Girth Measurement Based on Multi-View Stereo Images for Garment Design

Abstract

In this paper, we propose a novel girth measurement system based on multi-view stereo images for garment design. Our system is set in a fixed location to capture three pairs of stereo images for the subject by six calibrated and synchronously triggered cameras. An important feature of this system is the use of an optimized semantic segmentation network that can efficiently segment the girth region in the captured six-view stereo images. Another important feature of this system is the use of color subspace classification and coordinate clustering that can effectively constrain the stereo matching within the scope of markers. Then, the system performs only on the corresponding clusters to extract stereo matching point pairs of markers correctly. The space coordinates of 3D point corresponding to each stereo matching point pair are calculated in each coordinate system of stereo cameras. The unified coordinates of these 3D markers are transformed from three different coordinate systems into one unified coordinate system. Girth is measured by curve fitting of these markers and calculating the length of the fitting curve. Our proposed system performs passive and intelligent girth measurement in garment design, and overcomes the problem of too many invalid stereo matching point pairs in girth measurement. Experimental results demonstrate its accuracy. Our system has a maximum bust measurement error of 1.28cm for woman and 1.31cm for man and a maximum waist measurement error of 1.18cm for woman and 0.99cm for man, which are within the error limit regulated by China national standards GB/

An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System

“…The process of the stereovision-based anthropometry can be divided into three steps: semantic segmentation of the girth region; stereo matching and coordinate calculation; and girth fitting [ 17 , 18 ]. The flowchart is shown in Figure 1 .…”

“…In the anthropometric system in reference [ 17 , 18 ], color markers are used for stereo matching, and the matching point pair closest to the center of the marker is reserved for spatial coordinate calculation. In the anthropometric system in this paper, corners are used for stereo matching.…”

“…The anthropometric methods in [ 17 , 18 ] make a trade-off between the accuracy and complexity of the aforementioned two types of anthropometric methods with optical cameras. In reference [ 17 ], three pairs of synchronously triggered stereo cameras were adopted to collect three pairs of stereo images from the front, side and back of a subject.…”

“…The anthropometric methods in [ 17 , 18 ] make a trade-off between the accuracy and complexity of the aforementioned two types of anthropometric methods with optical cameras. In reference [ 17 ], three pairs of synchronously triggered stereo cameras were adopted to collect three pairs of stereo images from the front, side and back of a subject. In reference [ 18 ], one pair of stereo cameras and a turntable were used to acquire four pairs of stereo images of a subject from four different views with partially overlapping areas.…”

“…The matching precision is refined to the sub-pixel level by the checkerboard corner design and the corresponding corner detection algorithm, and the matching complexity is reduced by the regional constraint of the checkerboard corner. The proposed algorithm and design can significantly improve the accuracy of the anthropometric system in [ 17 , 18 ].…”