%0 Journal Article
%T Learning to measure for preshipment garment sizing
%A Joan Serrat
%A Felipe Lumbreras
%A Idoia Ruiz
%J Measurement
%D 2018
%V 130
%F Joan Serrat2018
%O ADAS; MSIAU; 600.122; 600.118
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3128), last updated on Tue, 25 Jan 2022 09:29:13 +0100
%X 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.
%K Apparel
%K Computer vision
%K Structured prediction
%K Regression
%U https://doi.org/10.1016/j.measurement.2018.08.019
%U http://refbase.cvc.uab.es/files/SLR2018.pdf
%P 327-339