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.
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Papers
Modified fractal signature (MFS): a new approach to document analysis for automatic knowledge acquisition
TL;DR: This paper presents a new approach to document analysis for automatic knowledge acquisition based on modified fractal signature that can overcome the traditional approaches' weaknesses.
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Multi-View Classification via a Fast and Effective Multi-View Nearest-Subspace Classifier
Ting Shu,Bob Zhang,Yuan Yan Tang +2 more
TL;DR: This paper proposes a fast and effective multi-view nearest-subspace classifier (MV-NSC) by taking advantage of both the two relationships simultaneously, and shows that the proposed method is effective, efficient, and robust in multi-View classification.
Handwritten Character Recognition Using Combined Gradient and Wavelet Feature
Weipeng Zhang,Yuan Yan Tang,Yun Xue +2 more
- 01 Nov 2006
TL;DR: A novel feature based on the combination of gradient feature and coefficients of wavelet transform and composed local and global characteristic in a combined feature to improve the discrimination power in handwritten character recognition.
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A fast convex hull algorithm with maximum inscribed circle affine transformation
TL;DR: This paper presents a fast convex hull algorithm for a large point set that imitates the procedure of human visual attention derived in a psychological experiment and proposes to use an affine transformation to solve the narrow shape problem for computing the conveX hull faster.
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A contourlet-based method for writer identification
Zhenyu He,Yuan Yan Tang,Xinge You +2 more
- 10 Oct 2005
TL;DR: This paper focuses on the text-independent writer identification based on off-line Chinese handwriting and presents a new contourlet-based GGD (Generalized Gaussian Density) method, which achieves a good experiment result in the authors' experiments.
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