Augmented Reality Recognition Registration Method Based on Text Features

2020 ◽  
Vol 57 (2) ◽  
pp. 021502
Author(s):  
李雪婷 Li Xueting ◽  
党建武 Dang Jianwu ◽  
王阳萍 Wang Yangping ◽  
高凡一 Gao Fanyi
Keyword(s):  
Augmented Reality ◽  
Registration Method ◽  
Text Features

scholarly journals Robust augmented reality registration method for localization of solid organs’ tumors using CT-derived virtual biomechanical model and fluorescent fiducials

2016 ◽  
Vol 31 (7) ◽  
pp. 2863-2871 ◽  
Author(s):  
Seong-Ho Kong ◽  
Nazim Haouchine ◽  
Renato Soares ◽  
Andrey Klymchenko ◽  
Bohdan Andreiuk ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Biomechanical Model ◽  
Registration Method

scholarly journals Augmented reality system using lidar point cloud data for displaying dimensional information of objects on mobile phones

2014 ◽  
Vol II-5 ◽  
pp. 153-159 ◽  
Author(s):  
S. Gupta ◽  
B. Lohani
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Smart Phone ◽  
Research Problem ◽  
Point Cloud Data ◽  
Registration Method ◽  
Cloud Data ◽  
Augmented Reality System ◽  
The Status ◽  
Intensity Image

Mobile augmented reality system is the next generation technology to visualise 3D real world intelligently. The technology is expanding at a fast pace to upgrade the status of a smart phone to an intelligent device. The research problem identified and presented in the current work is to view actual dimensions of various objects that are captured by a smart phone in real time. The methodology proposed first establishes correspondence between LiDAR point cloud, that are stored in a server, and the image t hat is captured by a mobile. This correspondence is established using the exterior and interior orientation parameters of the mobile camera and the coordinates of LiDAR data points which lie in the viewshed of the mobile camera. A pseudo intensity image is generated using LiDAR points and their intensity. Mobile image and pseudo intensity image are then registered using image registration method SIFT thereby generating a pipeline to locate a point in point cloud corresponding to a point (pixel) on the mobile image. The second part of the method uses point cloud data for computing dimensional information corresponding to the pairs of points selected on mobile image and fetch the dimensions on top of the image. This paper describes all steps of the proposed method. The paper uses an experimental setup to mimic the mobile phone and server system and presents some initial but encouraging results

scholarly journals An Automatic Marker–Object Offset Calibration Method for Precise 3D Augmented Reality Registration in Industrial Applications

2019 ◽  
Vol 9 (20) ◽  
pp. 4464 ◽  
Author(s):  
Xuyue Yin ◽  
Xiumin Fan ◽  
Xu Yang ◽  
Shiguang Qiu ◽  
Zhinan Zhang
Keyword(s):  
Augmented Reality ◽  
Calibration Method ◽  
Industrial Applications ◽  
Target Object ◽  
Initial Position ◽  
Normal Direction ◽  
3D Image ◽  
3D Tracking ◽  
Registration Method ◽  
Calibration Tool

Industrial augmented reality (AR) applications demand high on the visual consistency of virtual-real registration. To present, the marker-based registration method is most popular because it is fast, robust, and convenient to obtain the registration matrix. In practice, the registration matrix should multiply an offset matrix that describes the transformation between the attaching position and the initial position of the marker relative to the object. However, the offset matrix is usually measured, calculated, and set manually, which is not accurate and convenient. This paper proposes an accurate and automatic marker–object offset matrix calibration method. First, the normal direction of the target object is obtained by searching and matching the top surface of the CAD model. Then, the spatial translation is estimated by aligning the projected and the imaged top surface. Finally, all six parameters of the offset matrix are iteratively optimized using a 3D image alignment framework. Experiments were performed on the publicity monocular rigid 3D tracking dataset and an automobile gearbox. The average translation and rotation errors of the optimized offset matrix are 2.10 mm and 1.56 degree respectively. The results validate that the proposed method is accurate and automatic, which contributes to a universal offset matrix calibration tool for marker-based industrial AR applications.

scholarly journals Enhancement the Augmented Reality Framework Using Proposed Registration Method

2017 ◽  
Vol 20 (2) ◽  
pp. 153-161
Author(s):  
Yossra Hussain Ali ◽  
◽  
Abdul Ameer Abdulla ◽  
Ikhlas Watan Ghindawi ◽  
◽  
...  
Keyword(s):  
Augmented Reality ◽  
Registration Method
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