Journal articles: 'Multiview2'

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Avison, D. E., A. T. Wood‐Harper, R. T. Vidgen, and J. R. G. Wood. "A further exploration into information systems development: the evolution of Multiview2." Information Technology & People 11, no. 2 (June 1998): 124–39. http://dx.doi.org/10.1108/09593849810218319.

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Zhu, Shiping, Liyun Li, Juqiang Chen, and Kamel Belloulata. "An Efficient Fractal Video Sequences Codec with Multiviews." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/853283.

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Multiview video consists of multiple views of the same scene. They require enormous amount of data to achieve high image quality, which makes it indispensable to compress multiview video. Therefore, data compression is a major issue for multiviews. In this paper, we explore an efficient fractal video codec to compress multiviews. The proposed scheme first compresses a view-dependent geometry of the base view using fractal video encoder with homogeneous region condition. With the extended fractional pel motion estimation algorithm and fast disparity estimation algorithm, it then generates prediction images of other views. The prediction image uses the image-based rendering techniques based on the decoded video. And the residual signals are obtained by the prediction image and the original image. Finally, it encodes residual signals by the fractal video encoder. The idea is also to exploit the statistical dependencies from both temporal and interview reference pictures for motion compensated prediction. Experimental results show that the proposed algorithm is consistently better than JMVC8.5, with 62.25% bit rate decrease and 0.37 dB PSNR increase based on the Bjontegaard metric, and the total encoding time (TET) of the proposed algorithm is reduced by 92%.

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Li, Peng, Zhikui Chen, Jing Gao, Jianing Zhang, Shan Jin, Wenhan Zhao, Feng Xia, and Lu Wang. "A Deep Fusion Gaussian Mixture Model for Multiview Land Data Clustering." Wireless Communications and Mobile Computing 2020 (October 16, 2020): 1–9. http://dx.doi.org/10.1155/2020/8880430.

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With the rapid industrialization and urbanization, pattern mining of soil contamination of heavy metals is attracting increasing attention to control soil contamination. However, the correlation over various heavy metals and the high-dimension representation of heavy metal data pose vast challenges on the accurate mining of patterns over heavy metals of soil contamination. To solve those challenges, a multiview Gaussian mixture model is proposed in this paper, to naturally capture complicated relationships over multiviews on the basis of deep fusion features of data. Specifically, a deep fusion feature architecture containing modality-specific and modality-common stacked autoencoders is designed to distill fusion representations from the information of all views. Then, the Gaussian mixture model is extended on the fusion representations to naturally recognize the accurate patterns of the intra- and inter-views. Finally, extensive experiments are conducted on the representative datasets to evaluate the performance of the multiview Gaussian mixture model. Results show the outperformance of the proposed methods.

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Chen, Feiqiong, Guopeng Li, Shuaihui Wang, and Zhisong Pan. "Multiview Clustering via Robust Neighboring Constraint Nonnegative Matrix Factorization." Mathematical Problems in Engineering 2019 (November 23, 2019): 1–10. http://dx.doi.org/10.1155/2019/6084382.

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Many real-world datasets are described by multiple views, which can provide complementary information to each other. Synthesizing multiview features for data representation can lead to more comprehensive data description for clustering task. However, it is often difficult to preserve the locally real structure in each view and reconcile the noises and outliers among views. In this paper, instead of seeking for the common representation among views, a novel robust neighboring constraint nonnegative matrix factorization (rNNMF) is proposed to learn the neighbor structure representation in each view, and L2,1-norm-based loss function is designed to improve its robustness against noises and outliers. Then, a final comprehensive representation of data was integrated with those representations of multiviews. Finally, a neighboring similarity graph was learned and the graph cut method was used to partition data into its underlying clusters. Experimental results on several real-world datasets have shown that our model achieves more accurate performance in multiview clustering compared to existing state-of-the-art methods.

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Chang, Yan-Shuo, Feiping Nie, and Ming-Yu Wang. "Multiview Feature Analysis via Structured Sparsity and Shared Subspace Discovery." Neural Computation 29, no. 7 (July 2017): 1986–2003. http://dx.doi.org/10.1162/neco_a_00977.

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Since combining features from heterogeneous data sources can significantly boost classification performance in many applications, it has attracted much research attention over the past few years. Most of the existing multiview feature analysis approaches separately learn features in each view, ignoring knowledge shared by multiple views. Different views of features may have some intrinsic correlations that might be beneficial to feature learning. Therefore, it is assumed that multiviews share subspaces from which common knowledge can be discovered. In this letter, we propose a new multiview feature learning algorithm, aiming to exploit common features shared by different views. To achieve this goal, we propose a feature learning algorithm in a batch mode, by which the correlations among different views are taken into account. Multiple transformation matrices for different views are simultaneously learned in a joint framework. In this way, our algorithm can exploit potential correlations among views as supplementary information that further improves the performance result. Since the proposed objective function is nonsmooth and difficult to solve directly, we propose an iterative algorithm for effective optimization. Extensive experiments have been conducted on a number of real-world data sets. Experimental results demonstrate superior performance in terms of classification against all the compared approaches. Also, the convergence guarantee has been validated in the experiment.

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Wang, Haiyan, Guoqiang Han, Haojiang Li, Guihua Tao, Enhong Zhuo, Lizhi Liu, Hongmin Cai, and Yangming Ou. "A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences." Computational and Mathematical Methods in Medicine 2020 (August 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/7562140.

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Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.

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Kanaan-Izquierdo, Samir, Andrey Ziyatdinov, Maria Araceli Burgueño, and Alexandre Perera-Lluna. "Multiview: a software package for multiview pattern recognition methods." Bioinformatics 35, no. 16 (December 31, 2018): 2877–79. http://dx.doi.org/10.1093/bioinformatics/bty1039.

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Abstract Summary Multiview datasets are the norm in bioinformatics, often under the label multi-omics. Multiview data are gathered from several experiments, measurements or feature sets available for the same subjects. Recent studies in pattern recognition have shown the advantage of using multiview methods of clustering and dimensionality reduction; however, none of these methods are readily available to the extent of our knowledge. Multiview extensions of four well-known pattern recognition methods are proposed here. Three multiview dimensionality reduction methods: multiview t-distributed stochastic neighbour embedding, multiview multidimensional scaling and multiview minimum curvilinearity embedding, as well as a multiview spectral clustering method. Often they produce better results than their single-view counterparts, tested here on four multiview datasets. Availability and implementation R package at the B2SLab site: http://b2slab.upc.edu/software-and-tutorials/ and Python package: https://pypi.python.org/pypi/multiview. Supplementary information Supplementary data are available at Bioinformatics online.

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Zekovic, Amela, and Irini Reljin. "Multifractal analysis of multiview 3D video with different quantization parameters applying histogram method." Serbian Journal of Electrical Engineering 11, no. 1 (2014): 25–34. http://dx.doi.org/10.2298/sjee131130003z.

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In this paper, multifractal properties of multiview 3D video are determined. Multifractal spectra are determined by using the histogram method. For the analysis of multiview video, long video traces are used, for multiview video with two views. Differences between multifractal properties of different views of multiview video and different types of frames are highlighted. Additional analysis was performed for the left view of multiview 3D videos for different quantization parameters of the frames.

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Pei, Jifang, Weibo Huo, Chenwei Wang, Yulin Huang, Yin Zhang, Junjie Wu, and Jianyu Yang. "Multiview Deep Feature Learning Network for SAR Automatic Target Recognition." Remote Sensing 13, no. 8 (April 9, 2021): 1455. http://dx.doi.org/10.3390/rs13081455.

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Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

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Gu, Yi, and Kang Li. "Entropy-Based Multiview Data Clustering Analysis in the Era of Industry 4.0." Wireless Communications and Mobile Computing 2021 (April 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/9963133.

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In the era of Industry 4.0, single-view clustering algorithm is difficult to play a role in the face of complex data, i.e., multiview data. In recent years, an extension of the traditional single-view clustering is multiview clustering technology, which is becoming more and more popular. Although the multiview clustering algorithm has better effectiveness than the single-view clustering algorithm, almost all the current multiview clustering algorithms usually have two weaknesses as follows. (1) The current multiview collaborative clustering strategy lacks theoretical support. (2) The weight of each view is averaged. To solve the above-mentioned problems, we used the Havrda-Charvat entropy and fuzzy index to construct a new collaborative multiview fuzzy c-means clustering algorithm using fuzzy weighting called Co-MVFCM. The corresponding results show that the Co-MVFCM has the best clustering performance among all the comparison clustering algorithms.

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Zhao, Guotao, and Jie Ding. "Image Network Teaching Resource Retrieval Algorithm Based on Deep Hash Algorithm." Scientific Programming 2021 (October 11, 2021): 1–7. http://dx.doi.org/10.1155/2021/9683908.

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In order to improve the retrieval ability of multiview attribute coded image network teaching resources, a retrieval algorithm of image network teaching resources based on depth hash algorithm is proposed. The pixel big data detection model of the multiview attribute coding image network teaching resources is constructed, the pixel information collected by the multiview attribute coding image network teaching resources is reconstructed, the fuzzy information feature components of the multiview attribute coding image are extracted, and the edge contour distribution image is combined. The distributed fusion result of the edge contour of the view image of the network teaching resources realizes the construction of the view feature parameter set. The gray moment invariant feature analysis method is used to realize information coding, the depth hash algorithm is used to realize the retrieval of multiview attribute coded image network teaching resources, and the information recombination is realized according to the hash coding result of multiview attribute coded image network teaching resources, thus improving the fusion. The simulation results show that this method has higher precision, better retrieval precision, and higher level of resource fusion for multiview coded image network teaching resource retrieval.

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Leong, Chi Wa, Behnoosh Hariri, and Shervin Shirmohammadi. "Exploiting Orientational Redundancy in Multiview Video Compression." International Journal of Computer and Electrical Engineering 7, no. 2 (2015): 70–81. http://dx.doi.org/10.17706/ijcee.2015.v7.873.

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You, Cong-Zhe, and Xiao-Jun Wu. "Co-regularized weighting multiview clustering." Journal of Algorithms & Computational Technology 11, no. 3 (April 11, 2017): 217–23. http://dx.doi.org/10.1177/1748301817701027.

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This paper deals with clustering for multiview data. Multiview clustering has been a research hot spot in many domains or applications, such as information retrieval, biology, chemistry, and marketing. Exploring information from multiple views, one can hope to find a clustering that is more accurate than the ones obtained using the individual views. The aim is to search for clustering patterns that perform a consensus between the patterns from different views. Inspired by variable weighting and co-regularized strategy, this paper studies co-regularized weighting multiview clustering algorithms. Two co-regularized weighting multiview clustering algorithms are proposed from two aspects: pairwise co-regularization and centroid-based co-regularization. Experimental results obtained both on synthetic and real datasets show that the proposed algorithms outperform the main existing multiview clustering algorithms.

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Shi, Yaxin, Yuangang Pan, Donna Xu, and Ivor W. Tsang. "Multiview Alignment and Generation in CCA via Consistent Latent Encoding." Neural Computation 32, no. 10 (October 2020): 1936–79. http://dx.doi.org/10.1162/neco_a_01309.

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Multiview alignment, achieving one-to-one correspondence of multiview inputs, is critical in many real-world multiview applications, especially for cross-view data analysis problems. An increasing amount of work has studied this alignment problem with canonical correlation analysis (CCA). However, existing CCA models are prone to misalign the multiple views due to either the neglect of uncertainty or the inconsistent encoding of the multiple views. To tackle these two issues, this letter studies multiview alignment from a Bayesian perspective. Delving into the impairments of inconsistent encodings, we propose to recover correspondence of the multiview inputs by matching the marginalization of the joint distribution of multiview random variables under different forms of factorization. To realize our design, we present adversarial CCA (ACCA), which achieves consistent latent encodings by matching the marginalized latent encodings through the adversarial training paradigm. Our analysis, based on conditional mutual information, reveals that ACCA is flexible for handling implicit distributions. Extensive experiments on correlation analysis and cross-view generation under noisy input settings demonstrate the superiority of our model.

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Li, Shicheng, Qinghua Liu, Jiangyan Dai, Wenle Wang, Xiaolin Gui, and Yugen Yi. "Adaptive-Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis." Wireless Communications and Mobile Computing 2021 (June 5, 2021): 1–12. http://dx.doi.org/10.1155/2021/5526479.

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Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

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Zhu, Shiping, Dongyu Zhao, and Ling Zhang. "A Novel High Efficiency Fractal Multiview Video Codec." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/613714.

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Multiview video which is one of the main types of three-dimensional (3D) video signals, captured by a set of video cameras from various viewpoints, has attracted much interest recently. Data compression for multiview video has become a major issue. In this paper, a novel high efficiency fractal multiview video codec is proposed. Firstly, intraframe algorithm based on the H.264/AVC intraprediction modes and combining fractal and motion compensation (CFMC) algorithm in which range blocks are predicted by domain blocks in the previously decoded frame using translational motion with gray value transformation is proposed for compressing the anchor viewpoint video. Then temporal-spatial prediction structure and fast disparity estimation algorithm exploiting parallax distribution constraints are designed to compress the multiview video data. The proposed fractal multiview video codec can exploit temporal and spatial correlations adequately. Experimental results show that it can obtain about 0.36 dB increase in the decoding quality and 36.21% decrease in encoding bitrate compared with JMVC8.5, and the encoding time is saved by 95.71%. The rate-distortion comparisons with other multiview video coding methods also demonstrate the superiority of the proposed scheme.

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Ge, Hongwei, Zehang Yan, Jing Dou, Zhen Wang, and ZhiQiang Wang. "A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization." Mathematical Problems in Engineering 2018 (June 27, 2018): 1–11. http://dx.doi.org/10.1155/2018/5987906.

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Automatic image annotation is for more accurate image retrieval and classification by assigning labels to images. This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images. First, we construct a graph embedding term in the multiview NMF based on the association diagrams between labels for semantic constraints. Then, the multiview features are fused and dimensions are reduced based on multiview NMF algorithm. Finally, image annotation is achieved by using the new features through a KNN-based approach. Experiments validate that the proposed algorithm has achieved competitive performance in terms of accuracy and efficiency.

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Heo, Daerak, Sungjin Lim, Gunhee Lee, Geunseop Choi, and Joonku Hahn. "Full-Parallax Multiview Generation with High-Speed Wide-Angle Dual-Axis Scanning Optics." Applied Sciences 12, no. 9 (May 4, 2022): 4615. http://dx.doi.org/10.3390/app12094615.

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Three-dimensional displays are receiving considerable attention owing to their ability to deliver realistic content. Particularly, a multiview display with temporal multiplexing offers advantages in terms of fewer restrictions for optical alignment and flexibility in forming view density. However, most of studies realize horizontal parallax-only multiview display. In a horizontal parallax-only multiview display the content is distorted in the vertical direction as the observer changes the viewing distance. It is helpful to understand this phenomenon using the Wigner distribution function (WDF). In this study, we divided the viewing zone (VZ) into the sub-viewing zone and integrated viewing zone according to the number of views of the observer. Specifically, the changes in the contents are experimentally evaluated at different viewing distances to validate our expectation. For the experiment, we implemented a full-parallax multiview display with spherical symmetry and designed a high-speed wide-angle dual-axis scanner. This scanner comprises two single-axis scanners connected by high numerical-aperture scanning optics. The proposed system and WDF analysis of VZ will be helpful to evaluate the characteristics of the multiview system.

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Guo, Peng, Guoqi Xie, and Renfa Li. "Object Detection Using Multiview CCA-Based Graph Spectral Learning." Journal of Circuits, Systems and Computers 29, no. 02 (April 23, 2019): 2050022. http://dx.doi.org/10.1142/s021812662050022x.

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Recent years have witnessed a surge of interest in semi-supervised learning-based object detection. Object detection is usually accomplished by classification methods. Different from conventional methods, those usually adopt a single feature view or concatenate multiple features into a long feature vector, multiview graph spectral learning can attain simultaneously object classification and weight learning of multiview. However, most existing multiview graph spectral learning (GSL) methods are only concerned with the complementarities between multiple views but not with correlation information. Accurately representing image objects is difficult because there are multiple views simultaneously for an image object. Thus, we offer a GSL method based on multiview canonical correlation analysis (GSL-MCCA). The method adds MCCA regularization term to a graph learning framework. To enable MCCA to reveal the nonlinear correlation information hidden in multiview data, manifold local structure information is incorporated into MCCA. Thus, GSL-MCCA can lead to simultaneous selection of relevant features and learning transformation. Experimental evaluations based on Corel and VOC datasets suggest the effectiveness of GSL-MCCA in object detection.

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Ye, Yongkai, Xinwang Liu, Qiang Liu, and Jianping Yin. "Consensus Kernel K-Means Clustering for Incomplete Multiview Data." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3961718.

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Multiview clustering aims to improve clustering performance through optimal integration of information from multiple views. Though demonstrating promising performance in various applications, existing multiview clustering algorithms cannot effectively handle the view’s incompleteness. Recently, one pioneering work was proposed that handled this issue by integrating multiview clustering and imputation into a unified learning framework. While its framework is elegant, we observe that it overlooks the consistency between views, which leads to a reduction in the clustering performance. In order to address this issue, we propose a new unified learning method for incomplete multiview clustering, which simultaneously imputes the incomplete views and learns a consistent clustering result with explicit modeling of between-view consistency. More specifically, the similarity between each view’s clustering result and the consistent clustering result is measured. The consistency between views is then modeled using the sum of these similarities. Incomplete views are imputed to achieve an optimal clustering result in each view, while maintaining between-view consistency. Extensive comparisons with state-of-the-art methods on both synthetic and real-world incomplete multiview datasets validate the superiority of the proposed method.

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Yuan, Jianying, Qiong Wang, Xiaoliang Jiang, and Bailin Li. "A High-Precision Registration Technology Based on Bundle Adjustment in Structured Light Scanning System." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/897347.

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The multiview 3D data registration precision will decrease with the increasing number of registrations when measuring a large scale object using structured light scanning. In this paper, we propose a high-precision registration method based on multiple view geometry theory in order to solve this problem. First, a multiview network is constructed during the scanning process. The bundle adjustment method from digital close range photogrammetry is used to optimize the multiview network to obtain high-precision global control points. After that, the 3D data under each local coordinate of each scan are registered with the global control points. The method overcomes the error accumulation in the traditional registration process and reduces the time consumption of the following 3D data global optimization. The multiview 3D scan registration precision and efficiency are increased. Experiments verify the effectiveness of the proposed algorithm.

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de La Beaujardiere, J. F., Horace Mitchell, and A. Fritz Hasler. "MultiView (abstract)." ACM SIGWEB Newsletter 5, no. 2 (June 1996): 32. http://dx.doi.org/10.1145/231738.232623.

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Carballeira, Pablo, Jesus Gutierrez, Francisco Moran, Julian Cabrera, Fernando Jaureguizar, and Narciso Garcia. "MultiView Perceptual Disparity Model for Super MultiView Video." IEEE Journal of Selected Topics in Signal Processing 11, no. 1 (February 2017): 113–24. http://dx.doi.org/10.1109/jstsp.2016.2617302.

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Yao, Tuozhong, Wenfeng Wang, and Yuhong Gu. "A Deep Multiview Active Learning for Large-Scale Image Classification." Mathematical Problems in Engineering 2020 (December 14, 2020): 1–7. http://dx.doi.org/10.1155/2020/6639503.

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Multiview active learning (MAL) is a technique which can achieve a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. In this paper, we present a new deep multiview active learning (DMAL) framework which is the first to combine multiview active learning and deep learning for annotation effort reduction. In this framework, our approach advances the existing active learning methods in two aspects. First, we incorporate two different deep convolutional neural networks into active learning which uses multiview complementary information to improve the feature learnings. Second, through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. The experiments with two challenging image datasets demonstrate that our proposed DMAL algorithm can achieve promising results than several state-of-the-art active learning algorithms.

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Rioja, M., R. Dodson, G. Orosz, and H. Imai. "MultiView High Precision VLBI Astrometry at Low Frequencies." Proceedings of the International Astronomical Union 13, S336 (September 2017): 439–42. http://dx.doi.org/10.1017/s1743921317010560.

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AbstractObservations at low frequencies (<8GHz) are dominated by distinct direction dependent ionospheric propagation errors, which place a very tight limit on the angular separation of a suitable phase referencing calibrator and astrometry. To increase the capability for high precision astrometric measurements an effective calibration strategy of the systematic ionospheric propagation effects that is widely applicable is required. The MultiView technique holds the key to the compensation of atmospheric spatial-structure errors, by using observations of multiple calibrators and two dimensional interpolation. In this paper we present the first demonstration of the power of MultiView using three calibrators, several degrees from the target, along with a comparative study of the astrometric accuracy between MultiView and phase-referencing techniques. MultiView calibration provides an order of magnitude improvement in astrometry with respect to conventional phase referencing, achieving ~100micro-arcseconds astrometry errors in a single epoch of observations, effectively reaching the thermal noise limit.

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Lou, Jian-Guang, Hua Cai, and Jiang Li. "Interactive Multiview Video Delivery Based on IP Multicast." Advances in Multimedia 2007 (2007): 1–8. http://dx.doi.org/10.1155/2007/97535.

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As a recently emerging service, multiview video provides a new viewing experience with a high degree of freedom. However, due to the huge data amounts transferred, multiview video's delivery remains a daunting challenge. In this paper, we propose a multiview video-streaming system based on IP multicast. It can support a large number of users while still maintaining a high degree of interactivity and low bandwidth consumption. Based on a careful user study, we have developed two schemes: one is for automatic delivery and the other for on-demand delivery. In automatic delivery, a server periodically multicasts special effect snapshots at a certain time interval. In on-demand delivery, the server delivers the snapshots based on distribution of user requests. We conducted extensive experiments and user-experience studies to evaluate the proposed system's performance, and found that it provides satisfying multiview video service for users on a large scale.

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Li, Xinning, Hu Wu, Xianhai Yang, Peng Xue, and Shuai Tan. "Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network." Journal of Robotics 2021 (July 7, 2021): 1–13. http://dx.doi.org/10.1155/2021/3584422.

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In order to better realize the orchard intelligent mechanization and reduce the labour intensity of workers, the study of intelligent fruit boxes handling robot is necessary. The first condition to realize intelligence is the fruit boxes recognition, which is the research content of this paper. The method of multiview two-dimensional (2D) recognition was adopted. A multiview dataset for fruits boxes was built. For the sake of the structure of the original image, the model of binary multiview 2D kernel principal component analysis network (BM2DKPCANet) was established to reduce the data redundancy and increase the correlation between the views. The method of multiview recognition for the fruits boxes was proposed combining BM2DKPCANet with the support vector machine (SVM) classifier. The performance was verified by comparing with principal component analysis network (PCANet), 2D principal component analysis network (2DPCANet), kernel principal component analysis network (KPCANet), and binary multiview kernel principal component analysis network (BMKPCANet) in terms of recognition rate and time consumption. The experimental results show that the recognition rate of the method is 11.84% higher than the mean value of PCANet though it needs more time. Compared with the mean value of KPCANet, the recognition rate exceeded 2.485%, and the time saved was 24.5%. The model can meet the requirements of fruits boxes handling robot.

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Lv, Bailin, and Yizhang Jiang. "Prediction of Short-Term Stock Price Trend Based on Multiview RBF Neural Network." Computational Intelligence and Neuroscience 2021 (November 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/8495288.

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Stock price prediction is important in both financial and commercial domains, and using neural networks to forecast stock prices has been a topic of ongoing research and development. Traditional prediction models are often based on a single type of data and do not account for the interplay of many variables. This study covers a radial basis neural network modeling technique with multiview collaborative learning capabilities for incorporating the impacts of numerous elements into the prediction model. This research offers a multiview RBF neural network prediction model based on the classic RBF network by integrating a collaborative learning item with multiview learning capabilities (MV-RBF). MV-RBF can make full use of both the internal information provided by the correlation between each view and the distinct characteristics of each view to form independent sample information. By using two separate stock qualities as input feature information for trials, this study proves the viability of the multiview RBF neural network prediction model on a real data set.

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Sun, Xiaoqi, Wenxi Gao, and Yinong Duan. "MR Brain Image Segmentation Using a Fuzzy Weighted Multiview Possibility Clustering Algorithm with Low-Rank Constraints." Journal of Medical Imaging and Health Informatics 11, no. 2 (February 1, 2021): 402–8. http://dx.doi.org/10.1166/jmihi.2021.3280.

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To expand the multiview clustering abilities of traditional PCM in increasingly complex MR brain image segmentation tasks, a fuzzy weighted multiview possibility clustering algorithm with low-rank constraints (LR-FW-MVPCM) is proposed. The LR-FW-MVPCM can effectively mine both the internal consistency and diversity of multiview data, which are two principles for constructing a multiview clustering algorithm. First, a kernel norm is introduced as a low-rank constraint of the fuzzy membership matrix among multiple perspectives. Second, to ensure the clustering accuracy of the algorithm, the view fuzzy weighted mechanism is introduced to the framework of possibility c-means clustering, and the weights of each view are adaptively allocated during the iterative optimization process. The segmentation results of different brain tissues based on the proposed algorithm and three other algorithms illustrate that the LR-FW-MVPCM algorithm can segment MR brain images much more effectively and ensure better segmentation performance.

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Qin, Xiujuan, Xinzhu Sang, Hui Li, Rui Xiao, Chongli Zhong, Binbin Yan, Zhi Sun, and Yu Dong. "High Resolution Multiview Holographic Display Based on the Holographic Optical Element." Micromachines 14, no. 1 (January 6, 2023): 147. http://dx.doi.org/10.3390/mi14010147.

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Limited by the low space-bandwidth product of the spatial light modulator (SLM), it is difficult to realize multiview holographic three-dimensional (3D) display. To conquer the problem, a method based on the holographic optical element (HOE), which is regarded as a controlled light element, is proposed in the study. The SLM is employed to upload the synthetic phase-only hologram generated by the angular spectrum diffraction theory. Digital grating is introduced in the generation process of the hologram to achieve the splicing of the reconstructions and adjust the position of the reconstructions. The HOE fabricated by the computer-generated hologram printing can redirect the reconstructed images of multiview into multiple viewing zones. Thus, the modulation function of the HOE should be well-designed to avoid crosstalk between perspectives. The experimental results show that the proposed system can achieve multiview holographic augmented reality (AR) 3D display without crosstalk. The resolution of each perspective is 4K, which is higher than that of the existing multiview 3D display system.

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Geng Lei, 耿磊, 曹春鹏 Cao Chunpeng, 肖志涛 Xiao Zhitao, and 张芳 Zhang Fang. "基于激光雷达的多视角点云配准方法." Laser & Optoelectronics Progress 59, no. 12 (2022): 1228004. http://dx.doi.org/10.3788/lop202259.1228004.

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Zhang, Junyi, and Yuan Rao. "A Target Recognition Method Based on Multiview Infrared Images." Scientific Programming 2022 (March 22, 2022): 1–6. http://dx.doi.org/10.1155/2022/1358586.

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Infrared image target recognition provides an important means of night traffic management and battlefield environment monitoring. With the improvement of the performance of infrared sensors and the popularization of applications, it becomes possible to obtain multiview infrared images of the same target in the same scene. A target recognition method combining multiview infrared images is proposed. At first, the internal correlation analysis of multiview infrared images is performed based on the nonlinear correlation information entropy (NCIE). The view subset from all the multiview images with the largest NCIE is selected as candidate samples for the subsequent target recognition. The joint sparse representation (JSR) is used to classify all infrared images in the candidate view subset. JSR can effectively investigate the internal correlation of multiple related sparse representation problems and improve the reconstruction accuracy and classification capabilities. In the experiments, the tests are performed on the collected infrared images of multiple types of traffic vehicles, under the conditions of original, noisy, and occluded samples. The effectiveness and robustness of the proposed method can be verified by comparative analysis.

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Pei, Jifang, Zhiyong Wang, Xueping Sun, Weibo Huo, Yin Zhang, Yulin Huang, Junjie Wu, and Jianyu Yang. "FEF-Net: A Deep Learning Approach to Multiview SAR Image Target Recognition." Remote Sensing 13, no. 17 (September 2, 2021): 3493. http://dx.doi.org/10.3390/rs13173493.

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Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.

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Wang, Xiaoyan, and Chunping Hou. "Improved Crosstalk Reduction on Multiview 3D Display by Using BILS Algorithm." Journal of Applied Mathematics 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/428602.

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In multiview three-dimensional (3D) displays, crosstalk is one of the most annoying artefacts degrading the quality of the 3D image. In this paper, we present a system-introduced crosstalk measurement method and derive an improved crosstalk reduction method. The proposed measurement method is applied to measure the exact crosstalk among subpixels corresponding to different view images and the obtained results are very effective for crosstalk reduction method. Furthermore, an improved crosstalk reduction method is proposed to alleviate crosstalk by searching for the optimal integral intensity values of subpixels on the synthetic image. The derived algorithm based on modified Schnorr-Euchner strategy is implemented to seek the optimal solution to this box-constrained integer least squares (BILS) problem, such that the Euclidean distance between solution and its target decreases substantially. The method we develop is applicable to both multiview 3D parallax barrier displays and multiview 3D lenticular displays. Both simulation and experimental results indicate that the derived method is capable of improving 3D image quality more effectively than the existing method on multiview 3D displays.

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Mallik, Bruhanth, Akbar Sheikh-Akbari, Pooneh Bagheri Zadeh, and Salah Al-Majeed. "HEVC Based Frame Interleaved Coding Technique for Stereo and Multi-View Videos." Information 13, no. 12 (November 25, 2022): 554. http://dx.doi.org/10.3390/info13120554.

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The standard HEVC codec and its extension for coding multiview videos, known as MV-HEVC, have proven to deliver improved visual quality compared to its predecessor, H.264/MPEG-4 AVC’s multiview extension, H.264-MVC, for the same frame resolution with up to 50% bitrate savings. MV-HEVC’s framework is similar to that of H.264-MVC, which uses a multi-layer coding approach. Hence, MV-HEVC would require all frames from other reference layers decoded prior to decoding a new layer. Thus, the multi-layer coding architecture would be a bottleneck when it comes to quicker frame streaming across different views. In this paper, an HEVC-based Frame Interleaved Stereo/Multiview Video Codec (HEVC-FISMVC) that uses a single layer encoding approach to encode stereo and multiview video sequences is presented. The frames of stereo or multiview video sequences are interleaved in such a way that encoding the resulting monoscopic video stream would maximize the exploitation of temporal, inter-view, and cross-view correlations and thus improving the overall coding efficiency. The coding performance of the proposed HEVC-FISMVC codec is assessed and compared with that of the standard MV-HEVC’s performance for three standard multi-view video sequences, namely: “Poznan_Street”, “Kendo” and “Newspaper1”. Experimental results show that the proposed codec provides more substantial coding gains than the anchor MV-HEVC for coding both stereo and multi-view video sequences.

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Liu, Cuiwei, Zhaokui Li, Xiangbin Shi, and Chong Du. "Learning a Mid-Level Representation for Multiview Action Recognition." Advances in Multimedia 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/3508350.

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Recognizing human actions in videos is an active topic with broad commercial potentials. Most of the existing action recognition methods are supposed to have the same camera view during both training and testing. And thus performances of these single-view approaches may be severely influenced by the camera movement and variation of viewpoints. In this paper, we address the above problem by utilizing videos simultaneously recorded from multiple views. To this end, we propose a learning framework based on multitask random forest to exploit a discriminative mid-level representation for videos from multiple cameras. In the first step, subvolumes of continuous human-centered figures are extracted from original videos. In the next step, spatiotemporal cuboids sampled from these subvolumes are characterized by multiple low-level descriptors. Then a set of multitask random forests are built upon multiview cuboids sampled at adjacent positions and construct an integrated mid-level representation for multiview subvolumes of one action. Finally, a random forest classifier is employed to predict the action category in terms of the learned representation. Experiments conducted on the multiview IXMAS action dataset illustrate that the proposed method can effectively recognize human actions depicted in multiview videos.

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Horisaki, Ryoichi, Yuki Mori, and Jun Tanida. "Incoherent light control through scattering media based on machine learning and its application to multiview stereo displays." Optical Review 26, no. 6 (October 19, 2019): 709–12. http://dx.doi.org/10.1007/s10043-019-00554-y.

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Abstract In this paper, we present a method for controlling incoherent light through scattering media based on machine learning and its potential application to multiview stereo displays. The inverse function between input and output light intensity patterns through a scattering medium is regressed with a machine learning algorithm. The inverse function is used for calculating an input pattern for generating a target output pattern through a scattering medium. We demonstrate the proposed method by assuming a potential application to multiview stereo displays. This concept enables us to use a diffuser as a parallax barrier, a cylindrical lens array, or a lens array on a conventional multiview stereo display, which will contribute to a low-cost, highly functional display. A neural network is trained with a large number of pairs of displayed random patterns and their parallax images at different observation points, and then a displayed image is calculated from arbitrary parallax images using the trained neural network. In the experimental demonstration, the scattering-based multiview stereo display was composed of a diffuser and a conventional liquid crystal display, and it reproduced different handwritten characters, which were captured by a stereo camera.

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Sabah, Ali, Sabrina Tiun, Nor Samsiah Sani, Masri Ayob, and Adil Yaseen Taha. "Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach." PLOS ONE 16, no. 1 (January 15, 2021): e0245264. http://dx.doi.org/10.1371/journal.pone.0245264.

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Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.

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Zhang, R., X. Yi, H. Li, L. Liu, G. Lu, Y. Chen, and X. Guo. "MULTIRESOLUTION PATCH-BASED DENSE RECONSTRUCTION INTEGRATING MULTIVIEW IMAGES AND LASER POINT CLOUD." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 153–59. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-153-2022.

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Abstract. A dense point cloud with rich and realistic texture is generated from multiview images using dense reconstruction algorithms such as Multi View Stereo (MVS). However, its spatial precision depends on the performance of the matching and dense reconstruction algorithms used. Moreover, outliers are usually unavoidable as mismatching of image features. The lidar point cloud lacks texture but performs better spatial precision because it avoids computational errors. This paper proposes a multiresolution patch-based 3D dense reconstruction method based on integrating multiview images and the laser point cloud. A sparse point cloud is firstly generated with multiview images by Structure from Motion (SfM), and then registered with the laser point cloud to establish the mapping relationship between the laser point cloud and multiview images. The laser point cloud is reprojected to multiview images. The corresponding optimal level of the image pyramid is predicted by the distance distribution of projected pixels, which is used as the starting level for patch optimization during dense reconstruction. The laser point cloud is used as stable seed points for patch growth and expansion, and stored by the dynamic octree structure. Subsequently, the corresponding patches are optimized and expanded with the pyramid image to achieve multiscale and multiresolution dense reconstruction. In addition, the octree’s spatial index structure facilitates parallel computing with highly efficiency. The experimental results show that the proposed method is superior to the traditional MVS technology in terms of model accuracy and completeness, and have broad application prospects in high-precision 3D modeling of large scenes.

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Qiu, Wenge, Liao Jian, Yunjian Cheng, and Hengbin Bai. "Three-Dimensional Reconstruction of Tunnel Face Based on Multiple Images." Advances in Civil Engineering 2021 (April 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/8837309.

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The current geological sketch in tunnel engineering is mainly based on sketches of workers. However, geological sketch drawn by workers always offers fundamental data purely due to its drawing mode. A novel drawing method for geological sketch has been introduced using multiview photos in this process. The images of tunnel faces are taken from multiple angles, and every two pictures have overlaps. By measuring the distance between the camera and the tunnel face using a laser range finder, the photographic scale of each photo can be confirmed. SpeededUp Robust Features (SURF) is a good practice for detecting feature points, and the sparse point cloud is reconstructed from multiview photos by structure from motion (SFM). However, the sparse point cloud is not suitable for analysis for structural planes due to its sparsity. Therefore, patch-based multiview stereo (PMVS) is used to reconstruct dense point cloud from the sparse point cloud. After 3D reconstruction, the details of the tunnel face are recorded. The proposed technique was applied to multiview photos acquired in the Xiaosanxia railway tunnel and Fengjie tunnel in Chongqing, China. In order to record the geological conditions of the tunnel face quickly and accurately, Chengdu Tianyou Tunnelkey has developed a set of software and hardware integration system called CameraPad. Besides, CameraPad was used to collect the multiview photos of the tunnel face in the No. 1 Xinan railway tunnel in Jilin, China. By comparing with traditional and existing methods, the proposed method offers a more reductive model for geological conditions of the tunnel face.

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Zhan, Kun, Jinhui Shi, Jing Wang, Haibo Wang, and Yuange Xie. "Adaptive Structure Concept Factorization for Multiview Clustering." Neural Computation 30, no. 4 (April 2018): 1080–103. http://dx.doi.org/10.1162/neco_a_01055.

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Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full use of the data correlation between views. With the interview correlation, a concept factorization–based multiview clustering method is developed for data integration, and the adaptive method correlates the affinity weights of all views. This method differs from nonnegative matrix factorization–based clustering methods in that it can be applicable to data sets containing negative values. Experiments are conducted to demonstrate the effectiveness of the proposed method in comparison with state-of-the-art approaches in terms of accuracy, normalized mutual information, and purity.

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Hernandez Esteban, Carlos, George Vogiatzis, and Roberto Cipolla. "Multiview Photometric Stereo." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 3 (March 2008): 548–54. http://dx.doi.org/10.1109/tpami.2007.70820.

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Yoon, Ju Hong, Ming-Hsuan Yang, and Kuk-Jin Yoon. "Interacting Multiview Tracker." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 5 (May 1, 2016): 903–17. http://dx.doi.org/10.1109/tpami.2015.2473862.

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Tian Xia, Dacheng Tao, Tao Mei, and Yongdong Zhang. "Multiview Spectral Embedding." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, no. 6 (December 2010): 1438–46. http://dx.doi.org/10.1109/tsmcb.2009.2039566.

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Jeong, Young Ju, Hyun Sung Chang, Hyoseok Hwang, Dongkyung Nam, and C. C. Jay Kuo. "Uncalibrated multiview synthesis." Optical Engineering 56, no. 4 (April 12, 2017): 043103. http://dx.doi.org/10.1117/1.oe.56.4.043103.

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Chen, Zhixiang, and Jie Zhou. "Collaborative multiview hashing." Pattern Recognition 75 (March 2018): 149–60. http://dx.doi.org/10.1016/j.patcog.2017.02.026.

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Joswig, Michael, Joe Kileel, Bernd Sturmfels, and André Wagner. "Rigid multiview varieties." International Journal of Algebra and Computation 26, no. 04 (June 2016): 775–88. http://dx.doi.org/10.1142/s021819671650034x.

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The multiview variety from computer vision is generalized to images by [Formula: see text] cameras of points linked by a distance constraint. The resulting five-dimensional variety lives in a product of [Formula: see text] projective planes. We determine defining polynomial equations, and we explore generalizations of this variety to scenarios of interest in applications.

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Fakeri-Tabrizi, Ali, Massih-Reza Amini, Cyril Goutte, and Nicolas Usunier. "Multiview self-learning." Neurocomputing 155 (May 2015): 117–27. http://dx.doi.org/10.1016/j.neucom.2014.12.041.

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Flierl, Markus, and Bernd Girod. "Multiview Video Compression." IEEE Signal Processing Magazine 24, no. 99 (2007): 66–76. http://dx.doi.org/10.1109/msp.2007.4317465.

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Flierl, Markus, and Bernd Girod. "Multiview Video Compression." IEEE Signal Processing Magazine 24, no. 6 (November 2007): 66–76. http://dx.doi.org/10.1109/msp.2007.905699.

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Journal articles on the topic 'Multiview2'

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