Non-contact clothing anthropometry based on two-dimensional image contour detection and feature point recognition

Abstract

Developing the technology of estimating human body size from two-dimensional images is the key to realising more digitalization and artificial intelligence in the textile and garment industry. Therefore, this paper is an in-depth study of estimating body sizes from two-dimensional images in a self-collected database of human body samples. First, the artificial thresholds in the Canny edge operator were replaced by adaptive thresholds. The improved Canny edge operator was combined with mathematical morphology so that it could detect a clear and complete single human contour. Then a joint point detection algorithm based on a convolution neural network and human proportion is proposed. It can detect human feature points with different body proportions. Finally, front and side images and manual body measurements of 122 males aged 18–22 years were collected as the human sample database, calculating the length and fit of the girth size. Compared with manual body measurement data, the error of human length and girth size parameters within the national standard range of –1.5 ~ 1.5 cm can reach 91% on average. This study provides an accurate and convenient anthropometric method for digital garment engineering, which can be used for online shopping and garment customization, and has a certain practical value.