scholarly journals 3-D human pose measurement method based on deep learning and epipolar constraint

2020 ◽  
Vol 41 (6) ◽  
pp. 1166-1173
Author(s):  
SONG Limei ◽  
◽  
◽  
LYU Kunkun ◽  
YANG Yan’gang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Measurement Method ◽  
Epipolar Constraint ◽  
Human Pose

In-Cabin vehicle synthetic data to test Deep Learning based human pose estimation models

2021 ◽  
Author(s):  
Luis Gustavo Tomal Ribas ◽  
Marta Pereira Cocron ◽  
Joed Lopes Da Silva ◽  
Alessandro Zimmer ◽  
Thomas Brandmeier
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Synthetic Data ◽  
Human Pose Estimation ◽  
Human Pose ◽  
Estimation Models

scholarly journals Analysis and Best Parameters Selection for Person Recognition based on Gait model using CNN algorithm and Image Augmentation

2020 ◽  
Author(s):  
Abeer Saleh ◽  
Talal Hamoud
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Person Recognition ◽  
Critical Issues ◽  
Dataset Size ◽  
Deep Convolution Neural Network ◽  
Human Pose ◽  
Improved Model ◽  
Pose Variation ◽  
Best Parameters

Abstract Person recognition based on gait model and motion print is indeed a challenging and novel task due to its usages and to the critical issues of human pose variation, human body occlusion, camera view variation, etc. In this project, a deep convolution neural network (CNN) was modified and adapted for person recognition with image augmentation technique. CNN is best algorithm of deep learning algorithms. Adaptation aims to get best values for CNN parameters to get best CNN model. In Addition to the CNN parameters, the design of CNN model itself was adapted to get best model design; number of layers and normalization between them. After choosing best parameters and best design, Image augmentation was used to increase train dataset with many copies of the image to boost the number of different images that will be used to train Deep learning algorithms. The tests were achieved using known dataset (Market dataset). The dataset contains sequential pictures of people in different gait status. The image in CNN model as matrix is extracted to many images or matrices, so dataset size may be bigger by hundred times to make the problem a big data problem, in this project Results show that adaptation has improved the accuracy of person recognition using gait model, that is represented in many successive images for the same person. In addition, dataset contains images of person carrying things. The improved model of CNN is robust to image dimensions (quality and resolution) and to carried things by persons.

scholarly journals Variation of female pronucleus reveal oocyte or embryo abnormality: An expert experience deep learning of non-dark box analysis

2021 ◽  
Author(s):  
Jingwei Yang ◽  
Yikang Wang ◽  
Chong Li ◽  
Wei Han ◽  
Weiwei Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Measurement Method ◽  
Genetic Test ◽  
Automatic Recognition ◽  
Exponential Fitting ◽  
Qualitative Detection ◽  
Female Pronucleus ◽  
Analysis Of The Images ◽  
Zygote Stage ◽  
Paradoxical Results

Background: Pronuclear assessment appears to have the ability to distinguish good and bad embryos in the zygote stage,but paradoxical results were obtained in clinical studies.This situation might be caused by the robust qualitative detection of the development of dynamic pronuclei. Here,we aim to establish a quantitative pronuclear measurement method by applying expert experience deep learning from large annotated datasets. Methods: Convinced handle-annotated 2PN images(13419) were used for deep learning then corresponded errors were recorded through handle check for subsequent parameters adjusting. We used 790 embryos with 52479 PN images from 155 patients for analysis the area of pronuclei and the preimplantation genetic test results.Establishment of the exponential fitting equation and the key coefficient β1 was extracted from the model for quantitative analysis for pronuclear(PN) annotation and automatic recognition. Findings: Based on the female original PN coefficient β1,the chromosome normal rate in the blastocyst with biggest PN area is much higher than that of the blastocyst with smallest PN area(58.06% vs.45.16%, OR=1.68[1.07-2.64];P=0.031).After adjusting coefficient β1 by the first three frames which high variance of outlier PN areas was removed, coefficient β1 at 12 hours and at 14 hours post-insemination,similar but stronger evidence was obtained. All these discrepancies resulted from the female propositus in the PGT(SR) subgroup and smaller chromosomal errors. Conclusion(s): The results suggest that detailed analysis of the images of embryos could improve our understanding of developmental biology. Funding: None

A Stereovision Measurement Method Using Epipolar Constraint to Correct Digital Image Correlation Matching

2017 ◽  
Vol 44 (8) ◽  
pp. 0804003 ◽  
Author(s):  
Shan Baohua ◽  
哈尔滨工业大学土木工程学院 ◽  
黑龙江 哈尔滨 ◽  
哈尔滨工业大学结构工程灾变与控制教育部重点实验室 ◽  
黑龙江 哈尔滨 ◽  
...  
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
Measurement Method ◽  
Image Correlation ◽  
Epipolar Constraint ◽  
Correlation Matching

Multi-stage deep learning based single-frame fringe projection 3D measurement method

2020 ◽  
Vol 49 (6) ◽  
pp. 20200023
Author(s):  
张钊 Zhao Zhang ◽  
韩博文 Bowen Han ◽  
于浩天 Haotian Yu ◽  
张毅 Yi Zhang ◽  
郑东亮 Dongliang Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Measurement Method ◽  
Fringe Projection ◽  
3D Measurement ◽  
Single Frame ◽  
Multi Stage

SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery

2020 ◽  
Vol 106 ◽  
pp. 107472 ◽  
Author(s):  
Meysam Madadi ◽  
Hugo Bertiche ◽  
Sergio Escalera
Keyword(s):  
Deep Learning ◽  
Shape Recovery ◽  
Human Pose

scholarly journals Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2484 ◽  
Author(s):  
Weixing Zhang ◽  
Chandi Witharana ◽  
Weidong Li ◽  
Chuanrong Zhang ◽  
Xiaojiang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Measurement Method ◽  
Detection Algorithm ◽  
Quality Data ◽  
Visual Interpretation ◽  
Buffer Zones ◽  
Google Street View ◽  
Street View ◽  
Utility Poles

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.

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