First use of data fusion and multivariate analysis of ToF-SIMS and SEM image data for studying deuterium-assisted degradation processes in duplex steels

2016 ◽  
Vol 48 (7) ◽  
pp. 474-478 ◽  
Author(s):  
Oded Sobol ◽  
Gerald Holzlechner ◽  
Markus Holzweber ◽  
Hans Lohninger ◽  
Thomas Boellinghaus ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Data Fusion ◽  
Image Data ◽  
Sem Image ◽  
Degradation Processes ◽  
Use Of Data ◽  
Tof Sims ◽  
Duplex Steels

scholarly journals TOF-SIMS Image Data Fusion by Multivariate Analysis and TOF-SIMS Spectrum Analysis by Sparse Modeling and Machine Learning

2018 ◽  
Vol 25 (2) ◽  
pp. 103-114 ◽  
Author(s):  
Wataru Ishikura ◽  
Kazuma Takahashi ◽  
Takayuki Yamagishi ◽  
Dan Aoki ◽  
Kazuhiko Fukushima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multivariate Analysis ◽  
Data Fusion ◽  
Spectrum Analysis ◽  
Image Data ◽  
Sparse Modeling ◽  
Tof Sims

scholarly journals Augmented reality visualisation for orthopaedic surgical guidance with pre‐ and intra‐operative multimodal image data fusion

2018 ◽  
Vol 5 (5) ◽  
pp. 189-193 ◽  
Author(s):  
Houssam El‐Hariri ◽  
Prashant Pandey ◽  
Antony J. Hodgson ◽  
Rafeef Garbi
Keyword(s):  
Augmented Reality ◽  
Data Fusion ◽  
Image Data ◽  
Surgical Guidance

scholarly journals Multivariate analysis strategies for processing ToF-SIMS images of biomaterials

Biomaterials ◽  
2007 ◽  
Vol 28 (15) ◽  
pp. 2412-2423 ◽  
Author(s):  
B TYLER ◽  
G RAYAL ◽  
D CASTNER
Keyword(s):  
Multivariate Analysis ◽  
Tof Sims ◽  
Analysis Strategies

CT–MR image data fusion for computer assisted navigated neurosurgery of temporal bone tumors

2007 ◽  
Vol 62 (2) ◽  
pp. 192-198 ◽  
Author(s):  
Stefan Franz Nemec ◽  
Markus Alexander Donat ◽  
Sheida Mehrain ◽  
Klaus Friedrich ◽  
Christian Krestan ◽  
...  
Keyword(s):  
Data Fusion ◽  
Temporal Bone ◽  
Bone Tumors ◽  
Image Data ◽  
Computer Assisted ◽  
Mr Image

scholarly journals Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 77
Author(s):  
Tsu Chiang Lei ◽  
Shiuan Wan ◽  
You Cheng Wu ◽  
Hsin-Ping Wang ◽  
Chia-Wen Hsieh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Decision Tree ◽  
Data Fusion ◽  
Dynamic Time Warping ◽  
Image Data ◽  
Synthetic Aperture ◽  
Time Warping ◽  
Radar Images ◽  
Dynamic Time

This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.

scholarly journals Multivariate Analysis Applications in ToF-SIMS

2014 ◽  
Vol 20 (S3) ◽  
pp. 2050-2051
Author(s):  
Robert M. Moision ◽  
John A. Chaney
Keyword(s):  
Multivariate Analysis ◽  
Tof Sims

Surface immobilized antibody orientation determined using ToF-SIMS and multivariate analysis

2017 ◽  
Vol 55 ◽  
pp. 172-182 ◽  
Author(s):  
Nicholas G. Welch ◽  
Robert M.T. Madiona ◽  
Thomas B. Payten ◽  
Christopher D. Easton ◽  
Luisa Pontes-Braz ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Tof Sims ◽  
Antibody Orientation

CT–MR image data fusion for computer-assisted navigated surgery of orbital tumors

2010 ◽  
Vol 73 (2) ◽  
pp. 224-229 ◽  
Author(s):  
Stefan Franz Nemec ◽  
Philipp Peloschek ◽  
Maria Theresa Schmook ◽  
Christian Robert Krestan ◽  
Wolfgang Hauff ◽  
...  
Keyword(s):  
Data Fusion ◽  
Image Data ◽  
Computer Assisted ◽  
Orbital Tumors ◽  
Mr Image ◽  
Navigated Surgery
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