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Learning Based Food Image Analysis - Detection, Recognition and Segmentation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning Based Food Image Analysis - Detection, Recognition and Segmentation./
Author:	Mao, Runyu.
Description:	1 online resource (138 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
Contained By:	Dissertations Abstracts International84-12A.
Subject:	Food. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30501527click for full text (PQDT)
ISBN:	9798379664268

Learning Based Food Image Analysis - Detection, Recognition and Segmentation.
Mao, Runyu.

Learning Based Food Image Analysis - Detection, Recognition and Segmentation. - 1 online resource (138 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: A.

Thesis (Ph.D.)--Purdue University, 2023.

Includes bibliographical references

Advances of mobile and wearable technologies have enabled a wide range of new methods for dietary assessment and monitoring, such as active or passive capturing of food images of an eating scene. Compared to traditional methods, these approaches are less burdensome and can reduce biased measurements. Food image analysis, consists of food region detection, food category classification, and food segmentation, can benefit subsequent nutrient analysis. However, due to the different complexity levels of food images and inter-class similarity of food categories, it is challenging for an image-based food analysis system to achieve high performance outside of a lab setting.In this thesis, we investigate four research topics related to image-based dietary assessment: (1) construction of the VIPER-FoodNet dataset, (2) food recognition, (3) nutrient integrated hierarchy food classification, and (4) weakly supervised segmentation. For topic (1), we developed a learning-based method to automatically remove non-food images for dataset construction. For topic (2), we proposed a novel two-step food recognition system that consists of food localization and hierarchical food classification. For topic (3), we developed a cross-domain food classification framework that integrates nutrition information to help the classification system make better mistakes. Finally, for topic (4), a weaklysupervised segmentation system is developed which only requires image-level supervision during training.In addition, we developed a high-quality Photoplethysmography (PPG) signal selection method for a wearable device when subjects are undergoing daily life activities, which could be used to inform the health status of the individual.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379664268Subjects--Topical Terms:

551593
Food.
Index Terms--Genre/Form:

542853
Electronic books.

Learning Based Food Image Analysis - Detection, Recognition and Segmentation.
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245 1 0 $a Learning Based Food Image Analysis - Detection, Recognition and Segmentation.
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300 $a 1 online resource (138 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
500 $a Advisor: Zhu, Fengqing M.
502 $a Thesis (Ph.D.)--Purdue University, 2023.
504 $a Includes bibliographical references
520 $a Advances of mobile and wearable technologies have enabled a wide range of new methods for dietary assessment and monitoring, such as active or passive capturing of food images of an eating scene. Compared to traditional methods, these approaches are less burdensome and can reduce biased measurements. Food image analysis, consists of food region detection, food category classification, and food segmentation, can benefit subsequent nutrient analysis. However, due to the different complexity levels of food images and inter-class similarity of food categories, it is challenging for an image-based food analysis system to achieve high performance outside of a lab setting.In this thesis, we investigate four research topics related to image-based dietary assessment: (1) construction of the VIPER-FoodNet dataset, (2) food recognition, (3) nutrient integrated hierarchy food classification, and (4) weakly supervised segmentation. For topic (1), we developed a learning-based method to automatically remove non-food images for dataset construction. For topic (2), we proposed a novel two-step food recognition system that consists of food localization and hierarchical food classification. For topic (3), we developed a cross-domain food classification framework that integrates nutrition information to help the classification system make better mistakes. Finally, for topic (4), a weaklysupervised segmentation system is developed which only requires image-level supervision during training.In addition, we developed a high-quality Photoplethysmography (PPG) signal selection method for a wearable device when subjects are undergoing daily life activities, which could be used to inform the health status of the individual.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
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650 4 $a Carbohydrates. $3 522687
650 4 $a Activities of daily living. $3 3564186
650 4 $a Nutrition. $3 517777
650 4 $a Localization. $3 3560711
650 4 $a Respiration. $3 610890
650 4 $a Semantics. $3 520060
650 4 $a Heart rate. $3 3563099
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650 4 $a Physiology. $3 518431
650 4 $a Public health. $3 534748
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773 0 $t Dissertations Abstracts International $g 84-12A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30501527 $z click for full text (PQDT)