CTADA: The design of a crowdsourcing tool for online food image identification and segmentation

QR Code

CTADA: The design of a crowdsourcing tool for online food image identification and segmentation

© 2018 IEEE. Measuring accurate dietary intake, the process of determining what someone eats during the course of the day is considered to be an open research problem in the nutrition and health fields. We have developed image-based tools to automatically obtain accurate estimates of what foods and...

Full description

Bibliographic Details
Main Authors:	Fang, S., Liu, C., Tahboub, K., Zhu, F., Delp, E., Boushey, Carol
Format:	Conference Paper
Published:	2018
Online Access:	http://hdl.handle.net/20.500.11937/73377

Similar Items

Food image analysis: Segmentation, identification and weight estimation
by: He, Y., et al.
Published: (2013)

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)

Snakes assisted food image segmentation
by: He, Y., et al.
Published: (2012)

The use of co-occurrence patterns in single image based food portion estimation
by: Fang, S., et al.
Published: (2018)

Multiple hypotheses image segmentation and classification with application to dietary assessment
by: Zhu, F., et al.
Published: (2015)

Segmentation assisted food classification for dietary assessment
by: Zhu, F., et al.
Published: (2011)

Image segmentation for image-based dietary assessment: A comparative study
by: He, Y., et al.
Published: (2013)

A comparison of food portion size estimation using geometric models and depth images
by: Fang, S., et al.
Published: (2016)

Food image analysis: The big data problem you can eat!
by: Wang, Y., et al.
Published: (2017)

Single-View Food Portion Estimation Based on Geometric Models
by: Fang, S., et al.
Published: (2016)

The use of temporal information in food image analysis
by: Wang, Y., et al.
Published: (2015)

Context based food image analysis
by: He, Y., et al.
Published: (2013)

Analysis of food images: Features and classification
by: He, Y., et al.
Published: (2014)

Image-based food volume estimation
by: Xu, C., et al.
Published: (2013)

A printer indexing system for color calibration with applications in dietary assessment
by: Fang, S., et al.
Published: (2015)

Mobile image based color correction using deblurring
by: Wang, Y., et al.
Published: (2015)

Context based image analysis with application in dietary assessment and evaluation
by: Wang, Y., et al.
Published: (2017)

Volume estimation using food specific shape templates in mobile image-based dietary assessment
by: Chae, J., et al.
Published: (2011)

Image enhancement and quality measures for dietary assessment using mobile devices
by: Xu, C., et al.
Published: (2012)

Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks
by: Fang, S., et al.
Published: (2018)

Merging dietary assessment with the adolescent lifestyle
by: Schap, T., et al.
Published: (2014)

Specular highlight removal for image-based dietary assessment
by: He, Y., et al.
Published: (2012)

New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods
by: Boushey, C., et al.
Published: (2017)

Crowdsourcing an emotional wardrobe
by: Hughes, L., et al.
Published: (2012)

Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information
by: See, Linda, et al.
Published: (2016)

A method to determine the density of foods using X-ray imaging
by: Kelkar, S., et al.
Published: (2015)

Model-based food volume estimation using 3D pose
by: Xu, C., et al.
Published: (2013)

Crowdsource requirements engineering: Using online reviews as input to software features clustering
by: Bakar, Noor Hasrina, et al.
Published: (2017)

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)

Enhanced Se-Resnet 101 For Food Image Segmentation
by: Abuowaida, Suhaila Farhan Ahmad
Published: (2022)

Technology-based dietary assessment in youth with and without developmental disabilities
by: Polfuss, M., et al.
Published: (2018)

Crowdsourcing as Content Collaboration for STEM Edutainment
by: Azwin Arif, Abdul Rahim, et al.
Published: (2021)

Crowdsourcing stem content for educational gamification
by: Azwin Arif, Abdul Rahim, et al.
Published: (2018)

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)

Integrated database system for mobile dietary assessment and analysis
by: Bosch, M., et al.
Published: (2011)

i-Diary: A crowdsource-based spatio-Temporal multimedia enhanced points of interest authoring tool
by: Ahmad, Akhlaq, et al.
Published: (2015)

Characterizing early adolescent plate waste using the mobile food record
by: Panizza, C., et al.
Published: (2017)

A mobile food record for integrated dietary assessment
by: Ahmad, Ziad, et al.
Published: (2016)

A New Texture Feature for Improved Food Recognition Accuracy in a Mobile Phone Based Dietary Assessment System
by: Hafizur, Rahman, et al.
Published: (2012)

Cannot write session to /tmp/vufind_sessions/sess_4uhr0q3ur0sibqmef9j909ngc4