Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
175 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Image Based Food Energy Estimation With Depth Domain Adaptation (2208.12153v1)

Published 25 Aug 2022 in cs.CV, cs.AI, and cs.LG

Abstract: Assessment of dietary intake has primarily relied on self-report instruments, which are prone to measurement errors. Dietary assessment methods have increasingly incorporated technological advances particularly mobile, image based approaches to address some of these limitations and further automation. Mobile, image-based methods can reduce user burden and bias by automatically estimating dietary intake from eating occasion images that are captured by mobile devices. In this paper, we propose an "Energy Density Map" which is a pixel-to-pixel mapping from the RGB image to the energy density of the food. We then incorporate the "Energy Density Map" with an associated depth map that is captured by a depth sensor to estimate the food energy. The proposed method is evaluated on the Nutrition5k dataset. Experimental results show improved results compared to baseline methods with an average error of 13.29 kCal and an average percentage error of 13.57% between the ground-truth and the estimated energy of the food.

Citations (8)

Summary

We haven't generated a summary for this paper yet.