A Survey on Food Computing

Min, Weiqing; Jiang, Shuqiang; Liu, Linhu; Rui, Yong; Jain, Ramesh

doi:10.1145/3329168

Cited by 243 publications

(150 citation statements)

References 240 publications

(299 reference statements)

Supporting

Mentioning

134

Contrasting

Order By: Relevance

“…They applied various classifiers (Artificial Neural Network (ANN), Fourier-descriptor (FD), …) for some types of fruit/vegetable listed in Table A1 of Appendix A . In addition, there is a superior survey that delivers not only a summary of many different methods, tasks, and concepts but also the key challenges and future directions for food computing [ 11 ]. Typically, the mass of the product will be measured using a load cell incorporated with a conveyor belt such as C35 AdvancedLine WD Checkweigher by Mettler Toledo, which can measure weight from 25 g to 7000 g with throughput up to 250 packets per minute or 2000 g Checkweigher from A&D Inspection has throughput up to 320 packets per minute or digital scale for individuals or businesses [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Size and Mass Estimation of Slender Axi-Symmetric Fruit/Vegetable Using a Single Top View Image

Huynh

Tran

Dao

2020

Sensors

View full text Add to dashboard Cite

Among the physical attributes of agricultural materials, mass, volume, and sizes have always been important quality parameters. Previous research focused mostly on volume estimation using stereo-based approaches, which rely on manual intervention or require a multiple-cameras set up or multiple-frames captures from different viewing angles to reconstruct the three-dimensional point-cloud information. These approaches are tedious and not suitable for practical machine vision systems. In this work, we only use a single camera mounted on the ceiling of the imaging chamber, which is directly above the fruit/vegetable to capture its top-view, two-dimensional image. We developed a method to estimate the mass/volume of agricultural products with axi-symmetrical shapes such as a carrot or a cucumber. The mass/volume is estimated as the sum of smaller standard blocks, such as chopped pyramids, an elliptical cone, or a conical cone. The computed mass/volume showed good agreement with analytical and experimental results. The weight estimation error is 95% for the case of the carrot and 96.7% for the cucumber. The method proved to be sufficiently accurate, easy to use, and rotationally invariant.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Size and Mass Estimation of Slender Axi-Symmetric Fruit/Vegetable Using a Single Top View Image

Huynh

Tran

Dao

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Consequently, this survey is different from previous attempts of reviewing the existing works (see next section 1.2 for more detail), which usually focus on one aspect of food-related technology only. Min et al (2019) proposed a survey on Food Computing, defined as an interdisciplinary field addressing food-related studies via computer science. In their view, Food Computing applies computational approaches for acquiring and analyzing heterogeneous food data from various sources for perception, recognition, retrieval, recommendation and monitoring of food to address food related issues in health, biology, gastronomy, and agronomy.…”

Section: Selection Of Sourcesmentioning

confidence: 99%

Computational Commensality: From Theories to Computational Models for Social Food Preparation and Consumption in HCI

et al. 2019

View full text Add to dashboard Cite

Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as "commensality," positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of "Computational Commensality," that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food-and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieving Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective.

show abstract

“…Moreover, as noted for the previous workshops, there is a growing interest in the context of HFI to capitalize on multisensory interactions to enhance our food-and drink-related experiences [5][6][7]. This should not, perhaps, come as a surprise, given that flavor, for example, is the product of the integration of, at least, gustatory and (retronasal) olfactory perception, and can be influenced by all our senses [8].…”

Section: Introductionmentioning

confidence: 96%

Multisensory Approaches to Human-Food Interaction

Velasco

Nijholt

Spence

et al. 2020

Proceedings of the 2020 International Conference on Multimodal Interaction

View full text Add to dashboard Cite

Here, we present the outcome of the 4 th workshop on Multisensory Approaches to Human-Food Interaction (MHFI), developed in collaboration with ICMI 2020 in Utrecht, The Netherlands. Capitalizing on the increasing interest on multisensory aspects of human-food interaction and the unique contribution that our community offers, we developed a space to discuss ideas ranging from mechanisms of multisensory food perception, through multisensory technologies, to new applications of systems in the context of MHFI. All in all, the workshop involved 11 contributions, which will hopefully further help shape the basis of a field of inquiry that grows as we see progress in our understanding of the senses and the development of new technologies in the context of food.

show abstract

A Survey on Food Computing

Cited by 243 publications

References 240 publications

Real-Time Size and Mass Estimation of Slender Axi-Symmetric Fruit/Vegetable Using a Single Top View Image

Real-Time Size and Mass Estimation of Slender Axi-Symmetric Fruit/Vegetable Using a Single Top View Image

Computational Commensality: From Theories to Computational Models for Social Food Preparation and Consumption in HCI

Multisensory Approaches to Human-Food Interaction

Contact Info

Product

Resources

About