Body Scanning App: Data-Driven 3D Reconstruction and Applications

Ballester, Alfredo; Parrilla, Eduardo; Vivas, Julio Antonio Vivas; Pierola, Ana; Uriel, Jordi; Puigcerver, Sergio Antonio; Piqueras, Paola; Solves-Camallonga, Clara; Rodrı́guez, Marisol; González, Juan Carlos; Alemany, Sandra

doi:10.15221/15.184

Cited by 11 publications

(10 citation statements)

References 10 publications

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“…As a result, size matching in children should be based on their anthropometric measurements and their relationship with the garment measurements. Note that nowadays customers can obtain their detailed body sizes using their own digital cameras or other measuring technologies (Cordier et al, 2003;Ballester et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

An ensemble of ordered logistic regression and random forest for child garment size matching

Pierola

Epifanio

Alemany

2016

Computers & Industrial Engineering

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Size fitting is a significant problem for online garment shops. The return rates due to size misfit are very high. We propose an ensemble (with an original and novel definition of the weights) of ordered logistic regression and random forest (RF) for solving the size matching problem, where ordinal data should be classified. These two classifiers are good candidates for combined use due to their complementary characteristics. A multivariate response (an ordered factor and a numeric value assessing the fit) was considered with a conditional random forest. A fit assessment study was carried out with 113 children. They were measured using a 3D body scanner to obtain their anthropometric measurements. Children tested different garments of different sizes, and their fit was assessed by an expert. Promising results have been achieved with our methodology. Two new measures have been introduced based on RF with multivariate responses to gain a better understanding of the data. One of them is an intervention in prediction measure defined locally and globally. It is shown that it is a good alternative to variable importance measures and it can be used for new observations and with multivariate responses. The other proposed tool informs us about the typicality of a case and allows us to determine archetypical observations in each class.Keywords: Multivariate conditional random forest; Proportional odds logistic regression; Supervised learning; Ordinal classification; Childrenswear garment fitting; Variable importance ACKNOWLEDGEMENTS This work has been partially supported by Grants DPI2013-47279-C2-1-R and DPI2013-47279-C2-2-R.An ensemble of ordered logistic regression and random forest for child garment size matchingSize fitting is a significant problem for online garment shops. The return rates due to size misfit are very high. We propose an ensemble (with an original and novel definition of the weights) of ordered logistic regression and random forest (RF) for solving the size matching problem, where ordinal data should be classified. These two classifiers are good candidates for combined use due to their complementary characteristics. A multivariate response (an ordered factor and a numeric value assessing the fit) was considered with a conditional random forest. A fit assessment study was carried out with 113 children. They were measured using a 3D body scanner to obtain their anthropometric measurements. Children tested different garments of different sizes, and their fit was assessed by an expert. Promising results have been achieved with our methodology. Two new measures have been introduced based on RF with multivariate responses to gain a better understanding of the data. One of them is an intervention in prediction measure defined locally and globally. It is shown that it is a good alternative to variable importance measures and it can be used for new observations and with multivariate responses. The other proposed tool informs us about the typicality of a case and allows us to determine archetypical obs...

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Section: Introductionmentioning

confidence: 99%

An ensemble of ordered logistic regression and random forest for child garment size matching

Pierola

Epifanio

Alemany

2016

Computers & Industrial Engineering

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“…At one of the stages of 3D shoe model reconstruction that our competitors [7], [8] use the process of defining the contour of the foot is performed. Our method, on the other hand, has its own peculiarities.…”

Section: Recognition Of the Closed Outline Of The Foot And Shinmentioning

confidence: 99%

FITTIN - Online 3D Shoe Try-on

Revkov¹,

Kanin²

2020

Proceedings of 3DBODY.TECH 2020 - 11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

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Description for an easy and user-friendly computerized method for the online try-on of shoes that dynamically references a three-dimensional topological model of a foot with a three-dimensional topological model of the inner surface of footwear. A comfort level is then calculated based on the matching results. With the help of these calculations, a consumer can make a conclusion about the suitability of the footwear and make a purchase decision. The topological 3D model of a foot is reconstructed by analyzing a series of foot images taken with a mobile device (smartphone or digital camera). In addition to geometric information (such as shape and size), the topological model of the footwear's inner surface also contains information about pressure forces and tensile stiffness at different points. The present article gives a critical analysis of existing virtual shoe fitting methods.

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“…That way, the body measuring instrument could be available at any place with internet access. Our approach is based on data-driven methods for 3D reconstruction [11,12]. For children that cannot stand or follow instructions (until 3 years-old) our method uses a Partial Least Square (PLS) regression to obtain 19 body dimensions from age, gender, height and weight reported by parents.…”

Section: Kidsize App -A New Body Measuring Instrumentmentioning

confidence: 99%

Kidsize: Always Get the Right Size!

Ballester¹,

Pierola²,

Parrilla³

et al. 2016

Proceedings of the 7th International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 30 Nov.-1 Dec. 2016

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This paper describes the two innovations underpinning Kidsize concept and presents the results of their validation. The first one is a mobile phone app to measure a child in 3D by taking two pictures. This new method is more accurate and consistent than an untrained person using a measuring tape at home or in the shop. The second one is an expert system that recommends the size that best fits the child and assesses the fit of the garment at different body areas. Project results show that it can provide nearly 90% right size recommendations, thus outperforming existing methods like age-or height-based size guides, which achieve 40 and 60% right recommendations respectively.

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Body Scanning App: Data-Driven 3D Reconstruction and Applications

Cited by 11 publications

References 10 publications

An ensemble of ordered logistic regression and random forest for child garment size matching

An ensemble of ordered logistic regression and random forest for child garment size matching

FITTIN - Online 3D Shoe Try-on

Kidsize: Always Get the Right Size!

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