Goldilocks and the Three Parameters:Empirically Finding the "Just Right" for Segmenting Food Images for the AFINI-T System

Measuring nutritional intake is a tool that is critical to themonitoring of health, both as an individual or of a group. It isespecially important in the monitoring of those at risk formalnutrition, an issue which costs billions of dollars globally, andcurrent methods used in practice are manual, ti...

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Bibliographic Details
Published inJournal of Computational Vision and Imaging Systems Vol. 3; no. 1
Main Authors MacLean, Alexander, Pfisterer, Kaylen, Amelard, Robert, Chung, Audrey G., Kumar, Devinder, Wong, Alexander
Format Journal Article
LanguageEnglish
Published 15.10.2017
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Summary:Measuring nutritional intake is a tool that is critical to themonitoring of health, both as an individual or of a group. It isespecially important in the monitoring of those at risk formalnutrition, an issue which costs billions of dollars globally, andcurrent methods used in practice are manual, time-consuming,and have inherent biases and inaccuracies. This study proposes anovel imaging system with a superpixel-based segmentationalgorithm as part of an automated nutritional intake system. Thestudy also examines three important parameters of the algorithmand their ideal values; region size and spatial regularization forsuperpixel segmentation, as well as spatial weighting inclustering. The experimental results demonstrate that theproposed system is effective in segmenting an image of a plate intoits constituent foods.
ISSN:2562-0444
2562-0444
DOI:10.15353/vsnl.v3i1.183