Pattern recognition with convolutional neural networks: humpback whale tails
This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens from the unique patterns of their tails. Starting from a dataset composed of images of whale tails, all the phases of the process of creation and training of a neural n...
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ftunivpmadrid:oai:oa.upm.es:68093 2023-05-15T16:35:49+02:00 Pattern recognition with convolutional neural networks: humpback whale tails Mingo Lopez, Fernando De Morales Lucas, Clemencio Gomez Blas, Nuria Ivanova, Krassimira 2019-02 application/pdf https://oa.upm.es/68093/ eng eng E.T.S.I de Sistemas Informáticos (UPM) https://www.preprints.org/ info:eu-repo/semantics/altIdentifier/doi/10.20944/preprints201902.0257.v1 https://oa.upm.es/68093/ https://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess CC-BY-NC-ND Preprints, ISSN 2095-8099, 2019-02 Informática info:eu-repo/semantics/article Artículo PeerReviewed 2019 ftunivpmadrid https://doi.org/10.20944/preprints201902.0257.v1 2022-06-06T18:28:20Z This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens from the unique patterns of their tails. Starting from a dataset composed of images of whale tails, all the phases of the process of creation and training of a neural network are detailed ? from the analysis and pre-processing of images to the elaboration of predictions, using TensorFlow and Keras frameworks. Other possible alternatives are also explained when it comes to tackling this problem and the complications that have arisen during the process of developing this paper. Article in Journal/Newspaper Humpback Whale Archivo Digital UPM (Universidad Politécnica de Madrid) |
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Open Polar |
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Archivo Digital UPM (Universidad Politécnica de Madrid) |
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ftunivpmadrid |
language |
English |
topic |
Informática |
spellingShingle |
Informática Mingo Lopez, Fernando De Morales Lucas, Clemencio Gomez Blas, Nuria Ivanova, Krassimira Pattern recognition with convolutional neural networks: humpback whale tails |
topic_facet |
Informática |
description |
This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens from the unique patterns of their tails. Starting from a dataset composed of images of whale tails, all the phases of the process of creation and training of a neural network are detailed ? from the analysis and pre-processing of images to the elaboration of predictions, using TensorFlow and Keras frameworks. Other possible alternatives are also explained when it comes to tackling this problem and the complications that have arisen during the process of developing this paper. |
format |
Article in Journal/Newspaper |
author |
Mingo Lopez, Fernando De Morales Lucas, Clemencio Gomez Blas, Nuria Ivanova, Krassimira |
author_facet |
Mingo Lopez, Fernando De Morales Lucas, Clemencio Gomez Blas, Nuria Ivanova, Krassimira |
author_sort |
Mingo Lopez, Fernando De |
title |
Pattern recognition with convolutional neural networks: humpback whale tails |
title_short |
Pattern recognition with convolutional neural networks: humpback whale tails |
title_full |
Pattern recognition with convolutional neural networks: humpback whale tails |
title_fullStr |
Pattern recognition with convolutional neural networks: humpback whale tails |
title_full_unstemmed |
Pattern recognition with convolutional neural networks: humpback whale tails |
title_sort |
pattern recognition with convolutional neural networks: humpback whale tails |
publisher |
E.T.S.I de Sistemas Informáticos (UPM) |
publishDate |
2019 |
url |
https://oa.upm.es/68093/ |
genre |
Humpback Whale |
genre_facet |
Humpback Whale |
op_source |
Preprints, ISSN 2095-8099, 2019-02 |
op_relation |
https://www.preprints.org/ info:eu-repo/semantics/altIdentifier/doi/10.20944/preprints201902.0257.v1 https://oa.upm.es/68093/ |
op_rights |
https://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.20944/preprints201902.0257.v1 |
_version_ |
1766026122491854848 |