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W. K. Tang

dblp:92/1218 · DBLP profile ↗
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4ranked-venue papers
3as first author
0since 2021 · last 2006
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 2 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 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.

Artificial intelligence
1 paper
3D vision · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d reconstruction		 0.112006
Projective Reconstruction from Multiple Views with Minimization of 2D Reprojection Error · Int. J. Comput. Vis. 2006
Computer vision › 3D vision › structure from motion
bundle adjustment		 0.112006
Projective Reconstruction from Multiple Views with Minimization of 2D Reprojection Error · Int. J. Comput. Vis. 2006
Computer vision › 3D vision › 3d reconstruction
projective reconstruction		 0.112006
Projective Reconstruction from Multiple Views with Minimization of 2D Reprojection Error · Int. J. Comput. Vis. 2006
YearPublicationVenuePosition
2006 A column-space approach to projective reconstruction
W. K. Tang, Yeung Sam Hung
 Comput. Vis. Image Underst.1
2006 Projective Reconstruction from Multiple Views with Minimization of 2D Reprojection Error
Yeung Sam Hung, W. K. Tang
 Int. J. Comput. Vis.2
2006 A subspace method for projective reconstruction from multiple images with missing data
W. K. Tang, Yeung Sam Hung
 Image Vis. Comput.1
1998 Load forecasting by fuzzy neural network in Box-Jenkins models
abstract
In this paper, the use of fuzzy neural network (FNN) to identify appropriate Box-Jenkins models for the electricity load forecasting in Hong Kong is presented. FNN is found to be suitable for identifying the Box-Jenkins model. By using such model, one can forecast the load accurately.
W. K. Tang, M. H. Wong, T. S. Chung
 SMC1