Towards Computer Vision Powered Color-Nutrient Assessment of Pureed Food

Kaylen J. Pfisterer, Robert Amelard, Braeden Syrnyk, Alexander Wong; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


With one in four individuals afflicted with malnutrition, computer vision may provide a way of introducing a new level of automation in the nutrition field to reliably monitor food and nutrient intake. We present results on the link between color and vitamin A content using transmittance imaging of a pureed foods dilution series in a computer-vision powered intelligent nutrient sensing system prototype and use a fine-tuned deep autoencoder network to predict the relative concentration of sweet potato purees. Results indicate an network accuracy of 80% across beginner (6 month) and intermediate (8 month) commercially prepared pureed sweet potato samples. Network errors may be explained by fundamental differences in optical properties which are further discussed.

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[bibtex]
@InProceedings{Pfisterer_2019_CVPR_Workshops,
author = {Pfisterer, Kaylen J. and Amelard, Robert and Syrnyk, Braeden and Wong, Alexander},
title = {Towards Computer Vision Powered Color-Nutrient Assessment of Pureed Food},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}