Relevant bibliographies by topics / Single to multiview conversion

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  1. Journal articles

Academic literature on the topic 'Single to multiview conversion'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Single to multiview conversion"

1

Lu, Shao-Ping, Sibo Feng, Beerend Ceulemans, Miao Wang, Rui Zhong, and Adrian Munteanu. "Multiview conversion of 2D cartoon images." Communications in Information and Systems 16, no. 4 (2016): 229–54. http://dx.doi.org/10.4310/cis.2016.v16.n4.a2.

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Chapiro, Alexandre, Simon Heinzle, Tunç Ozan Aydın, et al. "Optimizing stereo-to-multiview conversion for autostereoscopic displays." Computer Graphics Forum 33, no. 2 (2014): 63–72. http://dx.doi.org/10.1111/cgf.12291.

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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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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 (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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Wang, Qingjun, Haiyan Lv, Jun Yue, and Eugene Mitchell. "Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier." Neural Computing and Applications 28, no. 8 (2016): 2293–301. http://dx.doi.org/10.1007/s00521-016-2189-8.

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Shin, Hong-Chang, Jinwhan Lee, Gwangsoon Lee, and Namho Hur. "Stereo-To-Multiview Conversion System Using FPGA and GPU Device." Journal of Broadcast Engineering 19, no. 5 (2014): 616–26. http://dx.doi.org/10.5909/jbe.2014.19.5.616.

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Kuo, Pin-Chen, Kuan-Lun Lo, Huan-Kai Tseng, Kuan-Ting Lee, Bin-Da Liu, and Jar-Ferr Yang. "Stereoview to Multiview Conversion Architecture for Auto-Stereoscopic 3D Displays." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 11 (2018): 3274–87. http://dx.doi.org/10.1109/tcsvt.2017.2732061.

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8

Pei, Jifang, Weibo Huo, Chenwei Wang, et al. "Multiview Deep Feature Learning Network for SAR Automatic Target Recognition." Remote Sensing 13, no. 8 (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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Pei, Jifang, Zhiyong Wang, Xueping Sun, et al. "FEF-Net: A Deep Learning Approach to Multiview SAR Image Target Recognition." Remote Sensing 13, no. 17 (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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10

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 (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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