Low-rank And Sparse Decomposition Research Articles

Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation.

One major limiting factor that prevents the accurate delineation of human organs has been the presence of severe pathology and pathology affecting organ borders. Overcoming these limitations is exactly what we are concerned in this study. We propose an automatic method for accurate and robust pathological organ segmentation from CT images. The method is grounded in the active shape model (ASM) framework. It leverages techniques from low-rank and sparse decomposition (LRSD) theory to robustly recover a subspace from grossly corrupted data. We first present a population-specific LRSD-based shape prior model, called LRSD-SM, to handle non-Gaussian gross errors caused by weak and misleading appearance cues of large lesions, complex shape variations, and poor adaptation to the finer local details in a unified framework. For the shape model initialization, we introduce a method based on patient-specific LRSD-based probabilistic atlas (PA), called LRSD-PA, to deal with large errors in atlas-to-target registration and low likelihood of the target organ. Furthermore, to make our segmentation framework more efficient and robust against local minima, we develop a hierarchical ASM search strategy. Our method is tested on the SLIVER07 database for liver segmentation competition, and ranks 3rd in all the published state-of-the-art automatic methods. Our method is also evaluated on some pathological organs (pathological liver and right lung) from 95 clinical CT scans and its results are compared with the three closely related methods. The applicability of the proposed method to segmentation of the various pathological organs (including some highly severe cases) is demonstrated with good results on both quantitative and qualitative experimentation; our segmentation algorithm can delineate organ boundaries that reach a level of accuracy comparable with those of human raters.

Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix.

Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix. We trained our LRS-based method on the PSICOV dataset, and tested it on both GREMLIN and CASP11 datasets. Our experimental results suggested that LRS significantly improves the contact prediction precision. For example, when equipped with the LRS technique, the prediction precision of MI and mfDCA increased from 0.25 to 0.67 and from 0.58 to 0.70, respectively (Top L/10 predicted contacts, sequence separation: 5 AA, dataset: GREMLIN). In addition, our LRS technique also consistently outperforms the popular denoising technique APC (average product correction), on both local (MI_LRS: 0.67 vs MI_APC: 0.34) and global measures (mfDCA_LRS: 0.70 vs mfDCA_APC: 0.67). Interestingly, we found out that when equipped with our LRS technique, local inference strategies performed in a comparable manner to that of global inference strategies, implying that the application of LRS technique narrowed down the performance gap between local and global inference strategies. Overall, our LRS technique greatly facilitates protein contact prediction by removing background correlations. An implementation of the approach called COLORS (improving COntact prediction using LOw-Rank and Sparse matrix decomposition) is available from http://protein.ict.ac.cn/COLORS/.

Incremental low-rank and sparse decomposition for compressing videos captured by fixed cameras

Videos captured by stationary cameras are usually with a static or gradually changed background. Existing schemes are not able to globally exploit the strong background temporal redundancy. In this paper, motivated by the recent advance on low-rank and sparse decomposition (LRSD), we propose to apply it for the compression of videos captured by fixed cameras. In particular, the LRSD is employed to decompose the input video into the low-rank component, representing the background, and the sparse component, representing the moving objects, which are encoded by different methods. Moreover, we further propose an incremental LRSD (ILRSD) algorithm to reduce the large memory requirement and high computational complexity of the existing LRSD algorithm, which facilitates the process of large-scale video sequences without much performance loss. Experimental results show that the proposed coding scheme can significantly improve the existing standard codecs, H.264/AVC and HEVC, and outperform the state-of-the-art background modeling based coding schemes.

Pansharpening Based on Low-Rank and Sparse Decomposition

This paper explores the low-rank and sparse (LRS) decomposition to solve the problem of pansharpening. By exploiting the significant correlation among the multispectral (MS) image bands, the LRS decomposition is employed as a decorrelation tool, from which the spectral and spatial informations in MS images can be separated. Based on Go Decomposition (GoDec), we provide two contributions. 1) An LRS-based pansharpening method (i.e., ImPCA) which is designed in terms of component substitution (CS) concept is given. 2) In order to improve the performance of ImPCA by reducing the spectral distortion which is characterized by the color or radiometric changes in the pansharpened images, the local dissimilarity between MS and panchromatic (PAN) images is taken into account by exploiting the context-based decision (CBD) model. Experimental results with both simulated and real data demonstrate that after the local dissimilarity is considered, the quality of the pansharpened images is significantly improved. The improved version of ImPCA is comparable with other popular methods.

Surveillance video analysis using compressive sensing with low latency

We propose a method for analysis of surveillance video by using low rank and sparse decomposition (LRSD) with low latency combined with compressive sensing to segment the background and extract moving objects in a surveillance video. Video is acquired by compressive measurements, and the measurements are used to analyze the video by a low rank and sparse decomposition of a matrix. The low rank component represents the background, and the sparse component, which is obtained in a tight wavelet frame domain, is used to identify moving objects in the surveillance video. An important feature of the proposed low latency method is that the decomposition can be performed with a small number of video frames, which reduces latency in the reconstruction and makes it possible for real time processing of surveillance video. The low latency method is both justified theoretically and validated experimentally.