Optimization of the Regularization in Background and Foreground Modeling

Background and foreground modeling is a typical method in the application of computer vision. The current general “low-rank + sparse” model decomposes the frames from the video sequences into low-rank background and sparse foreground. But the sparse assumption in such a model may not conform with the reality, and the model cannot directly reflect the correlation between the background and foreground either. Thus, we present a novel model to solve this problem by decomposing the arranged data matrixDinto low-rank backgroundLand moving foregroundM. Here, we only need to give the priori assumption of the background to be low-rank and let the foreground be separated from the background as much as possible. Based on this division, we use a pair of dual norms, nuclear norm and spectral norm, to regularize the foreground and background, respectively. Furthermore, we use a reweighted function instead of the normal norm so as to get a better and faster approximation model. Detailed explanation based on linear algebra about our two models will be presented in this paper. By the observation of the experimental results, we can see that our model can get better background modeling, and even simplified versions of our algorithms perform better than mainstream techniques IALM and GoDec.

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FOREGROUND DETECTION IN VIDEO SEQUENCES WITH PROBABILISTIC SELF-ORGANIZING MAPS

International Journal of Neural Systems ◽

10.1142/s012906571100281x ◽

2011 ◽

Vol 21 (03) ◽

pp. 225-246 ◽

Cited By ~ 54

Author(s):

EZEQUIEL LÓPEZ-RUBIO ◽

RAFAEL MARCOS LUQUE-BAENA ◽

ENRIQUE DOMÍNGUEZ

Keyword(s):

Vision System ◽

Statistical Correlation ◽

Background Modeling ◽

Video Sequences ◽

Foreground Detection ◽

Computer Vision System ◽

Correlation Measure ◽

Self Organizing Maps ◽

Background Models ◽

Better Than

Background modeling and foreground detection are key parts of any computer vision system. These problems have been addressed in literature with several probabilistic approaches based on mixture models. Here we propose a new kind of probabilistic background models which is based on probabilistic self-organising maps. This way, the background pixels are modeled with more flexibility. On the other hand, a statistical correlation measure is used to test the similarity among nearby pixels, so as to enhance the detection performance by providing a feedback to the process. Several well known benchmark videos have been used to assess the relative performance of our proposal with respect to traditional neural and non neural based methods, with favourable results, both qualitatively and quantitatively. A statistical analysis of the differences among methods demonstrates that our method is significantly better than its competitors. This way, a strong alternative to classical methods is presented.

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Multi-View Laser Point Cloud Global Registration for a Single Object

Sensors ◽

10.3390/s18113729 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3729 ◽

Cited By ~ 3

Author(s):

Shuai Wang ◽

Hua-Yan Sun ◽

Hui-Chao Guo ◽

Lin Du ◽

Tian-Jian Liu

Keyword(s):

Spatial Distribution ◽

Objective Function ◽

Point Cloud ◽

Point Clouds ◽

Low Rank ◽

Single Object ◽

Loop Closure Detection ◽

Global Registration ◽

Distribution Features ◽

Better Than

Global registration is an important step in the three-dimensional reconstruction of multi-view laser point clouds for moving objects, but the severe noise, density variation, and overlap ratio between multi-view laser point clouds present significant challenges to global registration. In this paper, a multi-view laser point cloud global registration method based on low-rank sparse decomposition is proposed. Firstly, the spatial distribution features of point clouds were extracted by spatial rasterization to realize loop-closure detection, and the corresponding weight matrix was established according to the similarities of spatial distribution features. The accuracy of adjacent registration transformation was evaluated, and the robustness of low-rank sparse matrix decomposition was enhanced. Then, the objective function that satisfies the global optimization condition was constructed, which prevented the solution space compression generated by the column-orthogonal hypothesis of the matrix. The objective function was solved by the Augmented Lagrange method, and the iterative termination condition was designed according to the prior conditions of single-object global registration. The simulation analysis shows that the proposed method was robust with a wide range of parameters, and the accuracy of loop-closure detection was over 90%. When the pairwise registration error was below 0.1 rad, the proposed method performed better than the three compared methods, and the global registration accuracy was better than 0.05 rad. Finally, the global registration results of real point cloud experiments further proved the validity and stability of the proposed method.

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Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification

Sensors ◽

10.3390/s18072325 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2325 ◽

Cited By ~ 2

Author(s):

Yong Lv ◽

Houzhuang Zhang ◽

Cancan Yi

Keyword(s):

Convex Optimization ◽

Empirical Mode Decomposition ◽

Dimensional Space ◽

Real Data ◽

Rolling Bearing ◽

Low Rank ◽

Data Matrix ◽

Mode Decomposition ◽

Uniform Sample ◽

Local Mean

As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate the local mean. However, the unbalanced data distribution in high-dimensional space often conflicts with the uniform samples and its performance is sensitive to the noise components. Considering the common fact that the vibration signal is generated by three sensors located in different measuring positions in the domain of the structural health monitoring for the key equipment, thus a novel trivariate empirical mode decomposition via convex optimization was proposed for rolling bearing condition identification in this paper. For the trivariate data matrix, the low-rank matrix approximation via convex optimization was firstly conducted to achieve the denoising. It is worthy to note that the non-convex penalty function as a regularization term is introduced to enhance the performance. Moreover, the non-uniform sample scheme was determined by applying singular value decomposition (SVD) to the obtained low-rank trivariate data and then the approach used in conventional MEMD algorithm was employed to estimate the local mean. Numerical examples of synthetic defined by the fault model and real data generated by the fault rolling bearing on the experimental bench are provided to demonstrate the fruitful applications of the proposed method.

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A Non-negative Low Rank and Sparse Model for Action Recognition

Communications in Computer and Information Science - Pattern Recognition ◽

10.1007/978-3-662-45643-9_28 ◽

2014 ◽

pp. 266-275 ◽

Cited By ~ 1

Author(s):

Biyun Sheng ◽

Wankou Yang ◽

Baochang Zhang ◽

Changyin Sun

Keyword(s):

Action Recognition ◽

Low Rank ◽

Sparse Model

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A PSO Based Approach for Producing Optimized Latent Factor in Special Reference to Big Data

International Journal of Service Science Management Engineering and Technology ◽

10.4018/ijssmet.2016070104 ◽

2016 ◽

Vol 7 (3) ◽

pp. 55-70 ◽

Cited By ~ 9

Author(s):

Bharat Singh ◽

Om Prakash Vyas

Keyword(s):

Big Data ◽

Matrix Factorization ◽

Nonnegative Matrix ◽

Low Rank ◽

Data Matrix ◽

Experimental Result ◽

Low Rank Approximation ◽

Latent Factor ◽

New Approach ◽

Rank Approximation

Now a day's application deal with Big Data has tremendously been used in the popular areas. To tackle with such kind of data various approaches have been developed by researchers in the last few decades. A recent investigated techniques to factored the data matrix through a known latent factor in a lower size space is the so called matrix factorization. In addition, one of the problems with the NMF approaches, its randomized valued could not provide absolute optimization in limited iteration, but having local optimization. Due to this, the authors have proposed a new approach that considers the initial values of the decomposition to tackle the issues of computationally expensive. They have devised an algorithm for initializing the values of the decomposed matrix based on the PSO. In this paper, the auhtors have intended a genetic algorithm based technique while incorporating the nonnegative matrix factorization. Through the experimental result, they will show the proposed method converse very fast in comparison to other low rank approximation like simple NMF multiplicative, and ACLS technique.

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Improving POI Recommendation via Dynamic Tensor Completion

Scientific Programming ◽

10.1155/2018/3907804 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Jinzhi Liao ◽

Jiuyang Tang ◽

Xiang Zhao ◽

Haichuan Shang

Keyword(s):

Large Scale ◽

Real Life ◽

Location Based Services ◽

Low Rank ◽

Tensor Structure ◽

Tensor Completion ◽

Tensor Method ◽

Rank Tensor ◽

Poi Recommendation ◽

Novel Model

POI recommendation finds significant importance in various real-life applications, especially when meeting with location-based services, e.g., check-ins social networks. In this paper, we propose to solve POI recommendation through a novel model of dynamic tensor, which is among the first triumphs of its kind. In order to carry out timely recommendation, we predict POI by utilizing a completion algorithm based on fast low-rank tensor. Particularly, the dynamic tensor structure is complemented by the fast low-rank tensor completion algorithm so as to achieve prediction with better performance, where the parameter optimization is achieved by a pigeon-inspired heuristic algorithm. In short, our POI recommendation via the dynamic tensor method can take advantage of the intrinsic characteristics of check-ins data due to the multimode features such as current categories, subsequent categories, and temporal information as well as seasons variations are all integrated into the model. Extensive experiment results not only validate the superiority of our proposed method but also imply the application prospect in large-scale and real-time POI recommendation environment.

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Differentiating isoform functions with collaborative matrix factorization

Bioinformatics ◽

10.1093/bioinformatics/btz847 ◽

2019 ◽

Author(s):

Keyao Wang ◽

Jun Wang ◽

Carlotta Domeniconi ◽

Xiangliang Zhang ◽

Guoxian Yu

Keyword(s):

Matrix Factorization ◽

Characteristic Curve ◽

Function Prediction ◽

Low Rank ◽

Data Matrix ◽

Supplementary Information ◽

Genomic Databases ◽

Gene Level ◽

The Matrix ◽

Level Function

Abstract Motivation Isoforms are alternatively spliced mRNAs of genes. They can be translated into different functional proteoforms, and thus greatly increase the functional diversity of protein variants (or proteoforms). Differentiating the functions of isoforms (or proteoforms) helps understanding the underlying pathology of various complex diseases at a deeper granularity. Since existing functional genomic databases uniformly record the annotations at the gene-level, and rarely record the annotations at the isoform-level, differentiating isoform functions is more challenging than the traditional gene-level function prediction. Results Several approaches have been proposed to differentiate the functions of isoforms. They generally follow the multi-instance learning paradigm by viewing each gene as a bag and the spliced isoforms as its instances, and push functions of bags onto instances. These approaches implicitly assume the collected annotations of genes are complete and only integrate multiple RNA-seq datasets. As such, they have compromised performance. We propose a data integrative solution (called DisoFun) to Differentiate isoform Functions with collaborative matrix factorization. DisoFun assumes the functional annotations of genes are aggregated from those of key isoforms. It collaboratively factorizes the isoform data matrix and gene-term data matrix (storing Gene Ontology (GO) annotations of genes) into low-rank matrices to simultaneously explore the latent key isoforms, and achieve function prediction by aggregating predictions to their originating genes. In addition, it leverages the PPI network and GO structure to further coordinate the matrix factorization. Extensive experimental results show that DisoFun improves the AUROC (area under the receiver-operating characteristic curve) and AUPRC (area under the precision-recall curve) of existing solutions by at least 7.7% and 28.9%, respectively. We further investigate DisoFun on four exemplar genes (LMNA, ADAM15, BCL2L1, and CFLAR) with known functions at the isoform-level, and observed that DisoFun can differentiate functions of their isoforms with 90.5% accuracy. Availability The code of DisoFun is available at mlda.swu.edu.cn/codes.php?name=DisoFun. Supplementary information Supplementary data are available at Bioinformatics online.

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Developing a Novel Miniature 3D-Printed TLBS with High Mechanical Efficiency and Better Controllability

Micromachines ◽

10.3390/mi11070662 ◽

2020 ◽

Vol 11 (7) ◽

pp. 662

Author(s):

Chung-Wei Lee ◽

Jung-Hua Chou

Keyword(s):

Mechanical Efficiency ◽

Ball Screw ◽

Maximum Efficiency ◽

Design Parameters ◽

Affecting Factors ◽

Lead Screw ◽

Torque Loss ◽

3D Printed ◽

Novel Model ◽

Better Than

This paper focuses on the development of a 3D-printed threadless ball screw (TLBS) for the applications that require miniaturization, customization, and accuracy controllability. To enhance the efficiency of the TLBS, a novel model of the TLBS for analyzing the mechanical efficiency is presented to obtain the key affecting factors. From these factors, the design parameters for fabrication are determined. For miniaturization, a novel 3D-printed one-piece preloaded structure of light weight of 0.9 g is implemented as the TLBS nut part. Experimental results show that the measured mechanical efficiency of TLBS is close to that predicted by the theoretical model with a normalized root mean square error of 3.16%. In addition, the mechanical efficiency of the present TLBS (maximum efficiency close to 90%) is better than that of the lead screw and close to the ball screw. The unique characteristic of the present TLBS is that its total torque loss is a weak function of the load, a phenomenon not observed in either the ball screw or the lead screw. This characteristic is advantageous in enhancing the controllability of accuracy at different loads.

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