@article{MacLean_Pfisterer_Amelard_Chung_Kumar_Wong_2017, title={Goldilocks and the Three Parameters:Empirically Finding the "Just Right" for Segmenting Food Images for the AFINI-T System}, volume={3}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/183}, DOI={10.15353/vsnl.v3i1.183}, abstractNote={<p>Measuring nutritional intake is a tool that is critical to the<br />monitoring of health, both as an individual or of a group. It is<br />especially important in the monitoring of those at risk for<br />malnutrition, an issue which costs billions of dollars globally, and<br />current methods used in practice are manual, time-consuming,<br />and have inherent biases and inaccuracies. This study proposes a<br />novel imaging system with a superpixel-based segmentation<br />algorithm as part of an automated nutritional intake system. The<br />study also examines three important parameters of the algorithm<br />and their ideal values; region size and spatial regularization for<br />superpixel segmentation, as well as spatial weighting in<br />clustering. The experimental results demonstrate that the<br />proposed system is effective in segmenting an image of a plate into<br />its constituent foods.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={MacLean, Alexander and Pfisterer, Kaylen and Amelard, Robert and Chung, Audrey G. and Kumar, Devinder and Wong, Alexander}, year={2017}, month={Oct.} }