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Abstract

The aim of this work is to develop a method to evaluate garment bagging by image processing with different modeling techniques. Garment bagging is a kind of three-dimen sional residual deformation during wear, which can be characterized by a few parameters such as bagging height, volume, shape, and anisotropy. Traditional methods are limited to evalu ating bagging appearance by single parameters such as height, which cannot represent the abundant information given by the appearance of a bagged fabric. In this paper, we develop a method to evaluate fabric bagging from captured images of bagged fabrics by image processing and abstracting the criteria to recognize bagging magnitude. Based on an analysis of the intensity images, eight criteria are extracted to characterize the image features including bagging height, volume, and shape, and fabric surface patterns on bagging appearance. The criteria are used as variables in three predictive models. The work shows that bagging appearance can be predicted by the criteria extracted from the images of bagged fabrics.

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Evaluating and Predicting Fabric Bagging with Image Processing

Abstract

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