Time reversal focusing through a human skull optimized by skull surface detection algorithm

2002 ◽  
Vol 112 (5) ◽  
pp. 2433-2433
Author(s):  
Jean‐Francois Aubry ◽  
Didier Cassereau ◽  
Mathias Fink
Keyword(s):  
Time Reversal ◽  
Detection Algorithm ◽  
Human Skull ◽  
Surface Detection

Comparison between time reversal and spatio‐temporal inverse filter application to focusing through a human skull

2001 ◽  
Vol 109 (5) ◽  
pp. 2397-2397
Author(s):  
Mickael Tanter ◽  
Jean‐Francois Aubry ◽  
Jean‐Louis Thomas ◽  
Mathias Fink
Keyword(s):  
Time Reversal ◽  
Inverse Filter ◽  
Human Skull ◽  
Spatio Temporal

scholarly journals Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7769
Author(s):  
Wansik Choi ◽  
Jun Heo ◽  
Changsun Ahn
Keyword(s):  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Detection Algorithm ◽  
Road Surface ◽  
Detection Methods ◽  
Learning Technology ◽  
Surface Conditions ◽  
The Road ◽  
Detection Algorithms ◽  
Surface Detection

Road surface detection is important for safely driving autonomous vehicles. This is because the knowledge of road surface conditions, in particular, dry, wet, and snowy surfaces, should be considered for driving control of autonomous vehicles. With the rise of deep learning technology, road surface detection methods using deep neural networks (DNN) have been widely used for developing road surface detection algorithms. To apply DNN in road surface detection, the dataset should be large and well-balanced for accurate and robust performance. However, most of the images of road surfaces obtained through usual data collection processes are not well-balanced. Most of the collected surface images tend to be of dry surfaces because road surface conditions are highly correlated with weather conditions. This could be a challenge in developing road surface detection algorithms. This paper proposes a method to balance the imbalanced dataset using CycleGAN to improve the performance of a road surface detection algorithm. CycleGAN was used to artificially generate images of wet and snow-covered roads. The road surface detection algorithm trained using the CycleGAN-augmented dataset had a better IoU than the method using imbalanced basic datasets. This result shows that CycleGAN-generated images can be used as datasets for road surface detection to improve the performance of DNN, and this method can help make the data acquisition process easy.

scholarly journals Dynamic Water Surface Detection Algorithm Applied on PROBA-V Multispectral Data

2016 ◽  
Vol 8 (12) ◽  
pp. 1010 ◽  
Author(s):  
Luc Bertels ◽  
Bruno Smets ◽  
Davy Wolfs
Keyword(s):  
Water Surface ◽  
Detection Algorithm ◽  
Multispectral Data ◽  
Surface Detection
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