PUGTIFs: Passively user-generated thermal invariant features

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dc.contributor.author Jackson, Edward
dc.contributor.author Chermak, Lounis
dc.date.accessioned 2019-09-25T13:33:37Z
dc.date.available 2019-09-25T13:33:37Z
dc.date.issued 2019-08-08
dc.identifier.citation Jackson E and Chermak L. (2019) PUGTIFs: Passively user-generated thermal invariant features. IEEE Access, Volume 7, 2019, pp.109566-109576 en_UK
dc.identifier.issn 2169-3536
dc.identifier.uri https://doi.org/10.1109/ACCESS.2019.2933946
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/14564
dc.description.abstract Feature detection is a vital aspect of computer vision applications, but adverse environments, distance and illumination can affect the quality and repeatability of features or even prevent their identification. Invariance to these constraints would make an ideal feature attribute. Here we propose the first exploitation of consistently occurring thermal signatures generated by a moving platform, a paradigm we define as passively user-generated thermal invariant features (PUGTIFs). In this particular instance, the PUGTIF concept is applied through the use of thermal footprints that are passively and continuously user generated by heat differences, so that features are no longer dependent on the changing scene structure (as in classical approaches) but now maintain a spatial coherency and remain invariant to changes in illumination. A framework suitable for any PUGTIF has been designed consisting of three methods: first, the known footprint size is used to solve for monocular localisation and thus scale ambiguity; second, the consistent spatial pattern allows us to determine heading orientation; and third, these principles are combined in our automated thermal footprint detector (ATFD) method to achieve segmentation/feature detection. We evaluated the detection of PUGTIFs in four laboratory environments (sand, grass, grass with foliage, and carpet) and compared ATFD to typical image segmentation methods. We found that ATFD is superior to other methods while also solving for scaled monocular camera localisation and providing user heading in multiple environments. en_UK
dc.language.iso en en_UK
dc.publisher IEEE en_UK
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject feature detector en_UK
dc.subject image segmentation en_UK
dc.subject monocular scaled localisation en_UK
dc.subject thermal footprint en_UK
dc.title PUGTIFs: Passively user-generated thermal invariant features en_UK


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