Dish Detection and Segmentation for Dietary Assessment on Smartphones

Dehais, Joachim Blaise; Anthimopoulos, Marios; Mougiakakou, Stavroula (21 August 2015). Dish Detection and Segmentation for Dietary Assessment on Smartphones. In: Murino, Vittorio; Puppo, Enrico; Sona, Diego; Cristani, Marco; Sansone, Carlo (eds.) ICIAP 2015 Workshops. New Trends in Image Analysis and Processing. Lecture Notes in Computer Science: Vol. 9281 (pp. 433-440). Springer 10.1007/978-3-319-23222-5_53

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Diet-related chronic diseases severely affect personal and global health. However, managing or treating these diseases currently requires long training and high personal involvement to succeed. Computer vision systems could assist with the assessment of diet by detecting and recognizing different foods and their portions in images. We propose novel methods for detecting a dish in an image and segmenting its contents with and without user interaction. All methods were evaluated on a database of over 1600 manually annotated images. The dish detection scored an average of 99% accuracy with a .2s/image run time, while the automatic and semi-automatic dish segmentation methods reached average accuracies of 88% and 91% respectively, with an average run time of .5s/image, outperforming competing solutions.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Dehais, Joachim Blaise; Anthimopoulos, Marios and Mougiakakou, Stavroula

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISBN:

978-3-319-23221-8

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

04 Mar 2016 07:55

Last Modified:

04 Mar 2016 07:55

Publisher DOI:

10.1007/978-3-319-23222-5_53

URI:

https://boris.unibe.ch/id/eprint/77895

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