Towards computer vision powered color-nutrient assessment of pureed food

Autor:	Pfisterer, Kaylen J., Amelard, Robert, Syrnyk, Braeden, Wong, Alexander
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Neural and Evolutionary Computing Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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. In this study, we present a novel approach to modeling the link between color and vitamin A content using transmittance imaging of a pureed foods dilution series in a computer vision powered nutrient sensing system via a fine-tuned deep autoencoder network, which in this case was trained to predict the relative concentration of sweet potato purees. Experimental results show the deep autoencoder network can achieve an accuracy of 80% across beginner (6 month) and intermediate (8 month) commercially prepared pureed sweet potato samples. Prediction errors may be explained by fundamental differences in optical properties which are further discussed. Comment: 3 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1905.00310 Zobrazit plný text záznamu View this record from Arxiv