PUGTIFs: Passively User-Generated Thermal Invariant Features
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 t...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8792055/ |
| _version_ | 1818380748705497088 |
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| author | Edward Jackson Lounis Chermak |
| author_facet | Edward Jackson Lounis Chermak |
| author_sort | Edward Jackson |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T02:23:37Z |
| format | Article |
| id | doaj.art-5c0e5a41221d4c7cae944dfb8dfb84a0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:23:37Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5c0e5a41221d4c7cae944dfb8dfb84a02022-12-21T23:20:26ZengIEEEIEEE Access2169-35362019-01-01710956610957610.1109/ACCESS.2019.29339468792055PUGTIFs: Passively User-Generated Thermal Invariant FeaturesEdward Jackson0https://orcid.org/0000-0003-0324-576XLounis Chermak1Centre of Electronic Warfare Information and Cyber (CEWIC), Cranfield University, Cranfield, U.K.Centre of Electronic Warfare Information and Cyber (CEWIC), Cranfield University, Cranfield, U.K.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.https://ieeexplore.ieee.org/document/8792055/feature detectorimage segmentationmonocular scaled localisationthermal footprint |
| spellingShingle | Edward Jackson Lounis Chermak PUGTIFs: Passively User-Generated Thermal Invariant Features IEEE Access feature detector image segmentation monocular scaled localisation thermal footprint |
| title | PUGTIFs: Passively User-Generated Thermal Invariant Features |
| title_full | PUGTIFs: Passively User-Generated Thermal Invariant Features |
| title_fullStr | PUGTIFs: Passively User-Generated Thermal Invariant Features |
| title_full_unstemmed | PUGTIFs: Passively User-Generated Thermal Invariant Features |
| title_short | PUGTIFs: Passively User-Generated Thermal Invariant Features |
| title_sort | pugtifs passively user generated thermal invariant features |
| topic | feature detector image segmentation monocular scaled localisation thermal footprint |
| url | https://ieeexplore.ieee.org/document/8792055/ |
| work_keys_str_mv | AT edwardjackson pugtifspassivelyusergeneratedthermalinvariantfeatures AT lounischermak pugtifspassivelyusergeneratedthermalinvariantfeatures |