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Guojun Qin
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Low-rank Matrix Optimization for Video Segmentation Research Caiyun Huang; Guojun Qin
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 1: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i1.pp36-41

Abstract

This paper investigates how to perform robust and efficient unsupervised video segmentation while suppressing the effects of data noises and/or corruptions. The low-rank representation is pursued for video segmentation. The supervoxels affinity matrix of an observed video sequence is given, low-rank matrix optimization seeks a optimal solution by making the matrix rank explicitly determined. We iteratively optimize them with closed-form solutions. Moreover, we incorporate a discriminative replication prior into our framework based on the obervation that small-size video patterns, and it tends to recur frequently within the same object. The video can be segmented into several spatio-temporal regions by applying the Normalized-Cut algorithm with the solved low-rank representation. To process the streaming videos, we apply our algorithm sequentially over a batch of frames over time, in which we also develop several temporal consistent constraints improving the robustness. Extensive experiments are on the public benchmarks, they demonstrate superior performance of our framework over other approaches．
Compressed Sensing Speech Signal Enhancement Research Kuangfeng Ning; Guojun Qin
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 1: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i1.pp26-35

Abstract

The proposed Compressive sensing method is a new alternative method, it is used to eliminate noise from the input signal, and the quality of the speech signal is enhanced with fewer samples, thus it is required for the reconstruction than needed in some of the methods like Nyquist sampling theorem. The basic idea is that the speech signals are sparse in nature, and most of the noise signals are non-sparse in nature, and Compressive Sensing(CS) eliminates the non-sparse components and it reconstructs only the sparse components of the input signal. Experimental results prove that the average segmental SNR (signal to noise ratio) and PESQ (perceptual evaluation of speech quality) scores are better in the compressed domain．
Noisy Signal Processing Research based on Compressed Sensing Technology Guojun Qin; jingfang wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i3.pp489-495

Abstract

Compressed sensing (CS) is a kind of sampling method based on signal sparse property, it can effectively extract the signal which was contained in the message. In this study, a new noise speech enhancement method was proposed based on CS process.  Voice sparsity is used to this algorithm in the discrete fast Fourier transform (Fast Fourier transform, FFT),and observation matrix is  designed in complex domain,  and the noisy speech compression measurement and de-noising are made by soft threshold,  and the speech signal is sparsely reconstructed and restored by separable approximation (Sparse Reconstruction by Separable Approximation, SpaRSA) algorithm, speech enhancementis improved.  Experimental results show that the denoising compression reconstruction is made for the noisy signal in  the algorithm, SNR margin is improved greatly, and the background noise can be more effectively suppressed .
Co-Authors Caiyun Huang Jingfang Wang Kuangfeng Ning