Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System

Abstract

It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system “mentally” gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an “intelligent guess”, implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results.

Artificial intelligence to estimate wine volume from single-view images

“…Besides, and depending above all on the type of glass, this volume may be lower or higher than that estimated through surveys ( Pechey et al, 2016 ). Thus, the use of images could improve the quality of the data obtained on dietary assessment improving the estimation of portion size/volume ( Yang et al, 2021 ; Jia et al, 2019 ; Fang et al, 2015 ). In fact, the problem of measuring liquid volume in an image can be solved applying Machine Learning & Artificial Intelligence techniques, but is not deeply supported by research yet.…”

SUM-GAN-GEA: Video Summarization Using GAN with Gaussian Distribution and External Attention

“…Among them, unsupervised techniques have attracted more attention in recent years because they do not require manual external intervention (making expensive labels). In recent years, the rapid development of deep learning technologies has achieved success in many fields of application, such as human trajectory prediction [7], sheet metal bending [8], video anomaly detection and classification [9], real-time target detection and tracking in video surveillance systems [10], multi-view learning methods [11], arterial blood pressure prediction [12], the automatic screening of COVID-19 [13], smart parking systems [14], the generation of 3D geometric objects [15], and the estimation of human food volume [16].…”