Zhang, Wenshu, Ezard, Thomas, Searle-Barnes, Alex, Brombacher, Anieke, Katsamenis, Orestis and Nixon, Mark (2020) Towards understanding speciation by automated extraction and description of 3D foraminifera stacks. In, 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, , (doi:10.1109/SSIAI49293.2020.9094611), 2020-March) The Southwest Symposium on Image Analysis and Interpretation (29/03/20 - 31/03/20) Institute of Electrical and Electronics Engineers Inc., pp. 30-33. (doi:10.1109/SSIAI49293.2020.9094611).
Abstract
The sheer volume of 3D data restricts understanding of genetic speciation when analyzing specimens of planktonic foraminifera and so we develop an end-to-end computer vision system to solve and extend this. The observed fossils are planktonic foraminifera, which are single-celled organisms that live in vast numbers in the world's oceans. Each foram retains a complete record of its size and shape at each stage along its journey through life. In this study, a variety of individual foraminifera are analyzed to study the differences among them and compared with manually labelled ground truth. This is an approach which (i) automatically reconstructs individual chambers for each specimen from image sequences, (ii) uses a shape signature to describe different types of species. The automated analysis by computer vision gives insight that was hitherto unavailable in biological analysis: analyzing shape implies understanding spatial arrangement and this is new to the biological analysis of these specimens. By processing datasets of 3D samples containing 9GB of points, we show that speciation can indeed now be analyzed and that automated analysis from morphological features leads to new insight into the origins of life.
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- Current Faculties > Faculty of Engineering and Physical Sciences > School of Electronics and Computer Science > Vision, Learning and Control
School of Electronics and Computer Science > Vision, Learning and Control - Faculties (pre 2018 reorg) > Faculty of Engineering and the Environment (pre 2018 reorg) > Southampton Marine & Maritime Institute (pre 2018 reorg)
- Faculties (pre 2018 reorg) > Faculty of Natural and Environmental Sciences (pre 2018 reorg) > Ocean and Earth Science (pre 2018 reorg) > Paleooceanography & Palaeoclimate (pre 2018 reorg)
Current Faculties > Faculty of Environmental and Life Sciences > School of Ocean and Earth Science > Ocean and Earth Science (pre 2018 reorg) > Paleooceanography & Palaeoclimate (pre 2018 reorg)
School of Ocean and Earth Science > Ocean and Earth Science (pre 2018 reorg) > Paleooceanography & Palaeoclimate (pre 2018 reorg) - Faculties (pre 2018 reorg) > Faculty of Natural and Environmental Sciences (pre 2018 reorg) > Institute for Life Sciences (pre 2018 reorg)
Current Faculties > Faculty of Environmental and Life Sciences > Institute for Life Sciences > Institute for Life Sciences (pre 2018 reorg)
Institute for Life Sciences > Institute for Life Sciences (pre 2018 reorg) - Current Faculties > Faculty of Environmental and Life Sciences > School of Ocean and Earth Science > Paleooceanography and Palaeoclimate
School of Ocean and Earth Science > Paleooceanography and Palaeoclimate - Current Faculties > Faculty of Engineering and Physical Sciences > School of Engineering > Mechanical Engineering > Engineering Materials and Surface Engineering Group
Mechanical Engineering > Engineering Materials and Surface Engineering Group
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