Improved classification of crystallization images using data fusion and multiple classifiers

2008 ◽  
Vol 64 (8) ◽  
pp. 823-833 ◽  
Author(s):  
Samarasena Buchala ◽  
Julie C. Wilson
Keyword(s):  
Data Fusion ◽  
Multiple Classifiers ◽  
Using Data

scholarly journals Classification of Multi-temporal SAR Data by Using Data Transform Based Features and Multiple Classifiers

2015 ◽  
Vol 31 (3) ◽  
pp. 205-214
Author(s):  
Hee Young Yoo ◽  
No-Wook Park ◽  
Sukyoung Hong ◽  
Kyungdo Lee ◽  
Yeseul Kim
Keyword(s):  
Multiple Classifiers ◽  
Multi Temporal ◽  
Sar Data ◽  
Using Data

Enhancing Athlete Tracking Using Data Fusion in Wearable Technologies

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Adnan Waqar ◽  
Iftekhar Ahmad ◽  
Daryoush Habibi ◽  
Nicolas Hart ◽  
Quoc Viet Phung
Keyword(s):  
Data Fusion ◽  
Wearable Technologies ◽  
Using Data

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

Hairline breakage detection in X-ray images using data fusion

2017 ◽  
Vol 77 (13) ◽  
pp. 17207-17222 ◽  
Author(s):  
C. Harriet Linda ◽  
G. Wiselin Jiji
Keyword(s):  
Data Fusion ◽  
X Ray ◽  
Using Data

2D- and 3D-visualisation of NDT-data using data fusion technique

2005 ◽  
Vol 38 (9) ◽  
pp. 817-826 ◽  
Author(s):  
C. Kohl ◽  
M. Krause ◽  
C. Maierhofer ◽  
J. Wöstmann
Keyword(s):  
Data Fusion ◽  
Fusion Technique ◽  
3D Visualisation ◽  
Using Data ◽  
2D And 3D
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