Deep learning for underwater scene classification

Ammar Mahmood

Research output: ThesisDoctoral Thesis

193 Downloads (Pure)

Abstract

This thesis poses the underwater image analysis as challenging computer vision problems and aims to address those using deep learning techniques. It proposes state-of-the-art deep learning methods to automatically classify coral reefs and kelp forests. It also extends these methods to automatically depict the trends of changing coral population and coverage of kelp forests. Lastly, the thesis proposes to generate synthetic parts data for automatic detection of lobsters in the complex underwater background, a challenging task in the marine research community.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Award date25 Mar 2019
DOIs
Publication statusUnpublished - 2019

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Reefs
Image analysis
Computer vision
Deep learning

Cite this

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title = "Deep learning for underwater scene classification",
abstract = "This thesis poses the underwater image analysis as challenging computer vision problems and aims to address those using deep learning techniques. It proposes state-of-the-art deep learning methods to automatically classify coral reefs and kelp forests. It also extends these methods to automatically depict the trends of changing coral population and coverage of kelp forests. Lastly, the thesis proposes to generate synthetic parts data for automatic detection of lobsters in the complex underwater background, a challenging task in the marine research community.",
keywords = "Deep Learning, Coral Reefs, Image Classification, Lobster Detection, Marine Imagery",
author = "Ammar Mahmood",
year = "2019",
doi = "10.26182/5cd131a06078f",
language = "English",
school = "The University of Western Australia",

}

Mahmood, A 2019, 'Deep learning for underwater scene classification', Doctor of Philosophy, The University of Western Australia. https://doi.org/10.26182/5cd131a06078f

Deep learning for underwater scene classification. / Mahmood, Ammar.

2019.

Research output: ThesisDoctoral Thesis

TY - THES

T1 - Deep learning for underwater scene classification

AU - Mahmood, Ammar

PY - 2019

Y1 - 2019

N2 - This thesis poses the underwater image analysis as challenging computer vision problems and aims to address those using deep learning techniques. It proposes state-of-the-art deep learning methods to automatically classify coral reefs and kelp forests. It also extends these methods to automatically depict the trends of changing coral population and coverage of kelp forests. Lastly, the thesis proposes to generate synthetic parts data for automatic detection of lobsters in the complex underwater background, a challenging task in the marine research community.

AB - This thesis poses the underwater image analysis as challenging computer vision problems and aims to address those using deep learning techniques. It proposes state-of-the-art deep learning methods to automatically classify coral reefs and kelp forests. It also extends these methods to automatically depict the trends of changing coral population and coverage of kelp forests. Lastly, the thesis proposes to generate synthetic parts data for automatic detection of lobsters in the complex underwater background, a challenging task in the marine research community.

KW - Deep Learning

KW - Coral Reefs

KW - Image Classification

KW - Lobster Detection

KW - Marine Imagery

U2 - 10.26182/5cd131a06078f

DO - 10.26182/5cd131a06078f

M3 - Doctoral Thesis

ER -