Recognition of the Hungarian Fingerspelling Alphabet using Convolutional Neural Network based on Depth Data

2018 ◽  
Author(s):  
Bence Danko ◽  
Gabor Kertesz
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Depth Data

scholarly journals Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5614
Author(s):  
Zhiqing Song ◽  
Ye Tuo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Damage Control ◽  
Mirror Reflection ◽  
Average Error ◽  
Traffic Sign ◽  
Efficient Manner ◽  
Urban Flood ◽  
Depth Data ◽  
Flood Depth

Flood depth monitoring is crucial for flood warning systems and damage control, especially in the event of an urban flood. Existing gauge station data and remote sensing data still has limited spatial and temporal resolution and coverage. Therefore, to expand flood depth data source taking use of online image resources in an efficient manner, an automated, low-cost, and real-time working frame called FloodMask was developed to obtain flood depth from online images containing flooded traffic signs. The method was built on the deep learning framework of Mask R-CNN (regional convolutional neural network), trained by collected and manually annotated traffic sign images. Following further the proposed image processing frame, flood depth data were retrieved more efficiently than manual estimations. As the main results, the flood depth estimates from images (without any mirror reflection and other inference problems) have an average error of 0.11 m, when compared to human visual inspection measurements. This developed method can be further coupled with street CCTV cameras, social media photos, and on-board vehicle cameras to facilitate the development of a smart city with a prompt and efficient flood monitoring system. In future studies, distortion and mirror reflection should be tackled properly to increase the quality of the flood depth estimates.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

No-Reference Utility Estimation with a Convolutional Neural Network

2018 ◽  
Vol 2018 (9) ◽  
pp. 202-1-202-6 ◽  
Author(s):  
Edward T. Scott ◽  
Sheila S. Hemami
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Utility Estimation

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAP-CNN)

2018 ◽  
Vol 2018 (10) ◽  
pp. 338-1-338-6
Author(s):  
Patrick Martell ◽  
Vijayan Asari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Defect Detection of Axle Box Cover Device Fixing Bolts in Metro Based on Convolutional Neural Network

2020 ◽  
Author(s):  
Yao Yang ◽  
Yuanjiang Hu ◽  
Lingling Chen ◽  
Xiaoman Liu ◽  
Na Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection

Convolutional Neural Network for Determining Sensitivity Matrix in ETI

2020 ◽  
Author(s):  
Haitao Ma ◽  
Shihong Yue ◽  
Jian Lu ◽  
Sidolla Yem ◽  
Huaxiang Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sensitivity Matrix
Sign in / Sign up

Export Citation Format

Share Document