no code implementations • 7 Jul 2015 • Xiao Fu, Wing-Kin Ma, José Bioucas-Dias, Tsung-Han Chan
The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing.
no code implementations • 20 Jan 2015 • Yuting Zhang, Kui Jia, Yueming Wang, Gang Pan, Tsung-Han Chan, Yi Ma
By assuming a human face as piece-wise planar surfaces, where each surface corresponds to a facial part, we develop in this paper a Constrained Part-based Alignment (CPA) algorithm for face recognition across pose and/or expression.
no code implementations • 15 Sep 2014 • Xiao Fu, Wing-Kin Ma, Tsung-Han Chan, José M. Bioucas-Dias
We then perform exact recovery analyses, and prove that the proposed greedy algorithm is robust to noise---including its identification of the (unknown) number of endmembers---under a sufficiently low noise level.
no code implementations • CVPR 2014 • Lin Sun, Kui Jia, Tsung-Han Chan, Yuqiang Fang, Gang Wang, Shuicheng Yan
In this paper, we propose to combine SFA with deep learning techniques to learn hierarchical representations from the video data itself.
2 code implementations • 14 Apr 2014 • Tsung-Han Chan, Kui Jia, Shenghua Gao, Jiwen Lu, Zinan Zeng, Yi Ma
In this work, we propose a very simple deep learning network for image classification which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms.
Ranked #48 on Image Classification on MNIST
no code implementations • 31 Mar 2014 • Kui Jia, Tsung-Han Chan, Zinan Zeng, Shenghua Gao, Gang Wang, Tianzhu Zhang, Yi Ma
The task is to identify the inlier features and establish their consistent correspondences across the image set.
no code implementations • 8 Feb 2014 • Liansheng Zhuang, Tsung-Han Chan, Allen Y. Yang, S. Shankar Sastry, Yi Ma
In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class.
no code implementations • CVPR 2013 • Zinan Zeng, Shijie Xiao, Kui Jia, Tsung-Han Chan, Shenghua Gao, Dong Xu, Yi Ma
Our framework is motivated by the observation that samples from the same class repetitively appear in the collection of ambiguously labeled training images, while they are just ambiguously labeled in each image.