Food image analysis: The big data problem you can eat!
© 2016 IEEE.Six of the ten leading causes of death in the United States can be directly linked to diet. Measuring accurate dietary intake, the process of determining what someone eats is considered to be an open research problem in the nutrition and health fields. We are developing image-based tools...
| Main Authors: | Wang, Y., Fang, S., Liu, C., Zhu, F., Kerr, D., Boushey, Carol, Delp, E. |
|---|---|
| Format: | Conference Paper |
| Published: |
2017
|
| Online Access: | http://hdl.handle.net/20.500.11937/52407 |
Similar Items
The use of co-occurrence patterns in single image based food portion estimation
by: Fang, S., et al.
Published: (2018)
by: Fang, S., et al.
Published: (2018)
Efficient superpixel based segmentation for food image analysis
by: Wang, Y., et al.
Published: (2016)
by: Wang, Y., et al.
Published: (2016)
Weakly supervised food image segmentation using class activation maps
by: Wang, Y., et al.
Published: (2018)
by: Wang, Y., et al.
Published: (2018)
The use of temporal information in food image analysis
by: Wang, Y., et al.
Published: (2015)
by: Wang, Y., et al.
Published: (2015)
CTADA: The design of a crowdsourcing tool for online food image identification and segmentation
by: Fang, S., et al.
Published: (2018)
by: Fang, S., et al.
Published: (2018)
Association between cognitive restraint, uncontrolled eating, emotional eating and BMI and the amount of food wasted in early adolescent girls
by: Banna, J., et al.
Published: (2018)
by: Banna, J., et al.
Published: (2018)
A comparison of food portion size estimation using geometric models and depth images
by: Fang, S., et al.
Published: (2016)
by: Fang, S., et al.
Published: (2016)
Snakes assisted food image segmentation
by: He, Y., et al.
Published: (2012)
by: He, Y., et al.
Published: (2012)
Single-View Food Portion Estimation Based on Geometric Models
by: Fang, S., et al.
Published: (2016)
by: Fang, S., et al.
Published: (2016)
Context based food image analysis
by: He, Y., et al.
Published: (2013)
by: He, Y., et al.
Published: (2013)
Analysis of food images: Features and classification
by: He, Y., et al.
Published: (2014)
by: He, Y., et al.
Published: (2014)
Food image analysis: Segmentation, identification and weight estimation
by: He, Y., et al.
Published: (2013)
by: He, Y., et al.
Published: (2013)
Adolescents in the United States can identify familiar foods at the time of consumption and when prompted with an image 14 h postprandial, but poorly estimate portions
by: Schap, T., et al.
Published: (2011)
by: Schap, T., et al.
Published: (2011)
Image-based food volume estimation
by: Xu, C., et al.
Published: (2013)
by: Xu, C., et al.
Published: (2013)
Context based image analysis with application in dietary assessment and evaluation
by: Wang, Y., et al.
Published: (2017)
by: Wang, Y., et al.
Published: (2017)
Mobile image based color correction using deblurring
by: Wang, Y., et al.
Published: (2015)
by: Wang, Y., et al.
Published: (2015)
Neyi yiyorsan osun sen: Helâl Gıda (You are what you are eating: Halal Food)
by: Kayadibi, Saim
Published: (2011)
by: Kayadibi, Saim
Published: (2011)
Feasibility and use of the mobile food record for capturing eating occasions among children ages 3–10 years in Guam
by: Aflague, T., et al.
Published: (2015)
by: Aflague, T., et al.
Published: (2015)
Watch what you eat
by: Chandran, Sheela
Published: (2013)
by: Chandran, Sheela
Published: (2013)
Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks
by: Fang, S., et al.
Published: (2018)
by: Fang, S., et al.
Published: (2018)
Image segmentation for image-based dietary assessment: A comparative study
by: He, Y., et al.
Published: (2013)
by: He, Y., et al.
Published: (2013)
You decide whether to eat or not to clarify the authenticity of radiation can Japanese seafood still be eaten
by: Sin Chew Daily
Published: (2023)
by: Sin Chew Daily
Published: (2023)
A printer indexing system for color calibration with applications in dietary assessment
by: Fang, S., et al.
Published: (2015)
by: Fang, S., et al.
Published: (2015)
New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods
by: Boushey, C., et al.
Published: (2017)
by: Boushey, C., et al.
Published: (2017)
Specular highlight removal for image-based dietary assessment
by: He, Y., et al.
Published: (2012)
by: He, Y., et al.
Published: (2012)
‘Can you have your cake and eat it?’
A small-scale exploratory study of the influence of neoliberalism in managerial practices at private LTOs in China
by: Liu, Siwei
Published: (2020)
by: Liu, Siwei
Published: (2020)
Volume estimation using food specific shape templates in mobile image-based dietary assessment
by: Chae, J., et al.
Published: (2011)
by: Chae, J., et al.
Published: (2011)
A New Texture Feature for Improved Food Recognition Accuracy in a Mobile Phone Based Dietary Assessment System
by: Hafizur, Rahman, et al.
Published: (2012)
by: Hafizur, Rahman, et al.
Published: (2012)
You, your children, your grandchildren, and their inflammatory responses are what you eat
by: De Bittencourt, P., et al.
Published: (2015)
by: De Bittencourt, P., et al.
Published: (2015)
You can have your cake and eat it too: Embracing paradox of safety as source of progress in safety science
by: Hu, X., et al.
Published: (2020)
by: Hu, X., et al.
Published: (2020)
Multiple hypotheses image segmentation and classification with application to dietary assessment
by: Zhu, F., et al.
Published: (2015)
by: Zhu, F., et al.
Published: (2015)
Image enhancement and quality measures for dietary assessment using mobile devices
by: Xu, C., et al.
Published: (2012)
by: Xu, C., et al.
Published: (2012)
Perception v. actual intakes of junk food and sugar-sweetened beverages in Australian young adults: assessed using the mobile food record
by: Harray, A., et al.
Published: (2017)
by: Harray, A., et al.
Published: (2017)
Evaluation of a web-based program promoting healthy eating and physical activity for adolescents: Teen Choice: Food and Fitness
by: Cullen, K., et al.
Published: (2013)
by: Cullen, K., et al.
Published: (2013)
A method to determine the density of foods using X-ray imaging
by: Kelkar, S., et al.
Published: (2015)
by: Kelkar, S., et al.
Published: (2015)
Model-based food volume estimation using 3D pose
by: Xu, C., et al.
Published: (2013)
by: Xu, C., et al.
Published: (2013)
Segmentation assisted food classification for dietary assessment
by: Zhu, F., et al.
Published: (2011)
by: Zhu, F., et al.
Published: (2011)
Feasibility of assessing diet with a mobile food record for adolescents and young adults with Down syndrome
by: Bathgate, Katherine, et al.
Published: (2017)
by: Bathgate, Katherine, et al.
Published: (2017)
Characterizing early adolescent plate waste using the mobile food record
by: Panizza, C., et al.
Published: (2017)
by: Panizza, C., et al.
Published: (2017)
How can Retailers use Big Data to improve Customer Experience?
by: Apserou, Ioanna
Published: (2013)
by: Apserou, Ioanna
Published: (2013)
Similar Items
-
The use of co-occurrence patterns in single image based food portion estimation
by: Fang, S., et al.
Published: (2018) -
Efficient superpixel based segmentation for food image analysis
by: Wang, Y., et al.
Published: (2016) -
Weakly supervised food image segmentation using class activation maps
by: Wang, Y., et al.
Published: (2018) -
The use of temporal information in food image analysis
by: Wang, Y., et al.
Published: (2015) -
CTADA: The design of a crowdsourcing tool for online food image identification and segmentation
by: Fang, S., et al.
Published: (2018)