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

  • Alexander MacLean
  • Kaylen Pfisterer
  • Robert Amelard
  • Audrey G. Chung
  • Devinder Kumar
  • Alexander Wong

Abstract

Measuring nutritional intake is a tool that is critical to the
monitoring of health, both as an individual or of a group. It is
especially important in the monitoring of those at risk for
malnutrition, an issue which costs billions of dollars globally, and
current methods used in practice are manual, time-consuming,
and have inherent biases and inaccuracies. This study proposes a
novel imaging system with a superpixel-based segmentation
algorithm as part of an automated nutritional intake system. The
study also examines three important parameters of the algorithm
and their ideal values; region size and spatial regularization for
superpixel segmentation, as well as spatial weighting in
clustering. The experimental results demonstrate that the
proposed system is effective in segmenting an image of a plate into
its constituent foods.

Published
2017-10-15
How to Cite
MacLean, A., Pfisterer, K., Amelard, R., Chung, A., Kumar, D., & Wong, A. (2017). Goldilocks and the Three Parameters:Empirically Finding the "Just Right" for Segmenting Food Images for the AFINI-T System. Journal of Computational Vision and Imaging Systems, 3(1). https://doi.org/10.15353/vsnl.v3i1.183
Section
Articles