@Article{JoanSerrat2018,
author="Joan Serrat
and Felipe Lumbreras
and Idoia Ruiz",
title="Learning to measure for preshipment garment sizing",
journal="Measurement",
year="2018",
volume="130",
pages="327--339",
optkeywords="Apparel",
optkeywords="Computer vision",
optkeywords="Structured prediction",
optkeywords="Regression",
abstract="Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.",
optnote="ADAS; MSIAU; 600.122; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3128), last updated on Tue, 25 Jan 2022 09:29:13 +0100",
opturl="https://doi.org/10.1016/j.measurement.2018.08.019",
file=":http://refbase.cvc.uab.es/files/SLR2018.pdf:PDF"
}