In this paper, a system with six depth cameras was built to scan both feet simultaneously. An improved calibration method based on a T-shaped checkerboard was used to calculate the extrinsic parameters of the cameras. T-shaped virtual checkerboards were introduced to further fine-tune the accuracy of calibration based on the iterative closest point algorithm. Based on the proposed foot scanner, a complete procedure was introduced to measure the foot automatically by locating the anatomical landmarks without manual intervention. Various experiments were presented to validate the performance of the scanner and the measurements. The results verified that the proposed methods were efficient and versatile for three-dimensional foot scanning and measurement.

Purpose -This paper presents methods and algorithms to automatically segment and measure the human body. Design/methodology/approach -In the segmentation procedure, two different methods are designed to find the crotch point for the situation of non-contacted thigh and contacted thigh, respectively. Three different methods: minimum distance algorithm, minimum inclination angle algorithm, and directional neighbor identification algorithm are introduced to search the branching points or triangle. In the body measurement procedure, a pre-sorted circling method is designed for circumference measurement, and the basic principle of landmark acquisition has been discussed. These techniques are validated via testing over different type of scanned model. Findings -The results of automatic segmentation and body measurement have verified that our methods are efficient and versatile in processing different type of scanned body.Research limitations/implications -The accurate and automatic locating of wrist, ankle and knees contour can be more difficult than it appears to be. Practical implications -The main usage of scanned body in our research is for 3D garment try-on. Originality/value -This paper introduces the methods for crotch identification, and the methods including minimum distance algorithm, minimum inclination angle algorithm, and directional neighbor identification algorithm for human body segmentation. It also explains the fundamental measuring techniques, and outlines the results of using these techniques in segmentation and measurement.

Using compound microscopy is one of the major options for the identification of cashmere/wool. To interpret human perception via machine vision, microscopic images captured by a charge-coupled device camera were transferred into projection curves. Three different deciphering methods, recurrence quantification analysis, direct geometrical description, and discrete wavelet transform were employed to reveal the embedded numerical features. The extracted parameters were used to screen the supervised classification methods, including a neural network with multilayer perceptrons, kernel ridge regression/classification, and the support vector machine (SVM). The experimental results indicated that the proposed projection curves could be used as a mathematical replica in automatic cashmere/wool identification. The best accuracy came from a SVM-trained decision function with the parameters extracted from recurrence quantification analysis.

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Personalized customization is a new manufacturing trend in high-end products (e.g. senior custom clothing).Traditional apparel customization (made-to-measure & bespoken) highly depends on experienced tailors. A personalized mannequin is essential for apparel customization using CAD technologies. Currently, a common method of reconstructing mannequin is based on body measurements or body features. It only preserves the body size instead of preserving the accurate user's stature. However, the same human body measurement does not equal to the same body shape. This may result in an unfit garment for the user. This paper proposes a novel scanning-based pipeline to reconstruct a personalized mannequin, which preserves both body size and body stature information. We first capture the body of a user via 3D scanning, and a statistical body model is fit to the scanned data. This results in a skinned articulated model of the user. The scanned body is then adjusted to be pose-symmetric via linear blending skinning. The updated pose-symmetric body is then segmented to initialize the stature symmetry processing. Finally, a slice-based method is proposed to generate a symmetric 3D mannequin. The process of apparel customization can be easily digitalized with the help of the proposed mannequin reconstruction system and the corresponding existing clothing CAD software.

In this paper, a new method was proposed to establish the relationship between three-dimensional (3D) foot shapes and their two-dimensional (2D) foot silhouettes, through which a complete 3D foot shape can be predicted by simply inputting its two 2D silhouettes. 3D foot scans of 80 participants were randomly selected as the training set, and those of another 20 participants were used as the testing set. Elliptical Fourier analysis (EFA) and principle component analysis (PCA) were adopted to parameterize the 3D foot shapes. A linear regressive model was then developed to predict the 3D foot shape with the foot silhouettes. Experiment results indicated individual 3D foot shape can be predicted with a mean error between 1.21 and 1.27 mm, which can provide enough accuracy for the fit evaluation of footwear.

In this article, we presented a new automatic three-dimensional-scanned garment fitting method for A-Pose-scanned human models. Both the garment and the human body were decomposed based on feature lines defined by various landmarks. The patches of the three-dimensional garment were automatically positioned around the human model by setting up the correspondence via feature matching. Virtual sewing was engaged to obtain the final results of virtual dressing. The penetration between cloth model and human model was solved by a geometrical method constrained by Laplacian-based deformation. The experimental results indicated that the proposed method was an efficient way for redressing various garments onto various human models while maintaining the original geometrical features of garments.

Purpose The automatic body measurement is the key of tailoring, mass customization and fit/ease evaluation. The major challenges include finding the landmarks and extracting the sizes accurately. The purpose of this paper is to propose a new method of body measurement based on the loop structure. Design/methodology/approach The scanned human model is sliced equally to layers consist of various shapes of loops. The semantic feature analysis has been regarded as a problem of finding the points of interest (POI) and the loop of interest (LOI) according to the types of loop connections. Methods for determining the basic landmarks have been detailed. Findings The experimental results validate that the proposed methods can be used to locate the landmarks and to extract sizes on markless human scans robustly and efficiently. Originality/value With the method, the body measurement can be quickly performed with average errors around 0.5 cm. The results of segmentation, landmarking and body measurements also validate the robustness and efficiency of the proposed methods.