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

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 Analysis of TOF-SIMS and microscope fused image data using sparse modeling and deep learning

2019 ◽  
Vol 26 (2) ◽  
pp. 132-133
Author(s):  
Satoka Aoyagi ◽  
Tomomi Akiyama ◽  
Takayuki Yamagishi
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Sparse Modeling ◽  
Tof Sims ◽  
Fused Image

Color Trend Analysis using Machine Learning with Fashion Collection Images

2021 ◽  
pp. 0887302X2199594
Author(s):  
Ahyoung Han ◽  
Jihoon Kim ◽  
Jaehong Ahn
Keyword(s):  
Machine Learning ◽  
Trend Analysis ◽  
Image Data ◽  
Fashion Industry ◽  
Color Palette ◽  
Web Scraping ◽  
Design Variables ◽  
Sales Organizations ◽  
Fashion Designers ◽  
Selection Of

Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.

scholarly journals Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Gangqiang Zhang ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Weiwei Lei
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Groundwater Level ◽  
North China Plain ◽  
North China ◽  
Gradient Boosting ◽  
Fusion Model ◽  
The North ◽  
The North China Plain ◽  
Prediction Module

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

scholarly journals Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ossama Mahmoud ◽  
Mahmoud El-Sakka ◽  
Barry G. H. Janssen
Keyword(s):  
Machine Learning ◽  
Blood Flow ◽  
Human Error ◽  
Image Data ◽  
Video Microscopy ◽  
Microvascular Blood Flow ◽  
Image Processing Algorithm ◽  
Step Algorithm ◽  
3D Cnn

AbstractMicrovascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.

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

scholarly journals Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021 ◽  
Vol 13 (3) ◽  
pp. 531
Author(s):  
Caiwang Zheng ◽  
Amr Abd-Elrahman ◽  
Vance Whitaker
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Precision Agriculture ◽  
Image Data ◽  
Fruit Shape ◽  
Biological Information ◽  
Genetic Traits ◽  
Multiple Sensors ◽  
Fruit Maturity ◽  
High Throughput Phenotyping

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

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