Multi-view Model Contour Matching Based Food Volume Estimation

Zheng, Xin; Gong, Yifei; Lei, Qinyi; Yao, Run; Yin, Qian

doi:10.1007/978-3-319-94229-2_9

Cited by 5 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dimensions (length, width, height) of the food item are calculated first, using the reference object, and then the volume is calculated. The references may include thumbs, hands or random objects [84]- [88].…”

Section: B) Multi-view Methodsmentioning

confidence: 99%

A Systematic Review of Sensor-Based Methodologies for Food Portion Size Estimation

Raju

Sazonov

2021

IEEE Sensors J.

View full text Add to dashboard Cite

Food portion size estimation (FPSE) is critical in dietary assessment and energy intake estimation. Traditional methods such as visual estimation are now replaced by faster, more accurate sensor-based methods. This paper presents a comprehensive review of the use of sensor methodologies for portion size estimation. The review was conducted using the PRISMA guidelines and full texts of 67 scientific articles were reviewed. The contributions of this paper are threefold: i) A taxonomy for sensor-based (SB) FPSE methods was identified, classifying the sensors (as wearable, portable and stationary) and the methodology (as direct and indirect). ii) A novel comprehensive review of the state-of-theart SB-FPSE methods was conducted and 5 sensor modalities (Acoustic, Strain, Imaging, Weighing, and Motion sensors) were identified. iii) The accuracy of portion size estimation and the applicability to free-living conditions of these SB-FPSE methods were assessed. This article concludes with a discussion of challenges and future trends of SB-FPSE.

show abstract

Section: B) Multi-view Methodsmentioning

confidence: 99%

A Systematic Review of Sensor-Based Methodologies for Food Portion Size Estimation

Raju

Sazonov

2021

IEEE Sensors J.

View full text Add to dashboard Cite

show abstract

“…Multi-view reconstruction approaches [24], [25] largely rely on on traditional computer vision techniques. [26] estimated the contour of the food from three different views and matched it with a predefined library to estimate the volume. However, their library used only nine types of food with over-simplified shapes.…”

Section: 𝐪𝐪 𝐑𝐑mentioning

confidence: 99%

Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images

Han

Pavlović

2020

Preprint

View full text Add to dashboard Cite

Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems. Although these systems may recognize the ingredients, a detailed analysis of their amounts in the meal, which is paramount for estimating the correct nutrition, is usually ignored. In this paper, we study the novel and challenging problem of predicting the relative amount of each ingredient from a food image. We propose PITA, the Picture-to-Amount deep learning architecture to solve the problem. More specifically, we predict the ingredient amounts using a domaindriven Wasserstein loss from image-to-recipe cross-modal embeddings learned to align the two views of food data. Experiments on a dataset of recipes collected from the Internet show the model generates promising results and improves the baselines on this challenging task. A demo of our system and our data is available at: foodai.cs.rutgers.edu.

show abstract

“…Even a more recent work [7] using two views uses only traditional computer vision techniques. [22] uses a simple method, mapping contour, but the performance might come from an easy dataset. Although [11] uses deep learning in their work, it's only for object detection and they use a simple method to calculate volume.…”

Section: Related Workmentioning

confidence: 99%

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

Pavlović

2019

Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management

View full text Add to dashboard Cite

In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two predictionbased models using deep learning that output sparse and dense predictions, coupled with important semi-automatic multi-database integrative data pre-processing, to solve the problem. Experiments on a dataset of recipes collected from the Internet show the models generate encouraging experimental results. CCS CONCEPTS• Computing methodologies → Neural networks.

show abstract

Multi-view Model Contour Matching Based Food Volume Estimation

Cited by 5 publications

References 1 publication

A Systematic Review of Sensor-Based Methodologies for Food Portion Size Estimation

A Systematic Review of Sensor-Based Methodologies for Food Portion Size Estimation

Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

Contact Info

Product

Resources

About