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BioRxiv (2020) Fu, Y. Jiang, G.-P. Ji, T. Zhou, Q. Zhao, D.-P. Fan, Light field salient object detection: A review and benchmark. Computational Visual Media, 1–26 (2022)E. Shafiee, M.G. Martini, Datasets for the quality assessment of light field imaging: comparison and future directions. IEEE Access 11, 15014–15029 (2023)Article Google Scholar N.K. Kalantari, T.-C. Wang, R. Ramamoorthi, Learning-based view synthesis for light field cameras code. [Online; accessed 10-August-2021] (2016)EPFL Light Field Image Dataset. [Online; accessed 10-August-2021]HCI Light Field Dataset. [Online; accessed 10-August-2021]M. Ziegler, R. Veld, J. Keinert, F. Zilly, Acquisition system for dense lightfield of large scenes. In 2017 3DTV Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4 (2017). IEEEStanford Light Field Archives. [Online; accessed 10-August-2021]Y. Yao, Z. Luo, S. Li, J. Zhang, Y. Ren, L. Zhou, T. Fang, L.: Quan,Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1790–1799 (2020)L. Liu, J. Gu, K. Zaw Lin, T.-S. Chua, C. Theobalt, Neural sparse voxel fields. Adv. Neural Inf. Process. Syst. 33, 15651–15663 (2020) Google Scholar A. Knapitsch, J. Park, Q.-Y. Zhou, V. Koltun, Tanks and temples: benchmarking large-scale scene reconstruction. ACM Trans. Gr. (ToG) 36(4), 1–13 (2017)Article Google Scholar S. Mahmoudpour, P. Schelkens, On the performance of objective quality metrics for lightfields. Signal Process. Image Commun. 93, 116179 (2021)Article Google Scholar M. Maria, Ieee standard on the quality assessment of light field imaging. In IEEE SA, pp. 20–55 (2022). IEEEC. Perra, S. Mahmoudpour, C. Pagliari, Jpeg pleno light field: Current standard and future directions. In Optics, Photonics and Digital Technologies for Imaging Applications VII, vol. 12138, pp. 153–156 (2022). SPIER.R. Tamboli, B. Appina, S. Channappayya, S. Jana, Super-multiview content with high angular resolution: 3d quality assessment on horizontal-parallax lightfield display. Signal Process. Image

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On Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 12–17 May 2019; pp. 8464–8468. [Google Scholar] [CrossRef] [Green Version]Arashloo, S.R. Unseen Face Presentation Attack Detection Using Class-Specific Sparse One-Class Multiple Kernel Fusion Regression. arXiv 2019, arXiv:1912.13276. [Google Scholar]Erdem, E. Linear Diffusion. 2012. Available online: (accessed on 19 September 2019).Hajiaboli, M.R.; Ahmad, M.O.; Wang, C. An Edge-Adapting Laplacian Kernel for Nonlinear Diffusion Filters. IEEE Trans. Image Process. 2012, 21, 1561–1572. [Google Scholar] [CrossRef] [PubMed]Perona, P.; Malik, J. Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 1990, 12, 629–639. [Google Scholar] [CrossRef] [Green Version]Weickert, J.; Romeny, B.M.T.H.; Viergever, M.A. Efficient and Reliable Schemes for Nonlinear Diffusion Filtering. IEEE Trans. Image Process. 1998, 7, 398–410. [Google Scholar] [CrossRef] [PubMed] [Green Version]Ralli, J. PDE Based Image Diffusion and AOS. 2014. Available online: (accessed on 28 August 2017).Szegedy, C.; Ioffe, S.; Vanhoucke, V. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. arXiv 2016, arXiv:1602.07261. [Google Scholar]Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Rabinovich, A. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7–12 June 2015; pp. 1–9. [Google Scholar] [CrossRef] [Green Version]Mulder, W.D.; Bethard, S.; Moens, M. A survey on the application of recurrent neural networks to statistical language modeling. Comput Speech Lang. 2015, 30, 61–98. [Google Scholar] [CrossRef] [Green Version]Hochreiter, S.; Schmidhuber, J. Long Short-Term Memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef] [PubMed]Lu, Z.; Wu, X.; He, R.

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Platform adoption by mobile application developers: A multimethodological approachJ Song, J Baker, Y Wang, HY Choi, A BhattacherjeeDecision Support Systems 107, 26-39, 2018502018Revealing key non-financial factors for online credit-scoring in e-financingY Wang, S Li, Z Lin2013 10th International Conference on Service Systems and Service Management …, 2013232013Image or text: Which one is more influential? a deep-learning approach for visual and textual data analysis in the digital economyY Wang, J SongCommunications of the Association for Information Systems 47 (1), 44, 2020102020The Success of Massive Open Online Courses (MOOCs): An Investigation on Course RelevanceY Wang, J SongCommunications of the Association for Information Systems 51 (1), 202292022MOOC Relevance: A Key Determinant of the Success of Massive Open Online Course (MOOC)Y Wang, W Wang, L AlbertJournal of Information Systems Education 34 (4), 456-471, 202342023How traditional incumbents react to sharing economy entrants? Evidence from the car industryY Guo, F Xin, Q Jia, S Barnes, Y Wang42018The role of service standardization capability in service innovation: Evidence from the knowledge-intensive service firmsY Wang, J Song, J Baker, Y Kim42018Investigating the value of information in mobile commerce: A text mining approachY Wang, M Aguirre-Urreta, J SongAsia pacific journal of information systems 26 (4), 577-592, 201632016Engaging Individuals in CSR Activities: The Role of Person-Environment Fit and Social Capital in Corporate Online CSR CommunitiesY Wang, J Song, B Mitchell, SY Lee, S ChaiE-Service Journal 13 (3), 1-29, 202222022The importance of objective and dynamic credit evaluation in P2P lending marketY Wang, Z LinWorking Paper, 201422014What Makes a Massive Open Online Courses (MOOCs) Excellent? An Investigation in Business Analytics CoursesY Wang, J Song27th Americas Conference on Information Systems 2021: Digital Innovation and …, 202112021Using image-based and text-based information for sales prediction: a deep neural network modelY Wang, Y Guo, J Song12018What Affects Mobile Application Downloads? The Role of in-Store InformationY Wang, M Aguirre-Urreta, J Song12016The Impact of Mobile Application Information on Application Download: A Text Mining ApproachJ Song, Y Wang12015Consumer Perceived Value and MOOC Subscription IntentionsY Wang, W Wang, X Zhang, N Aggarwal, L AlbertJournal of Computer Information Systems, doi.org/10.1080/08874417.2025.2467631, 20252025The Attractiveness of Visuals vs. Content: Investigating the Impact of Video Features

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Component analysis neural network. Opt Eng 44(9):1–9Article MATH Google Scholar Liu R, Liu E, Yang J, Zhang T, Wang F (2007) Infrared small target detection with kernel Fukunaga–Koontz transform. Meas Sci Technol 18:3025–3035Article Google Scholar Ning JF, Zhang Z, Wu CK (2009) Robust object tracking using joint color-texture histogram. Int J Pattern Recognit Artif Intell 23(7):1245–1263Article Google Scholar Nummiaro K, Koller-Meier E, Gool LV (2003) An adaptive color-based particle filter. Image Vis Comput 21(1):99–110Article Google Scholar Ojala T, Pietikäinen M, Mäenpä T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987Article Google Scholar Ojala T, Valkealahti K, Oja E, Pietikäinen M (2001) Texture discrimination with multi-dimensional distributions of signed gray level differences. Pattern Recognit 34(3):727–739Article MATH Google Scholar OTCBVS benchmark dataset. [online] Peng GH, Chen H, Wu Q (2011) Infrared small target detection under complex background. Adv Mater Res 346:615–619Article Google Scholar Polat E, Ozden M (2006) A nonparametric adaptive tracking algorithm based on multiple feature distributions. IEEE Trans Multimed 8:1156–1163Article Google Scholar Shao XP, Fan H, Lu GX, Xu J (2012) An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system. Infrared Phys Technol, Available online 15 June 2012Soni T, Zeidler JR, Ku W (1993) Performance evaluation of 2D adaptive prediction filters for detection of small objects in image data. IEEE Trans Image Process 2(3):327–340Article Google Scholar Valtteri V, Pietikainen M (2007) Multi-object tracking using color, texture and motion. In: Proc.

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