scholarly journals USING 3D MODELS TO GENERATE LABELS FOR PANOPTIC SEGMENTATION OF INDUSTRIAL SCENES

2019 ◽  
Vol IV-2/W5 ◽  
pp. 61-68
Author(s):  
A. Nivaggioli ◽  
J. F. Hullo ◽  
G. Thibault
Keyword(s):  
Large Scale ◽  
Direct Reduction ◽  
3D Models ◽  
Industrial Building ◽  
True Negative ◽  
Industrial Companies ◽  
Learning Techniques ◽  
Great Performance ◽  
The Many ◽  
Public Datasets

<p><strong>Abstract.</strong> Industrial companies often require complete inventories of their infrastructure. In many cases, a better inventory leads to a direct reduction of cost and uncertainty of engineering. While large scale panoramic surveys now allow these inventories to be performed remotely and reduce time on-site, the time and money required to visually segment the many types of components on thousands of high resolution panoramas can make the process infeasible. Recent studies have shown that deep learning techniques, namely deep neural networks, can accurately perform panoptic segmentation of <i>things</i> and <i>stuff</i> and hence be used to inventory the components of a picture. In order to train those deep architectures with specific industrial equipment, not available in public datasets, our approach uses an as-built 3D model of an industrial building to procedurally generate labels. Our results show that, despite the presence of errors during the generation of the dataset, our method is able to accurately perform panoptic segmentation on images of industrial scenes. In our testing, 80% of generated labels were correctly identified (non null intersection over union, i.e. true positive) by the panoptic segmentation, with great performance levels even for difficult classes, such as reflective heat insulators. We then visually investigated the 20% of true negative, and discovered that 80% were correctly segmented, but were counted as true negative because of errors in the dataset generation. Demonstrating this level of accuracy for panoptic segmentation on industrial panoramas for inventories also offers novel perspectives for 3D laser scan processing.</p>

scholarly journals EpiScanpy: integrated single-cell epigenomic analysis

2019 ◽  
Author(s):  
Anna Danese ◽  
Maria L. Richter ◽  
David S. Fischer ◽  
Fabian J. Theis ◽  
Maria Colomé-Tatché
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Large Scale ◽  
Feature Space ◽  
Rna Seq ◽  
Computational Framework ◽  
Learning Techniques ◽  
Multiple Feature ◽  
The Many ◽  
Cell Data

ABSTRACTEpigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.

scholarly journals EpiScanpy: integrated single-cell epigenomic analysis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anna Danese ◽  
Maria L. Richter ◽  
Kridsadakorn Chaichoompu ◽  
David S. Fischer ◽  
Fabian J. Theis ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Feature Space ◽  
Cell Types ◽  
Reduction Cell ◽  
Learning Techniques ◽  
Multiple Feature ◽  
The Many ◽  
Cell Data ◽  
Trajectory Learning

AbstractEpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types.

Integration of Ecological and Engineering Aspects in Planning Large Scale Tidal Power Development in the Bay of Fundy, Canada

1984 ◽  
Vol 16 (1-2) ◽  
pp. 281-295 ◽  
Author(s):  
Donald C Gordon
Keyword(s):  
Large Scale ◽  
Feasibility Studies ◽  
Bay Of Fundy ◽  
Final Decision ◽  
Power Development ◽  
Tidal Power ◽  
History Of ◽  
Scientists And Engineers ◽  
The Many ◽  
University Scientists

Large-scale tidal power development in the Bay of Fundy has been given serious consideration for over 60 years. There has been a long history of productive interaction between environmental scientists and engineers durinn the many feasibility studies undertaken. Up until recently, tidal power proposals were dropped on economic grounds. However, large-scale development in the upper reaches of the Bay of Fundy now appears to be economically viable and a pre-commitment design program is highly likely in the near future. A large number of basic scientific research studies have been and are being conducted by government and university scientists. Likely environmental impacts have been examined by scientists and engineers together in a preliminary fashion on several occasions. A full environmental assessment will be conducted before a final decision is made and the results will definately influence the outcome.

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

scholarly journals Quality Control in 3D Printing: Accuracy Analysis of 3D-Printed Models of Patient-Specific Anatomy

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1021
Author(s):  
Bernhard Dorweiler ◽  
Pia Elisabeth Baqué ◽  
Rayan Chaban ◽  
Ahmed Ghazy ◽  
Oroa Salem
Keyword(s):  
Wall Thickness ◽  
Large Scale ◽  
Fused Deposition Modeling ◽  
Vascular Anatomy ◽  
3D Models ◽  
Patient Specific ◽  
Stl File ◽  
Fused Deposition ◽  
3D Printed ◽  
The Mean

As comparative data on the precision of 3D-printed anatomical models are sparse, the aim of this study was to evaluate the accuracy of 3D-printed models of vascular anatomy generated by two commonly used printing technologies. Thirty-five 3D models of large (aortic, wall thickness of 2 mm, n = 30) and small (coronary, wall thickness of 1.25 mm, n = 5) vessels printed with fused deposition modeling (FDM) (rigid, n = 20) and PolyJet (flexible, n = 15) technology were subjected to high-resolution CT scans. From the resulting DICOM (Digital Imaging and Communications in Medicine) dataset, an STL file was generated and wall thickness as well as surface congruency were compared with the original STL file using dedicated 3D engineering software. The mean wall thickness for the large-scale aortic models was 2.11 µm (+5%), and 1.26 µm (+0.8%) for the coronary models, resulting in an overall mean wall thickness of +5% for all 35 3D models when compared to the original STL file. The mean surface deviation was found to be +120 µm for all models, with +100 µm for the aortic and +180 µm for the coronary 3D models, respectively. Both printing technologies were found to conform with the currently set standards of accuracy (<1 mm), demonstrating that accurate 3D models of large and small vessel anatomy can be generated by both FDM and PolyJet printing technology using rigid and flexible polymers.

Sign in / Sign up

Export Citation Format

Share Document