UAV Image Based Crop and Weed Distribution Estimation on Embedded GPU Boards

2019 ◽  
pp. 100-108
Author(s):  
Mulham Fawakherji ◽  
Ciro Potena ◽  
Domenico D. Bloisi ◽  
Marco Imperoli ◽  
Alberto Pretto ◽  
...  
Keyword(s):  
Distribution Estimation ◽  
Weed Distribution ◽  
Uav Image ◽  
Embedded Gpu

Probability distribution estimation for Web service QoS based on max entropy principle

2013 ◽  
Vol 32 (10) ◽  
pp. 2728-2731
Author(s):  
Zhi-hua DAI ◽  
Xiao-dong FU ◽  
Yuan HUANG ◽  
Nan JIA
Keyword(s):  
Probability Distribution ◽  
Web Service ◽  
Distribution Estimation ◽  
Entropy Principle

scholarly journals Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 27971-27986
Author(s):  
Shantam Shorewala ◽  
Armaan Ashfaque ◽  
R. Sidharth ◽  
Ujjwal Verma
Keyword(s):  
Supervised Learning ◽  
Precision Agriculture ◽  
Weed Density ◽  
Distribution Estimation

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.

Image-only Real-time Incremental UAV Image Mosaic for Multi-strip Flight

2020 ◽  
pp. 1-1
Author(s):  
Fangbing Zhang ◽  
Tao Yang ◽  
Linfeng Liu ◽  
Bang Liang ◽  
Yi Bai ◽  
...  
Keyword(s):  
Real Time ◽  
Image Mosaic ◽  
Uav Image

scholarly journals Asymptotic normality of conditional distribution estimation in the single index model

2017 ◽  
Vol 9 (1) ◽  
pp. 162-175
Author(s):  
Diaa Eddine Hamdaoui ◽  
Amina Angelika Bouchentouf ◽  
Abbes Rabhi ◽  
Toufik Guendouzi
Keyword(s):  
Distribution Function ◽  
Confidence Interval ◽  
Asymptotic Normality ◽  
Conditional Distribution ◽  
Conditional Distribution Function ◽  
Single Index ◽  
Index Model ◽  
Single Index Model ◽  
Distribution Estimation

AbstractThis paper deals with the estimation of conditional distribution function based on the single-index model. The asymptotic normality of the conditional distribution estimator is established. Moreover, as an application, the asymptotic (1 − γ) confidence interval of the conditional distribution function is given for 0 < γ < 1.

scholarly journals Adaptive Probability Distribution Estimation Based upon Maximum Entropy

1984 ◽  
Vol R-33 (4) ◽  
pp. 353-357 ◽  
Author(s):  
James E. Miller ◽  
Richard W. Kulp ◽  
George E. Orr
Keyword(s):  
Probability Distribution ◽  
Maximum Entropy ◽  
Distribution Estimation
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