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dc.contributor.authorFang, S.
dc.contributor.authorZhu, F.
dc.contributor.authorBoushey, Carol
dc.contributor.authorDelp, E.
dc.date.accessioned2018-06-29T12:27:50Z
dc.date.available2018-06-29T12:27:50Z
dc.date.created2018-06-29T12:09:04Z
dc.date.issued2018
dc.identifier.citationFang, S. and Zhu, F. and Boushey, C. and Delp, E. 2018. The use of co-occurrence patterns in single image based food portion estimation, pp. 462-466.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/68941
dc.identifier.doi10.1109/GlobalSIP.2017.8308685
dc.description.abstract

© 2017 IEEE. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. Food portions estimation is a challenging problem as food preparation and consumption process pose large variations on food shapes and appearances. We use geometric model based technique to estimate food portions and further improve estimation accuracy using co-occurrence patterns. We estimate the food portion co-occurrence patterns from food images we collected from dietary studies using the mobile Food Record (mFR) system we developed. Co-occurrence patterns is used as prior knowledge to refine portion estimation results. We show that the portion estimation accuracy has been improved when in-corporating the co-occurrence patterns as contextual information.

dc.titleThe use of co-occurrence patterns in single image based food portion estimation
dc.typeConference Paper
dcterms.source.volume2018-January
dcterms.source.startPage462
dcterms.source.endPage466
dcterms.source.title2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
dcterms.source.series2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
dcterms.source.isbn9781509059904
curtin.departmentSchool of Public Health
curtin.accessStatusFulltext not available


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