An Invoice Reading System Using a Graph Convolutional Network

2019 ◽  
pp. 144-158 ◽  
Author(s):  
D. Lohani ◽  
A. Belaïd ◽  
Y. Belaïd
Keyword(s):  
Convolutional Network ◽  
Reading System

Development of imaging plate for a TEM and its characteristics

1989 ◽  
Vol 47 ◽  
pp. 100-101
Author(s):  
N. Mori ◽  
T. Oikawa ◽  
Y. Harada ◽  
J. Miyahara ◽  
T. Matsuo
Keyword(s):  
Dynamic Range ◽  
Protective Layer ◽  
High Sensitivity ◽  
Imaging Plate ◽  
Phosphor Layer ◽  
Imaging Device ◽  
X Ray Imaging ◽  
New Type ◽  
Basic Characteristics ◽  
Reading System

The Imaging Plate (IP) is a new type imaging device, which was developed for diagnostic x ray imaging. We have reported that usage of the IP for a TEM has many merits; those are high sensitivity, wide dynamic range, and good linearity. However in the previous report the reading system was prototype drum-type-scanner, and IP was also experimentally made, which phosphor layer was 50μm thick with no protective layer. So special care was needed to handle them, and they were used only to make sure the basic characteristics. In this article we report the result of newly developed reading, printing system and high resolution IP for practical use. We mainly discuss the characteristics of the IP here. (Precise performance concerned with the reader and other system are reported in the other article.)Fig.1 shows the schematic cross section of the IP. The IP consists of three parts; protective layer, phosphor layer and support.

The Neurobiology of Reading Differs for Deaf and Hearing Adults

2020 ◽  
pp. 346-359
Author(s):  
Karen Emmorey
Keyword(s):  
Semantic Processing ◽  
Event Related Potentials ◽  
Coarse Grained ◽  
Reading Success ◽  
Deaf Readers ◽  
Electrophysiological Studies ◽  
Deaf Adults ◽  
Related Potentials ◽  
Reading System ◽  
New Evidence

Recent neuroimaging and electrophysiological studies reveal how the reading system successfully adapts when phonological codes are relatively coarse-grained due to reduced auditory input during development. New evidence suggests that the optimal end-state for the reading system may differ for deaf versus hearing adults and indicates that certain neural patterns that are maladaptive for hearing readers may be beneficial for deaf readers. This chapter focuses on deaf adults who are signers and have achieved reading success. Although the left-hemisphere-dominant reading circuit is largely similar in both deaf and hearing individuals, skilled deaf readers exhibit a more bilateral neural response to written words and sentences than their hearing peers, as measured by event-related potentials and functional magnetic resonance imaging. Skilled deaf readers may also rely more on neural regions involved in semantic processing than hearing readers do. Overall, emerging evidence indicates that the neural markers for reading skill may differ for deaf and hearing adults.

scholarly journals Neurofunctionally dissecting the reading system in children

2017 ◽  
Vol 27 ◽  
pp. 45-57 ◽  
Author(s):  
Johanna Liebig ◽  
Eva Froehlich ◽  
Carmen Morawetz ◽  
Mario Braun ◽  
Arthur M. Jacobs ◽  
...  
Keyword(s):  
Reading System

scholarly journals Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3848
Author(s):  
Wei Cui ◽  
Meng Yao ◽  
Yuanjie Hao ◽  
Ziwei Wang ◽  
Xin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Contextual Information ◽  
Information Aggregation ◽  
Semantic Segmentation ◽  
Spatial Correlations ◽  
Convolutional Network ◽  
Object Based ◽  
Graph Neural Networks ◽  
Salt And Pepper

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

scholarly journals SACN: A Novel Rotating Face Detector Based on Architecture Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 558
Author(s):  
Anping Song ◽  
Xiaokang Xu ◽  
Xinyi Zhai
Keyword(s):  
Face Detection ◽  
Human Face ◽  
Angle Error ◽  
Rotation Invariant ◽  
Convolutional Network ◽  
Data Set ◽  
Practical Applications ◽  
Model Size ◽  
Average Angle ◽  
Face Detector

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6° to 10.5°.

scholarly journals NPU RGB+D Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players

2021 ◽  
Vol 11 (10) ◽  
pp. 4426
Author(s):  
Chunyan Ma ◽  
Ji Fan ◽  
Jinghao Yao ◽  
Tao Zhang
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Evaluation Criteria ◽  
Image Data ◽  
Basketball Player ◽  
Basketball Players ◽  
Convolutional Network ◽  
Atomic Actions ◽  
New Feature

Computer vision-based action recognition of basketball players in basketball training and competition has gradually become a research hotspot. However, owing to the complex technical action, diverse background, and limb occlusion, it remains a challenging task without effective solutions or public dataset benchmarks. In this study, we defined 32 kinds of atomic actions covering most of the complex actions for basketball players and built the dataset NPU RGB+D (a large scale dataset of basketball action recognition with RGB image data and Depth data captured in Northwestern Polytechnical University) for 12 kinds of actions of 10 professional basketball players with 2169 RGB+D videos and 75 thousand frames, including RGB frame sequences, depth maps, and skeleton coordinates. Through extracting the spatial features of the distances and angles between the joint points of basketball players, we created a new feature-enhanced skeleton-based method called LSTM-DGCN for basketball player action recognition based on the deep graph convolutional network (DGCN) and long short-term memory (LSTM) methods. Many advanced action recognition methods were evaluated on our dataset and compared with our proposed method. The experimental results show that the NPU RGB+D dataset is very competitive with the current action recognition algorithms and that our LSTM-DGCN outperforms the state-of-the-art action recognition methods in various evaluation criteria on our dataset. Our action classifications and this NPU RGB+D dataset are valuable for basketball player action recognition techniques. The feature-enhanced LSTM-DGCN has a more accurate action recognition effect, which improves the motion expression ability of the skeleton data.

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