Limits...
Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping.

Probst Y, Nguyen DT, Tran MK, Li W - Nutrients (2015)

Bottom Line: This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition.Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images.Technical details are provided together with discussions on the issues and future work.

View Article: PubMed Central - PubMed

Affiliation: School of Medicine, University of Wollongong, Wollongong, NSW 2522, Australia. yasmine@uow.edu.au.

ABSTRACT
Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.

No MeSH data available.


Related in: MedlinePlus

An example of a food image containing five food categories: cheese, tomato, oranges, beans, and carrots.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4555113&req=5

nutrients-07-05274-f002: An example of a food image containing five food categories: cheese, tomato, oranges, beans, and carrots.

Mentions: The food image classification method was evaluated on a newly created dataset [19,20,21]. Table 2 summarises the dataset used in the evaluation. Note that in this dataset, one food image may contain more than one food category (see Figure 2). The dataset was organised so that the training sets (used during codebook creation) and test sets were separated.


Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping.

Probst Y, Nguyen DT, Tran MK, Li W - Nutrients (2015)

An example of a food image containing five food categories: cheese, tomato, oranges, beans, and carrots.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555113&req=5

nutrients-07-05274-f002: An example of a food image containing five food categories: cheese, tomato, oranges, beans, and carrots.
Mentions: The food image classification method was evaluated on a newly created dataset [19,20,21]. Table 2 summarises the dataset used in the evaluation. Note that in this dataset, one food image may contain more than one food category (see Figure 2). The dataset was organised so that the training sets (used during codebook creation) and test sets were separated.

Bottom Line: This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition.Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images.Technical details are provided together with discussions on the issues and future work.

View Article: PubMed Central - PubMed

Affiliation: School of Medicine, University of Wollongong, Wollongong, NSW 2522, Australia. yasmine@uow.edu.au.

ABSTRACT
Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.

No MeSH data available.


Related in: MedlinePlus