Sparse Reconstructive Evidential Clustering for Multi-View Data

Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.

Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.

Low-Rank Multi-View Subspace Clustering Based on Sparse Regularization

Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.

Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.

ER-Net:Efficient Recalibration Network for Multi-ViewMulti-Person 3D Pose Estimation

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods,the field has entered a new stage of development.However,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method.In this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external factors.Specifically,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints.We call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.

Relational graph location network for multi-view image localization

In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.

Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation

Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency of multi-view data,while neglecting the diversity among different views as well as the high-order relationships of data,resulting in the loss of valuable complementary information.In this paper,we design a hypergraph regularized diverse deep matrix factorization(HDDMF)model for multi-view data representation,to jointly utilize multi-view diversity and a high-order manifold in a multilayer factorization framework.A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data.Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view.An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis.Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms stateof-the-art multi-view learning approaches.

Stereopsis Disorders in Patients with Congenital Nystagmus

Purpose: To investigate the stereopsis functions in the cases of congenital nystagmus (CN) and determine the relations between nystagmus, visual acuity (VA) and stereoacuity (SA).Methods: The random-dot stereograms was used to examine the patients' SA, crossed and uncrossed disparity. The oscillation types of the eyeballs were determined under the naked eyes cooperating with the electronystagmorgraphy ( ENG) examination. VA was measured at 5m and 30cm with distance and near International Standard Charts. The patient was permitted to see the target with his compensatary head posture.Results: Of the 57 patients tested, only 8 cases have normal stereopsis including normal SA,crossed and uncrossed disparity, which are all the jerk nystagmus. Forty-one have abnormal SA including 19 stereoblind, 18 abnormal stereopsis and 4 abnormal SA only. Serious damages of stereopsis exist in most of the pendular and more than half of the jerk nystagmus. The relations between stereopsis and VA are that (1)the better the

原发性上斜肌亢进眼球客观旋转与近立体视功能的分析

目的:探讨原发性上斜肌亢进(PSOOA)患者眼球客观旋转与双眼近立体视功能之间的关系。方法:回顾性研究。收集2019-01/2023-11于我院行斜视手术并存在PSOOA的斜视患者共59例,采集眼底照相图片,根据黄斑中心凹与视乳头水平经线的相对位置将眼球分为内旋、外旋和无旋转组,进一步测量黄斑-视乳头夹角(FDA),并分析眼球客观旋转情况及FDA与患者近立体视功能的关系。结果:患者59例92眼存在上斜肌亢进。眼球外旋0例,眼球无旋转32例,眼球内旋27例,眼球无旋转组双眼总FDA明显小于眼球内旋组(2.83°±2.89°vs 16.12°±5.74°,P<0.001)。眼球无旋转组和内旋组近立体视保存率分别为66%和15%,差异显著(P<0.001);眼球无旋转组和内旋组精细近立体视保存率分别为38%和11%,差异显著(P=0.02)。所有患者近立体视与双眼总FDA存在相关性(r=-0.526,P<0.001),与内旋眼FDA相关性最大(r=-0.546,P<0.001);眼球内旋组近立体视与双眼总FDA无相关性(r=-0.366,P=0.060),与内旋眼FDA和亢进眼FDA负相关(r=-0.424,P=0.028;r=-0.485,P=0.010);眼球无旋转组近立体视与双眼总FDA、主导眼FDA、非主导眼FDA、亢进眼FDA和非亢进眼FDA均无相关性。结论:PSOOA患者出现眼球内旋者较眼球无旋转者近立体视功能保存率更低,近立体视功能与双眼总FDA负相关,尤其是内旋眼和亢进眼FDA越大,近立体视功能越差。

Botulinum toxin type A in treating early-stage patients with small-angle acute acquired comitant esotropia

AIM:To investigate botulinum toxin A(BTXA)efficacy on small-angle(≤25Δ)acute acquired concomitant esotropia(AACE)in early-stage patients.METHODS:The electronic medical record data of AACE patients during March 2019 and June 2023 were collected in this retrospective and hospital-based cohort study.A total of 72 small-angle AACE patients received BTXA extraocular muscle injection.Patients were grouped by onset-to-treatment time(Group A:≤6mo,Group B:>6mo).Deviation of esotropia,eye alignment and stereopsis were analyzed at the period of pre/post-injection(1wk,1,3,and 6mo).Orthophoria rate at 6mo(horizontal deviation<10Δand binocular single vision)were considered as outcome index.RESULTS:There were no significant baseline differences(P>0.05)between two groups except onset-to-treatment time(2mo vs 11mo,P<0.001).Higher orthophoria rates were in Group A at last follow-up(94.74%vs 73.53%,P=0.013).Post-BTXA deviations of two groups at 1mo showed no difference(P>0.05);while in 3 and 6mo Group A was significantly smaller than group B(all P<0.001).No statistically significant differences were observed among all post-BTXA deviations of near and distance in Group A.In Group B,deviation at 3mo(near:2Δvs 0,P<0.001;distance:4Δvs 0,P<0.001)and 6mo(near:6Δvs 0,P<0.001;distance:6Δvs 0,P<0.001)was significant increased compared to deviation at 1wk after treatment.Group A showed better stereopsis recovery in last follow-up compared to Group B(80″vs 200″,P=0.002).Both groups obtained improved stereopsis after treatment(Group A:80″vs 300″,P<0.001;Group B:200″vs 300″,P=0.037).CONCLUSION:BTXA is effective for AACE with small deviation(≤25Δ)in early stage.Delayed treatment(>6mo)may reduce BTXA efficacy.Early BTXA intervention benefits long-term eye alignment and stereopsis recovery.

间歇性外斜视双眼视功能检查进展及其与屈光不正的关系

文章旨在全面探讨间歇性外斜视(IXT)的双眼视功能检查进展及其与屈光不正的关系。IXT作为一种常见的斜视类型,不仅影响患者的视觉体验和心理健康,还可能随年龄增长发展为恒定性外斜视。文章概述了IXT的定义、患病率和临床表现;介绍了IXT的检查方法,包括斜视度检查、眼位控制力评估以及调节与集合功能的变化。文章还探讨了IXT患者立体视的改变和其他相关检查,为IXT的全面评估提供了科学依据。此外,文章深入分析了IXT与屈光不正,尤其是近视、屈光参差和远视的相互关系,指出屈光矫正对改善双眼视功能和促进立体视功能恢复的重要性。文章旨在为临床工作提供参考,帮助医生更好地理解IXT的诊断和治疗。

基于症状管理理论的标准化护理模式结合感知觉训练对斜视矫正患者术后恢复的影响

目的:探讨基于症状管理理论的标准化护理模式结合感知觉训练对斜视矫正患者术后恢复的影响。方法:选取2019年1月-2021年1月在笔者医院接受斜视矫正手术的49例患者作为对照组,2021年2月-2023年9月接受斜视矫正手术的49例患者作为观察组。对照组采用常规护理,观察组在常规护理基础上实施基于症状管理理论的标准化护理模式结合感知觉训练。比较两组患者术后眼位、视力、立体视、并发症发生率、住院时间、满意度等指标。结果:术后,观察组患者眼位、视力、立体视均优于对照组,并发症发生率低于对照组,住院时间短于对照组,患者满意度高于对照组(P<0.05)。结论:基于症状管理理论的标准化护理模式结合感知觉训练能够有效促进斜视矫正患者术后恢复,提高眼位、视力、立体视等功能,降低并发症发生率,缩短住院时间,提高满意度,值得临床推广应用。

Interactive transport of multi-view videos for 3DTV applications

The authors propose a novel method for transporting multi-view videos that aims to keep the bandwidth requirements on both end-users and servers as low as possible. The method is based on application layer multicast, where each end point re- ceives only a selected number of views required for rendering video from its current viewpoint at any given time. The set of selected videos changes in real time as the user’s viewpoint changes because of head or eye movements. Techniques for reducing the black-outs during fast viewpoint changes were investigated. The performance of the approach was studied through network experiments.

Multi-view feature fusion for rolling bearing fault diagnosis using random forest and autoencoder

To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the time domain, frequency domain and time-frequency domain are extracted through the Fourier transform, Hilbert transform and empirical mode decomposition (EMD).Then, the random forest model (RF) is applied to select features which are highly correlated with the bearing operating state. Subsequently, the selected features are fused via the autoencoder (AE) to further reduce the redundancy. Finally, the effectiveness of the fused features is evaluated by the support vector machine (SVM). The experimental results indicate that the proposed method based on the multi-view feature fusion can effectively reflect the difference in the state of the rolling bearing, and improve the accuracy of fault diagnosis.

Feature Fusion Multi-View Hashing Based on Random Kernel Canonical Correlation Analysis

Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information more comprehensively than traditional methods using a single-view.How to use hashing to combine multi-view data for image retrieval is still a challenge.In this paper,a multi-view fusion hashing method based on RKCCA(Random Kernel Canonical Correlation Analysis)is proposed.In order to describe image content more accurately,we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT(Bag-of-Words model+SIFT feature)feature.This algorithm uses RKCCA method to fuse multi-view features to construct association features and apply them to image retrieval.The algorithm generates binary hash code with minimal distortion error by designing quantization regularization terms.A large number of experiments on benchmark datasets show that this method is superior to other multi-view hashing methods.

Color Correction for Multi-view Video Using Energy Minimization of View Networks

Systems using numerous cameras are emerging in many fields due to their ease of production and reduced cost, and one of the fields where they are expected to be used more actively in the near future is in image-based rendering （IBR）. Color correction between views is necessary to use multi-view systems in IBR to make audiences feel comfortable when views are switched or when a free viewpoint video is displayed. Color correction usually involves two steps： the first is to adjust camera parameters such as gain, brightness, and aperture before capture, and the second is to modify captured videos through image processing. This paper deals with the latter, which does not need a color pattern board. The proposed method uses scale invariant feature transform （SIFT） to detect correspondences, treats RGB channels independently, calculates lookup tables with an energy-minimization approach, and corrects captured video with these tables. The experimental results reveal that this approach works well.

Multi-view video color correction using dynamic programming

Color inconsistency between views is an important problem to be solved in multi-view video systems. A multi-view video color correction method using dynamic programming is proposed. Three-dimensional histograms are constructed with sequential conditional probability in HSI color space. Then, dynamic programming is used to seek the best color mapping relation with the minimum cost path between target image histogram and source image histogram. Finally, video tracking technique is performed to correct multi-view video. Experimental results show that the proposed method can obtain better subjective and objective performance in color correction.

A Multi-View Gait Recognition Method Using Deep Convolutional Neural Network and Channel Attention Mechanism

In many existing multi-view gait recognition methods based on images or video sequences,gait sequences are usually used to superimpose and synthesize images and construct energy-like template.However,information may be lost during the process of compositing image and capture EMG signals.Errors and the recognition accuracy may be introduced and affected respectively by some factors such as period detection.To better solve the problems,a multi-view gait recognition method using deep convolutional neural network and channel attention mechanism is proposed.Firstly,the sliding time window method is used to capture EMG signals.Then,the back-propagation learning algorithm is used to train each layer of convolution,which improves the learning ability of the convolutional neural network.Finally,the channel attention mechanism is integrated into the neural network,which will improve the ability of expressing gait features.And a classifier is used to classify gait.As can be shown from experimental results on two public datasets,OULP and CASIA-B,the recognition rate of the proposed method can be achieved at 88.44%and 97.25%respectively.As can be shown from the comparative experimental results,the proposed method has better recognition effect than several other newer convolutional neural network methods.Therefore,the combination of convolutional neural network and channel attention mechanism is of great value for gait recognition.

Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Lung is an important organ of human body.More and more people are suffering from lung diseases due to air pollution.These diseases are usually highly infectious.Such as lung tuberculosis,novel coronavirus COVID-19,etc.Lung nodule is a kind of high-density globular lesion in the lung.Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis,which is inefficient.For this reason,the use of computer-assisted diagnosis of lung nodules has become the current main trend.In the process of computer-aided diagnosis,how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research.To solve this problem,we propose a three-dimensional optimization model to achieve the extraction of suspected regions,improve the traditional deep belief network,and to modify the dispersion matrix between classes.We construct a multi-view model,fuse local three-dimensional information into two-dimensional images,and thereby to reduce the complexity of the algorithm.And alleviate the problem of unbalanced training caused by only a small number of positive samples.Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%,which is in line with clinical application standards.

Three-dimensional,isotropic imaging of mouse brain using multi-view deconvolution light sheet microscopy

We present a threedimensional(3D)isotropic imaging of mouse brain using light-sheet fuo-rescent microscopy(LSFM)in conjumction with a multi-view imaging computation.Unlike common single view LSFM is used for mouse brain imaging,the brain tissue is 3D imaged under eight views in our study,by a home-built selective plane ilumination microscopy(SPIM).An output image containing complete structural infornation as well as significantly improved res olution(~4 times)are then computed based on these eight views of data,using a bead-guided multi-view registration and deconvolution.With superior imaging quality,the astrocyte and pyrarmidal neurons together with their subcellular nerve fbers can be clearly visualized and segmented.With further incuding other computational methods,this study can be potentially scaled up to map the conectome of whole mouse brain with a simple light.sheet microscope.