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Applying random matrix theory filters on SenseCam images |
Li, Na; Crane, Martin; Gurrin, Cathal; Ruskin, Heather J.
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Even though Microsoft’s SeneseCam can be effective as a memory-aid device, there exists a substantial challenge in effectively managing the vast amount of images that are maintained by this device. Deconstructing a sizeable collection of images into meaningful events for users represents a significant task. Such events may be identified by applying Random Matrix Theory (RMT) to a cross-correlation matrix C that has been constructed using SenseCam lifelog data streams. Overall, the RMT technique proves promising for major event detection in SenseCam images.
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Keyword(s):
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Lifelog; Image processing; Statistical physics |
Publication Date:
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2013 |
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Type:
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Other |
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Peer-Reviewed:
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Unknown |
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Language(s):
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English |
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Institution:
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Dublin City University |
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Citation(s):
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Li, Na, Crane, Martin ORCID: 0000-0001-7598-3126 <https://orcid.org/0000-0001-7598-3126>, Gurrin, Cathal ORCID: 0000-0003-2903-3968 <https://orcid.org/0000-0003-2903-3968> and Ruskin, Heather J. ORCID: 0000-0001-7101-2242 <https://orcid.org/0000-0001-7101-2242> (2013) Applying random matrix theory filters on SenseCam images. ERCIM News, 95 (10). pp. 19-20. |
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File Format(s):
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application/pdf |
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Related Link(s):
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http://doras.dcu.ie/21307/1/EN95.pdf,
http://ercim-news.ercim.eu/images/stories/EN95/EN95-web.pdf |
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First Indexed:
2016-07-30 05:05:38 Last Updated:
2019-02-09 06:17:13 |
