Obesity is a significant factor in health information and increases the risk of health problems. Hence, an application that can help users to monitor their body mass index (BMI) timelines is needed. The simulation of a personalized 3D body shape may encourage women to control their BMI for a healthy body and pleasant appearance. Therefore, this paper aims to develop computerized 3D models of female shapes for various weights and heights, and consists of three important parts. First, the preparation of six avatars is described. Second, the body proportions of 6767 female datasets are analyzed to find the relationship of variables in various weights and heights. Last, 3D morphing of different female shapes is developed and analyzed experimentally for appropriate morphing parameters. Accuracy tests are carried out in three ways. First, body proportions calculated using the Z-Size Ladies application, called “Z-Size calculations,” are compared with the body proportions of data obtained from 3D scanners. Second, the Z-Size calculations are compared with tape measurements. Last, the Z-Size calculations are compared with measurements of Z-Size 3D morph models. The results of accuracy tests are shown as the relationship graphs between the BMI and body proportion measurements of chest, waist, hip, and inseam. Bland–Altman plots and Pearson correlation calculation show high correlation. In conclusion, the data obtained from the Z-Size calculations, 3D Scanner, tape measurements, and Z-Size morph models’ measurements are in good agreement and are highly correlated. The simulation of 3D female shapes for different weights and heights as proposed shows good performance and high accuracy.
The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with partially known support (OMP-PKS) is applied to each signal in the ensemble to reconstruct L noisy outputs. The L noisy outputs are then averaged for denoising. The proposed method in this article is designed for CS reconstruction of image signal. The performance of our proposed method was compared with basis pursuit denoising, Lorentzian-based iterative hard thresholding, OMP-PKS and distributed compressed sensing using simultaneously orthogonal matching pursuit. The experimental results of 42 standard test images showed that our proposed method yielded higher peak signal-to-noise ratio at low measurement rate and better visual quality in all cases.
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