T
Tien D. Bui
Researcher at Concordia University
Publications - 183
Citations - 4028
Tien D. Bui is an academic researcher from Concordia University. The author has contributed to research in topics: Image segmentation & Wavelet transform. The author has an hindex of 30, co-authored 181 publications receiving 3759 citations. Previous affiliations of Tien D. Bui include Concordia University Wisconsin & McGill University.
Papers
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Journal ArticleDOI
Translation-invariant denoising using multiwavelets
Tien D. Bui,Guangyi Chen +1 more
TL;DR: This work extends Coifman and Donoho's TI single wavelet denoising scheme to multiwavelets and Experimental results show that TI multiwavelet Denoising is better than the single case for soft thresholding.
Journal ArticleDOI
Multiwavelets denoising using neighboring coefficients
Guangyi Chen,Tien D. Bui +1 more
TL;DR: Experimental results show that this approach is better than the conventional approach, which only uses the term-by-term multiwavelet denoising, and it outperforms neighbor single wavelet Denoising for some standard test signals and real-life images.
Proceedings ArticleDOI
Investigating age invariant face recognition based on periocular biometrics
TL;DR: This paper uses unsupervised discriminant projection (UDP) to build subspaces on WLBP featured periocular images and gain 100% rank-1 identification rate and 98% verification rate at 0.1% false accept rate on the entire FG-NET database.
Proceedings ArticleDOI
Age estimation using Active Appearance Models and Support Vector Machine regression
TL;DR: A novel age estimation technique that combines Active Appearance Models (AAMs) and Support Vector Machines (SVMs) to dramatically improve the accuracy of age estimation over the current state-of-the-art techniques is introduced.
Journal ArticleDOI
Multivariate statistical modeling for image denoising using wavelet transforms
Dongwook Cho,Tien D. Bui +1 more
TL;DR: The general estimation rule in the wavelet domain is derived to obtain the denoised coefficients from the noisy image based on the multivariate statistical theory and a parametric multivariate generalized Gaussian distribution model is defined which closely fits the sample distribution.