Youjun Xiang

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Occlusion in face recognition is a common yet challenging problem. While sparse representation based classification (SRC) has been shown promising performance in laboratory conditions (i.e. noiseless or random pixel corrupted), it performs much worse in practical scenarios. In this paper, we consider the practical face recognition problem, where the(More)
Transmission of videos in error prone environments may lead to video corruption or loss. Therefore error concealment at the decoder side has to be applied. Commonly error concealment techniques make use of the surrounding correctly received image data or motion information for concealment. In this paper, a novel spatio-temporal boundary matching algorithm(More)
The highly error-prone channel and limited computational power of terminal devices necessitates the implementation of robust yet simple error concealment. Error concealment techniques commonly make use of the surrounding correctly received image data or motion information for concealment. In this paper, we propose an efficient spatio-temporal boundary(More)
In this paper, a block version of the orthogonal matching pursuit with thresholding algorithm is proposed. Compared with the block version of the orthogonal matching pursuit algorithm, the block orthogonal matching pursuit algorithm works in a less greedy fashion in order to improve support estimating efficiency in each iteration. The lower and upper bounds(More)
Occlusion is a common yet challenging problem in face recognition. Most of the existing approaches cannot achieve the accuracy of the recognition with high efficiency in the occlusion case. To address this problem, this paper proposes a novel algorithm, called Efficient Locality-constrained Occlusion Coding (ELOC), improving the previous Sparse Error(More)
In this letter, robust sparse signal recovery is considered in the presence of the symmetric α-stable distributed noise. An M-estimate type model is constructed by approximating the location score function of the noise. A reweighed iterative hard thresholding algorithm is proposed to recover the sparse signal. The basis functions for the(More)
A new algorithm is proposed for compressed sensing-magnetic resonance imaging (CS-MRI). The <inline-formula> <tex-math notation="LaTeX">$l_p$</tex-math></inline-formula>-norm <inline-formula><tex-math notation="LaTeX">$(0 &lt; p \leq 1)$</tex-math></inline-formula> based adaptive regularization model is used for MRI. The algorithm is established by using a(More)