convolutional neural networks: a practical application to traffic-sign detection and classification
title_sort guide to convolutional neural networks: a practical application to traffic-sign detection and classification
topic Computing and Computers
url http://cds.cern.ch/record/2297588
work_keys_str_mv AT habibiaghdamhamed guidetoconvolutionalneuralnetworksapracticalapplicationtotrafficsigndetectionandclassification
AT jahaniheravielnaz guidetoconvolutionalneuralnetworksapracticalapplicationtotrafficsigndetectionandclassification