D
De-Shuang Huang
Researcher at Tongji University
Publications - 370
Citations - 14665
De-Shuang Huang is an academic researcher from Tongji University. The author has contributed to research in topics: Artificial neural network & Support vector machine. The author has an hindex of 60, co-authored 357 publications receiving 12345 citations. Previous affiliations of De-Shuang Huang include University of Science and Technology of China & City University of Hong Kong.
Papers
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Journal ArticleDOI
An efficient local Chan-Vese model for image segmentation
TL;DR: Comparisons with the well-known Chan-Vese (CV) model and recent popular local binary fitting (LBF) model show that the proposed LCV model can segment images with few iteration times and be less sensitive to the location of initial contour and the selection of governing parameters.
Book ChapterDOI
Robust and efficient subspace segmentation via least squares regression
TL;DR: This paper presents the Least Squares Regression (LSR) method for subspace segmentation, which takes advantage of data correlation, which is common in real data and significantly outperforms state-of-the-art methods.
Journal ArticleDOI
Palmprint Verification Based on Robust Orientation Code
Wei Jia,De-Shuang Huang +1 more
TL;DR: A novel orientation based scheme is proposed, in which three strategies, the modified finite Radon transform, enlarged training set and pixel to area matching, have been designed to further improve its performance.
Posted Content
Robust and Efficient Subspace Segmentation via Least Squares Regression
TL;DR: In this article, the Least Squares Regression (LSR) method was proposed for subspace segmentation, which takes advantage of data correlation and encourages a grouping effect which tends to group highly correlated data together.
Journal ArticleDOI
Radial basis probabilistic neural networks: model and application
TL;DR: This paper proposes a new feedforward neural network model referred to as radial basis probabilistic neural network (RBPNN), which inherits the merits of the two old odels to a great extent, and avoids their defects in some ways.