Yuan Yan Tang
University of Macau
683 Papers
3.7K Citations
Yuan Yan Tang is an academic researcher from University of Macau. The author has contributed to research in topics: Computer science & Wavelet. The author has an hindex of 58, co-authored 647 publications. Previous affiliations of Yuan Yan Tang include Hong Kong Community College & Southwest Baptist University.
Chat about Author
Papers
Spectral-Spatial Sparse Subspace Clustering Based on Three-Dimensional Edge-Preserving Filtering for Hyperspectral Image
TL;DR: The experimental results on three real-world HSI datasets demonstrated that the potential of including the proposed 3D EPF into the SSC framework can improve the clustering accuracy.
16
Kernel normalized mixed-norm algorithm for system identification
Shujian Yu,Xinge You,Kexin Zhao,Weihua Ou,Yuan Yan Tang +4 more
- 12 Jul 2015
TL;DR: The KNMN algorithm extends the linear mixed-norm adaptive filtering algorithms to Reproducing Kernel Hilbert Space (RKHS) and introduces a normalized step size as well as adaptive mixing parameter and conducts the mean square convergence analysis.
16
DNA Sequences Classification Based on Wavelet Packet Analysis
TL;DR: The classification of two types of DNA sequences is studied in this paper and 20 sample artificial DNA sequences whose types have been known are given to recognize the types of other DNA sequences.
16
Document architecture language (DAL) approach to document processing
C.L. Yu,Yuan Yan Tang,Ching Y. Suen +2 more
- 20 Oct 1993
TL;DR: A new document format definition language called Document Architecture Language (DAL) which can handle both rectangular and irregular blocks is presented and can be widely used in document analysis and understanding with very wide scopes.
15
Fast and accurate vanishing point detection in complex scenes
Weibin Yang,Xiaosong Luo,Bin Fang,Daiming Zhang,Yuan Yan Tang +4 more
- 20 Nov 2014
TL;DR: A novel vanishing point detection algorithm with the proposed Weber Orientation Descriptor (WOD) is introduced, which first employs the differential excitation component of WOD to extract reliable road clue regions from a complex background, and then adopts the orientation component from WOD and the proposed line-voting scheme (LVS) to locate the vanishing point.
15