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Applying random matrix theory filters on SenseCam images
Li, Na; Crane, Martin; Gurrin, Cathal; Ruskin, Heather J.
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.
Keyword(s): Lifelog; Image processing; Statistical physics
Publication Date:
2013
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): 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.
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/21307/1/EN95.pdf,
http://ercim-news.ercim.eu/images/stories/EN95/EN95-web.pdf
First Indexed: 2016-07-30 05:05:38 Last Updated: 2019-02-09 06:17:13