Text Detection using Multilayer Separation in Real Scene Images

2010 ◽  
Author(s):  
Manoj Kumar ◽  
Young Chul Kim ◽  
Guee Sang Lee
Keyword(s):  
Text Detection ◽  
Real Scene

scholarly journals A Neural Network Model for Text Detection in Chinese Drug Package Insert

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 39781-39791
Author(s):  
Haiwen Wu ◽  
Ri-Gui Zhou ◽  
Yaochong Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Package Insert ◽  
Text Detection ◽  
Chinese Drug

scholarly journals A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring

2021 ◽  
Vol 11 (5) ◽  
pp. 2174
Author(s):  
Xiaoguang Li ◽  
Feifan Yang ◽  
Jianglu Huang ◽  
Li Zhuo
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Dynamic Scene ◽  
Feature Maps ◽  
Training Scheme ◽  
Real Scene ◽  
Global And Local

Images captured in a real scene usually suffer from complex non-uniform degradation, which includes both global and local blurs. It is difficult to handle the complex blur variances by a unified processing model. We propose a global-local blur disentangling network, which can effectively extract global and local blur features via two branches. A phased training scheme is designed to disentangle the global and local blur features, that is the branches are trained with task-specific datasets, respectively. A branch attention mechanism is introduced to dynamically fuse global and local features. Complex blurry images are used to train the attention module and the reconstruction module. The visualized feature maps of different branches indicated that our dual-branch network can decouple the global and local blur features efficiently. Experimental results show that the proposed dual-branch blur disentangling network can improve both the subjective and objective deblurring effects for real captured images.

scholarly journals Self-Localization of Mobile Robots Using a Single Catadioptric Camera with Line Feature Extraction

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4719
Author(s):  
Huei-Yung Lin ◽  
Yuan-Chi Chung ◽  
Ming-Liang Wang
Keyword(s):  
Mobile Robots ◽  
Ground Plane ◽  
Feature Correspondence ◽  
Projection Model ◽  
3D Space ◽  
Base Points ◽  
Real Scene ◽  
Correspondence Matching ◽  
Mobile Robot Localization ◽  
Catadioptric Camera

This paper presents a novel self-localization technique for mobile robots using a central catadioptric camera. A unified sphere model for the image projection is derived by the catadioptric camera calibration. The geometric property of the camera projection model is utilized to obtain the intersections of the vertical lines and ground plane in the scene. Different from the conventional stereo vision techniques, the feature points are projected onto a known planar surface, and the plane equation is used for depth computation. The 3D coordinates of the base points on the ground are calculated using the consecutive image frames. The derivation of motion trajectory is then carried out based on the computation of rotation and translation between the robot positions. We develop an algorithm for feature correspondence matching based on the invariability of the structure in the 3D space. The experimental results obtained using the real scene images have demonstrated the feasibility of the proposed method for mobile robot localization applications.

scholarly journals Research on Real Scene 3D Modelling Based on Multi-view

2021 ◽  
Vol 1852 (2) ◽  
pp. 022080
Author(s):  
Rui Liu ◽  
Liwu Yao ◽  
Lei Yan ◽  
Heping Li ◽  
Xiaolong Liu ◽  
...  
Keyword(s):  
3D Modelling ◽  
Real Scene

scholarly journals High-precision 3D real scene model construction method based on air-ground data combination

2020 ◽  
Vol 570 ◽  
pp. 042022
Author(s):  
Fei Yang ◽  
Chengrong Ma ◽  
Bowen Zhang ◽  
Xuannan Chen ◽  
Li Cao ◽  
...  
Keyword(s):  
High Precision ◽  
Construction Method ◽  
Model Construction ◽  
Data Combination ◽  
Real Scene
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