Anthropometric Landmarks Extraction and Dimensions Measurement Based on ResNet

Wang, Xun; Liu, Baohua; Dong, Yukun; Pang, Shanchen; Tao, Xixi

doi:10.3390/sym12121997

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…ResNet. Deep residual network has been widely used due to its remarkable parameter optimization capability [30][31] . The deep residual network is composed of residual units, which are divided into 2-layer residual units and 3-layer residual units, as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Identification and Area Calculation of the Egg Mass of Spodoptera Frugiperda Based on Faster-RCNN

Liang

Peng

Sheikh

et al. 2021

Preprint

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Spodoptera frugiperda is a migratory and destructive crop pest. The number of eggs is an important method to evaluate the pest situation, which can be estimated by the area of egg mass. The traditional manual method is inefficient, but the new method of egg mass image recognition improves the efficiency of eggs number estimation. In this paper, the optimized Faster-RCNN target detection algorithm was used to recognize the egg mass image of Spodoptera frugiperda. The Maximum Between-Class Variance method (Otsu) was used for threshold segmentation to obtain the position, shape and size of the egg mass and calculate the area of the egg mass. The mean value of the relative error of the egg mass area in the test samples was -0.02032, and the minimum value was -0.00047. The experimental results show that the egg area calculation method proposed in this paper is fast and accurate, which can meet the requirements of egg area measurement.

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Section: Methodsmentioning

confidence: 99%

Identification and Area Calculation of the Egg Mass of Spodoptera Frugiperda Based on Faster-RCNN

Liang

Peng

Sheikh

et al. 2021

Preprint

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“…An HRTF filter can also be computed using deep neural networks (DNNs) with anthropometric measurements and ear images [22] or sound source directions [23] as input. DNN can also be used to detect an ear in an image and automatically label the points that define its shape [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Improving Monocular Camera Localization for Video-Based Three-Dimensional Outer Ear Reconstruction Tasks

et al. 2023

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This work addresses challenges related to camera 3D localization while reconstructing a 3D model of an ear. This work explores the potential solution of using a cap, specifically designed not to obstruct the ear, and its efficiency in enhancing the camera localization for structure-from-motion (SfM)-based object reconstruction. The proposed solution is described, and an elaboration of the experimental scenarios used to investigate the background textures is provided; data collection and software tools used in the research are reported. The results show that the proposed method is effective, and using the cap with texture leads to a reduction in the camera localization error. Errors in the 3D location reconstruction of the camera were calculated by comparing cameras localized within typical ear reconstruction situations to those of higher-accuracy reconstructions. The findings also show that caps with sparse dot patterns and a regular knitted patterned winter hat are the preferred patterns. The study provides a contribution to the field of 3D modeling, particularly in the context of creating 3D models of the human ear, and offers a step towards more accurate, reliable, and feasible 3D ear modeling and reconstruction.

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“…We have studied several ways to evaluate elliptic fitting to find the most accurate way to perform efficient human upper body measurements, leading us to compare the following three equations: Equation (3) was proposed by Xun Wang et al [7] who developed an approach that can estimate anthropometric dimensions based on 2D images by extracting landmarks through a convolutional neural network and by building a general multi-ellipse model in which body shape information is added to obtain more accurate results. Their network is trained by front and side views marked with 14 landmarks, together with a heatmap generated by a Gaussian template.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Body Circumference Measurement Technique using Ellipse Formula

MONTAZERIAN¹,

LEYMARIE²

2022

Proceedings of 3DBODY.TECH 2022 - 13th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

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In the present paper, ellipse-like approximations are considered with the aim of minimizing the difference between the results of direct and software measurements. The human body can only be approximately represented by elliptic cross sections, and these can vary for each individual. We show that better results are obtained by adapting the fit to the body shape based on a simple criterion. Based on this study, we have selected two mathematical models to estimate upper human body circumferences.The result is a fully trained system that can choose the best ellipse equation according to the human body shape to calculate human body circumferences.

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Anthropometric Landmarks Extraction and Dimensions Measurement Based on ResNet

Cited by 8 publications

References 23 publications

Identification and Area Calculation of the Egg Mass of Spodoptera Frugiperda Based on Faster-RCNN

Identification and Area Calculation of the Egg Mass of Spodoptera Frugiperda Based on Faster-RCNN

Improving Monocular Camera Localization for Video-Based Three-Dimensional Outer Ear Reconstruction Tasks

Adaptive Body Circumference Measurement Technique using Ellipse Formula

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