A 27mW reconfigurable marker-less logarithmic camera pose estimation engine for mobile augmented reality processor

2014 ◽  
Author(s):  
Injoon Hong ◽  
Gyeonghoon Kim ◽  
Youchang Kim ◽  
Donghyun Kim ◽  
Byeong-Gyu Nam ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Pose Estimation ◽  
Mobile Augmented Reality ◽  
Camera Pose Estimation ◽  
Camera Pose

A 27 mW Reconfigurable Marker-Less Logarithmic Camera Pose Estimation Engine for Mobile Augmented Reality Processor

2015 ◽  
Vol 50 (11) ◽  
pp. 2513-2523 ◽  
Author(s):  
Injoon Hong ◽  
Gyeonghoon Kim ◽  
Youchang Kim ◽  
Donghyun Kim ◽  
Byeong-Gyu Nam ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Pose Estimation ◽  
Mobile Augmented Reality ◽  
Camera Pose Estimation ◽  
Camera Pose

Real-Time Camera Pose Estimation for Wide-Area Augmented Reality Applications

2011 ◽  
Vol 31 (3) ◽  
pp. 56-68 ◽  
Author(s):  
Tao Guan ◽  
Liya Duan ◽  
Junqing Yu ◽  
Yongjian Chen ◽  
Xu Zhang
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Pose Estimation ◽  
Wide Area ◽  
Camera Pose Estimation ◽  
Camera Pose

Registration using adaptive particle filter and natural features matching techniques for augmented reality systems

2009 ◽  
Vol 29 (1) ◽  
pp. 75-84
Author(s):  
Guan Tao ◽  
Li Lijun ◽  
Liu Wei ◽  
Wang Cheng
Keyword(s):  
Augmented Reality ◽  
Particle Filter ◽  
Pose Estimation ◽  
Estimation Method ◽  
Scale Invariant ◽  
Registration Method ◽  
Content Type ◽  
Camera Pose Estimation ◽  
Camera Pose ◽  
Matching Techniques

PurposeThe purpose of this paper is to provide a flexible registration method for markerless augmented reality (AR) systems.Design/methodology/approachThe proposed method distinguishes itself as follows: firstly, the method is simple and efficient, as no man‐made markers are needed for both indoor and outdoor AR applications. Secondly, an adaptation method is presented to tune the particle filter dynamically. The result is a system which can achieve tolerance to fast motion and drift during tracking process. Thirdly, the authors use the reduced scale invariant feature transform (SIFT) and scale prediction techniques to match natural features. This method deals easily with the camera pose estimation problem in the case of large illumination and visual angle changes.FindingsSome experiments are provided to validate the performance of the proposed method.Originality/valueThe paper proposes a novel camera pose estimation method based on adaptive particle filter and natural features matching techniques.

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