Version 1
: Received: 26 February 2019 / Approved: 27 February 2019 / Online: 27 February 2019 (12:14:05 CET)
Version 2
: Received: 12 March 2019 / Approved: 12 March 2019 / Online: 12 March 2019 (10:18:12 CET)
How to cite:
de Mingo López, L.F.; Morales Lucas, C.; Gómez Blas, N.; Ivanova, K. Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails. Preprints2019, 2019020257. https://doi.org/10.20944/preprints201902.0257.v1
de Mingo López, L.F.; Morales Lucas, C.; Gómez Blas, N.; Ivanova, K. Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails. Preprints 2019, 2019020257. https://doi.org/10.20944/preprints201902.0257.v1
de Mingo López, L.F.; Morales Lucas, C.; Gómez Blas, N.; Ivanova, K. Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails. Preprints2019, 2019020257. https://doi.org/10.20944/preprints201902.0257.v1
APA Style
de Mingo López, L.F., Morales Lucas, C., Gómez Blas, N., & Ivanova, K. (2019). Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails. Preprints. https://doi.org/10.20944/preprints201902.0257.v1
Chicago/Turabian Style
de Mingo López, L.F., Nuria Gómez Blas and Krassimira Ivanova. 2019 "Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails" Preprints. https://doi.org/10.20944/preprints201902.0257.v1
Abstract
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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.