scholarly journals Shift-rotation absolute measurement method for irregular aperture optical surfaces based on deep learning

2021 ◽  
pp. 105020
Author(s):  
Lili Yang ◽  
Jiantai Dou ◽  
Zhongming Yang ◽  
Zhaojun Liu
Keyword(s):  
Deep Learning ◽  
Measurement Method ◽  
Absolute Measurement ◽  
Optical Surfaces ◽  
Shift Rotation

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

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

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.

Absolute measurement of flats with the method of shift-rotation

2013 ◽  
Vol 20 (5) ◽  
pp. 374-377 ◽  
Author(s):  
Weihong Song ◽  
Fan Wu ◽  
Xi Hou ◽  
Wenchuan Zhao ◽  
Yongjian Wan
Keyword(s):  
Absolute Measurement ◽  
Shift Rotation

scholarly journals A deep-learning based water-level measurement method from CCTV camera images

2019 ◽  
Vol 58 (1) ◽  
pp. 28-33
Author(s):  
Hideaki MAEHARA ◽  
Momoyo NAGASE ◽  
Michihiro KUCHI ◽  
Toshihisa SUZUKI ◽  
Kenji TAIRA
Keyword(s):  
Deep Learning ◽  
Water Level ◽  
Measurement Method ◽  
Level Measurement ◽  
Cctv Camera
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