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M. L. Yuan

dblp:15/6349 · DBLP profile ↗
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5ranked-venue papers
4as first author
0since 2021 · last 2007
0009-0004-6179-7700ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 5 · 4 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Geometric modeling and processing · 54% Virtual and augmented reality · 46%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › tracking
natural feature tracking
0.112006
Registration Using Natural Features for Augmented Reality Systems · IEEE Trans. Vis. Comput. Graph. 2006
Geometric modeling and processing
registration
0.112006
Registration Using Natural Features for Augmented Reality Systems · IEEE Trans. Vis. Comput. Graph. 2006
Computer vision › 3D vision › 3d reconstruction
projective reconstruction
0.112005
Registration Based on Projective Reconstruction Technique for Augmented Reality Systems · IEEE Trans. Vis. Comput. Graph. 2005
Virtual and augmented reality
augmented reality
0.112005
Registration Based on Projective Reconstruction Technique for Augmented Reality Systems · IEEE Trans. Vis. Comput. Graph. 2005
Geometric modeling and processing › registration
augmented reality registration
0.112005
Registration Based on Projective Reconstruction Technique for Augmented Reality Systems · IEEE Trans. Vis. Comput. Graph. 2005
Geometric modeling and processing › 3d reconstruction › multi-view reconstruction
projective reconstruction
0.012006
Registration Using Natural Features for Augmented Reality Systems · IEEE Trans. Vis. Comput. Graph. 2006

Methods — techniques the papers use, named apart from their topics

projective reconstruction · 0.2linear least squares · 0.1levenberg-marquardt minimization · 0.1kanade-lucas-tomasi feature tracking · 0.1
YearPublicationVenuePosition
2007 Markerless Augmented Reality Using a Robust Point Transferring Method
Soh-Khim Ong, M. L. Yuan, Andrew Y. C. Nee
MMM (2)2
2006 Registration Using Natural Features for Augmented Reality Systems
abstract
Registration is one of the most difficult problems in augmented reality (AR) systems. In this paper, a simple registration method using natural features based on the projective reconstruction technique is proposed. This method consists of two steps: embedding and rendering. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In rendering, the Kanade-Lucas-Tomasi (KLT) feature tracker is used to track the natural feature correspondences in the live video. The natural features that have been tracked are used to estimate the corresponding projective matrix in the image sequence. Next, the projective reconstruction technique is used to transfer the four specified points to compute the registration matrix for augmentation. This paper also proposes a robust method for estimating the projective matrix, where the natural features that have been tracked are normalized (translation and scaling) and used as the input data. The estimated projective matrix will be used as an initial estimate for a nonlinear optimization method that minimizes the actual residual errors based on the Levenberg-Marquardt (LM) minimization method, thus making the results more robust and stable. The proposed registration method has three major advantages: 1) It is simple, as no predefined fiducials or markers are used for registration for either indoor and outdoor AR applications. 2) It is robust, because it remains effective as long as at least six natural features are tracked during the entire augmentation, and the existence of the corresponding projective matrices in the live video is guaranteed. Meanwhile, the robust method to estimate the projective matrix can obtain stable results even when there are some outliers during the tracking process. 3) Virtual objects can still be superimposed on the specified areas, even if some parts of the areas are occluded during the entire process. Some indoor and outdoor experiments have been conducted to validate the performance of this proposed method.
M. L. Yuan, Soh-Khim Ong, Andrew Y. C. Nee
IEEE Trans. Vis. Comput. Graph.1
2005 A generalized registration method for augmented reality systems
M. L. Yuan, Soh-Khim Ong, Andrew Y. C. Nee
Comput. Graph.1
2005 Registration Based on Projective Reconstruction Technique for Augmented Reality Systems
abstract
In AR systems, registration is one of the most difficult problems currently limiting their application. In this paper, we propose a simple registration method using projective reconstruction. This method consists of two steps: embedding and tracking. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In tracking, a projective reconstruction technique is used to track these four specified points to compute the model view transformation for augmentation. This method is simple, as only four points need to be specified at the embedding stage and the virtual object can then be easily augmented onto a real scene from a video sequence. In addition, it can be extended to a scenario using the projective matrix that has been obtained from previous registration results using the same AR system. The proposed method has three advantages: 1) It is fast because the linear least square method can be used to estimate the related matrix in the algorithm and it is not necessary to calculate the fundamental matrix in the extended case. 2) A virtual object can still be superimposed on a related area even if some parts of the specified area are occluded during the whole process. 3) This method is robust because it remains effective even when not all the reference points are detected during the whole process, as long as at least six pairs of related reference points correspondences can be found. Some experiments have been conducted to validate the performance of the proposed method.
M. L. Yuan, Soh-Khim Ong, Andrew Y. C. Nee
IEEE Trans. Vis. Comput. Graph.1
2004 The virtual interaction panel: an easy control tool in augmented reality systems
abstract
Abstract In this paper, we propose and develop an easy control tool called Virtual Interaction Panel (VirIP) for Augmented Reality (AR) systems, which can be used to control AR systems. This tool is composed of two parts: the design of the VirIPs and the tracking of an interaction pen using a Restricted Coulomb Energy (RCE) neural network. The VirIP is composed of some virtual buttons, which have meaningful information that can be activated by an interaction pen during the augmentation process. The interaction pen is a general pen‐like object with a certain color distribution. It is tracked using a RCE network in real‐time and used to trigger the VirIPs for AR systems. In our system, only one camera is used for capturing the real world. Therefore, 2D information is used to trigger the virtual buttons to control the AR systems. The proposed method is real‐time because the RCE‐based image segmentation for a small region is fast. It can be used to control AR systems quite easily without any annoying sensors attached to entangling cables. This proposed method has good potential in many AR applications in manufacturing, such as assembly without the need for object recognition, collaborative product design, system control, etc. Copyright © 2004 John Wiley & Sons, Ltd.
M. L. Yuan, Soh-Khim Ong, Andrew Y. C. Nee
Comput. Animat. Virtual Worlds1