Erratum to: ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States

Abstract In this work, we improve the semantic segmentation of multi-layer top-view grid maps in the context of LiDAR-based perception for autonomous vehicles. To achieve this goal, we fuse sequential information from multiple consecutive LiDAR measurements with respect to the driven trajectory of an autonomous vehicle. By doing so, we enrich the multi-layer grid maps which are subsequently used as the input of a neural network. Our approach can be used for LiDAR-only 360 ° surround view semantic scene segmentation while being suitable for real-time critical systems. We evaluate the benefit of fusing sequential information using a dense semantic ground truth and discuss the effect on different classes.

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A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images

Remote Sensing ◽

10.3390/rs13061212 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1212

Author(s):

Saba Rabab ◽

Edmond Breen ◽

Alem Gebremedhin ◽

Fan Shi ◽

Pieter Badenhorst ◽

...

Keyword(s):

High Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Digital Images ◽

Individual Plant ◽

Multispectral Images ◽

Fresh Weight ◽

Field Environment ◽

Plant Phenomics ◽

Top View

The extraction of automated plant phenomics from digital images has advanced in recent years. However, the accuracy of extracted phenomics, especially for individual plants in a field environment, requires improvement. In this paper, a new and efficient method of extracting individual plant areas and their mean normalized difference vegetation index from high-resolution digital images is proposed. The algorithm was applied on perennial ryegrass row field data multispectral images taken from the top view. First, the center points of individual plants from digital images were located to exclude plant positions without plants. Second, the accurate area of each plant was extracted using its center point and radius. Third, the accurate mean normalized difference vegetation index of each plant was extracted and adjusted for overlapping plants. The correlation between the extracted individual plant phenomics and fresh weight ranged between 0.63 and 0.75 across four time points. The methods proposed are applicable to other crops where individual plant phenotypes are of interest.

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Localization of immunolabels by electron microscopy and image averaging

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100075208 ◽

1983 ◽

Vol 41 ◽

pp. 282-283

Author(s):

J. Frank ◽

P.-Y. Sizaret ◽

A. Verschoor ◽

J. Lamy

Keyword(s):

Electron Microscopy ◽

Single Particle ◽

Attachment Site ◽

Uranyl Acetate ◽

Limulus Polyphemus ◽

Resolution Limit ◽

Electron Microscopic ◽

Image Averaging ◽

Top View ◽

Better Than

The accuracy with which the attachment site of immunolabels bound to macromolecules may be localized in electron microscopic images can be considerably improved by using single particle averaging. The example studied in this work showed that the accuracy may be better than the resolution limit imposed by negative staining (∽2nm).The structure used for this demonstration was a halfmolecule of Limulus polyphemus (LP) hemocyanin, consisting of 24 subunits grouped into four hexamers. The top view of this structure was previously studied by image averaging and correspondence analysis. It was found to vary according to the flip or flop position of the molecule, and to the stain imbalance between diagonally opposed hexamers (“rocking effect”). These findings have recently been incorporated into a model of the full 8 × 6 molecule.LP hemocyanin contains eight different polypeptides, and antibodies specific for one, LP II, were used. Uranyl acetate was used as stain. A total of 58 molecule images (29 unlabelled, 29 labelled with antl-LPII Fab) showing the top view were digitized in the microdensitometer with a sampling distance of 50μ corresponding to 6.25nm.

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Three-Dimensional Reconstruction of Native Androctonus Australis Hemocyanin

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100181014 ◽

1990 ◽

Vol 48 (1) ◽

pp. 452-453

Author(s):

Nicolas Boisset ◽

Jean-Christophe Taveau ◽

Jean Lamy ◽

Terence Wagenknecht ◽

Michael Radermacher ◽

...

Keyword(s):

Electron Microscopy ◽

Solid Body ◽

Body Surface ◽

Three Dimensional ◽

Staining Technique ◽

Three Dimensional Reconstruction ◽

Dimensional Reconstruction ◽

View Reconstruction ◽

Surface Representations ◽

Top View

Hemocyanin, the respiratory pigment of the scorpion Androctonus australis is composed of 24 kidney shaped subunits. A model of architecture supported by many indirect arguments has been deduced from electron microscopy (EM) and immuno-EM. To ascertain, the disposition of the subunits within the oligomer, the 24mer was submitted to three-dimensional reconstruction by the method of single-exposure random-conical tilt series.A sample of native hemocyanin, prepared with the double layer negative staining technique, was observed by transmisson electron microscopy under low-dose conditions. Six 3D-reconstructions were carried out indenpendently from top, side and 45°views. The results are composed of solid-body surface representations, and slices extracted from the reconstruction volume.The main two characters of the molecule previously reported by Van Heel and Frank, were constantly found in the solid-body surface representations. These features are the presence of two different faces called flip and flop and a rocking of the molecule around an axis passing through diagonnally opposed hexamers. Furthermore, in the solid-body surface of the top view reconstruction, the positions and orientations of the bridges connecting the half molecules were found in excellent agreement with those predicted by the model.

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Face Completion Using Semantic Segmentation and Geometric Features

International Review of Automatic Control (IREACO) ◽

10.15866/ireaco.v11i6.15738 ◽

2018 ◽

Vol 11 (6) ◽

pp. 304

Author(s):

Javier Pinzon-Arenas ◽

Robinson Jimenez-Moreno ◽

Ruben Hernandez-Beleno

Keyword(s):

Semantic Segmentation ◽

Geometric Features

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Nano Probe Analysis of Device Characteristics Affected by Ring Type Crystalline Defect

ISTFA 2011: Conference Proceedings from the 37th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2011p0322 ◽

2011 ◽

Author(s):

Jong Hak Lee ◽

Yu Jun Lee ◽

Jung Sam Kim ◽

Seo Kyung Jeong ◽

Min Su Kim ◽

...

Keyword(s):

Failure Analysis ◽

Physical Method ◽

Hybrid Analysis ◽

Electrical Method ◽

Ring Type ◽

Crystalline Defects ◽

Address Data ◽

Signal Input ◽

Crystalline Defect ◽

Top View

Abstract In this work, crystalline defects (dislocations) occurred in the silicon substrate during annealing SOD (Spin On Dielectric) which is an easy choice for its superior STI gap-fill ability. The reversal of address data that share same SIO (Signal Input Out) line in a DQ arises from crystalline defects. The failure analysis of physical methods has difficulty finding minute defects within the active because it is scarcely detectable from the top view. Situation can be well understood by electrical analysis using the nano probe. Due to its ability to probing contact nodes around the fail area, a ring type crystalline defect which is hardly detected from the top view was effectively analyzed by 3D TEM with the assistance of nano probe. This work shows that hybrid analysis of electrical method by nano probe and physical method by 3D TEM is useful and effective in failure analysis in semiconductor.

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Plant Phenomics

10.34133/plantphenomics ◽

2020 ◽

Keyword(s):

Plant Phenomics

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Multi-model Integrated Weakly Supervised Semantic Segmentation Method

Journal of Computer-Aided Design & Computer Graphics ◽

10.3724/sp.j.1089.2019.17379 ◽

2019 ◽

Vol 31 (5) ◽

pp. 800

Author(s):

Changzhen Xiong ◽

Hui Zhi

Keyword(s):

Semantic Segmentation ◽

Segmentation Method ◽

Weakly Supervised

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Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽

10.21820/23987073.2020.2.9 ◽

2020 ◽

Vol 2020 (2) ◽

pp. 9-11

Author(s):

Tomohiro Fukuda

Keyword(s):

Deep Learning ◽

Real Time ◽

Computer Games ◽

Construction Projects ◽

Mixed Reality ◽

Semantic Segmentation ◽

Environment Design ◽

Aviation Training ◽

Architecture And Design ◽

World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

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