Sea ice surface characterization via semantic segmentation with convolutional neural networks ...
Visual data provides rich information about real-world objects. Computer vision is a substantial and growing field which seeks to distill useful information from photographic imagery. The primary focus of this work centers on the application of machine-learning based computer vision algorithms, alon...
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| Format: | Text |
| Language: | English |
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Memorial University of Newfoundland
2022
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| Online Access: | https://dx.doi.org/10.48336/0kfz-cg67 https://research.library.mun.ca/15765/ |
| Summary: | Visual data provides rich information about real-world objects. Computer vision is a substantial and growing field which seeks to distill useful information from photographic imagery. The primary focus of this work centers on the application of machine-learning based computer vision algorithms, along with minor applications of more traditional computer vision techniques. The specific task approached herein is known as semantic segmentation; the methodology by which each region of an image, at an individual pixel level, is assigned a classification from a predetermined set of possible classes. The classes considered in this work are: open water, level ice, broken ice, ridged ice, ice in flexural failure, and an ‘other’ class. Accurate segmentation of these classes is the primary objective of this work. The imagery utilized in this work were captured from two cameras mounted on different piers of the Confederation Bridge during the local ice season. Several hundred of the images collected have been manually ... |
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