Substantial Phase Exploration for Intuiting Covid using form Expedient with Variance Sensor
AbstractThis article focuses on implementing wireless sensors for monitoring exact distance between two individuals and to check whether everybody have sanitized their hands for stopping the spread of Corona Virus Disease (COVID). The idea behind this method is executed by implementing an objective function which focuses on maximizing distance, energy of nodes and minimizing the cost of implementation. Also, the proposed model is integrated with a variance detector which is denoted as Controlled Incongruity Algorithm (CIA). This variance detector is will sense the value and it will report to an online monitoring system named Things speak and for visualizing the sensed values it will be simulated using MATLAB. Even loss which is produced by sensors is found to be low when CIA is implemented. To validate the efficiency of proposed method it has been compared with prevailing methods and results prove that the better performance is obtained and the proposed method is improved by 76.8% than other outcomes observed from existing literatures.
 A. Borji, M.-M. Cheng, H. Jiang, and J. Li, Salient object detection: A benchmark, IEEE Trans. Image Process., vol. 24, no. 12, pp. 5706-5722, 2015.
 X. Shen and Y. Wu, A unified approach to salient object detection via low rank matrix recovery, in Proc. IEEE CVPR, Providence, RI, USA, 2012, pp. 853-860.
 H. Kim, Y. Kim, J.-Y. Sim, and C.-S. Kim, Spatiotemporal saliency detection for video sequences based on random walk with restart, IEEE Trans. Image Process., vol. 24, no. 8, pp. 2552-2564, Aug. 2015.
 W. Wang, J. Shen, and L. Shao, Video salient object detection via fully convolutional networks, IEEE Trans. Image Process., to be published, doi: 10.1109/TIP.2017.2754941.
 J. Peng, J. Shen, and X. Li, High-order energies for stereo segmentation, IEEE Trans. Cybern., vol. 46, no. 7, pp. 1616-1627, Jul. 2016.
 F. Perazzi, P. Krähenbühl, Y. Pritch, and A. Hornung, Saliency filters: Contrast based filtering for salient region detection, in Proc. IEEE CVPR, Providence, RI, USA, 2012, pp. 733-740.
 .M.-M. Cheng, N. J. Mitra, X. Huang, P. H. S. Torr, and S.-M. Hu, Global contrast based salient region detection, IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 569-582, Mar. 2015.
 W. Wang, Q. Lai, H. Fu, J. Shen, H. Ling, Salient object detection in the deep learning era: an in-depth survey, CoRR abs/1904.09146 (2019).
 L. Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Trans. Pattern Anal. Mach. Intell. 20 (11) (1998) 1254-1259.
 J. Harel, C. Koch, P. Perona, Graph-based visual saliency, in: International Conference on Neural Information Processing Systems, 2006, pp. 545-552.
 P. Zhang, T. Zhuo, W. Huang, K. Chen, M. Kankanhalli, Online object tracking based on CNN with spatial-temporal saliency guided sampling, Neurocomputing 257 (2017) 115-127.
 J. Zhang, K.A. Ehinger, H. Wei, K. Zhang, J. Yang, A novel graph-based optimization framework for salient object detection, PatternRecognit. 64 (1) (2017) 39-50.
 H. Chen, Y. Li, D. Su, Multi-modal fusion network with multi-scale multi- path and cross-modal interactions for RGB-D salient object detection, Pattern Recognit. 1 (1) (2018).1-1.
 E. Macaluso, C.D. Frith, J. Driver, Directing attention to locations and to sensory modalities: multiple levels of selective processing revealed with PET, Cerebral Cortex 12 (4) (2002) 357-368.
 T.S. Lee, D. Mumford, Hierarchical bayesian inference in the visual cortex, JOSAA 20 (7) (2003) 1434-1448.
 Q. Yan, L. Xu, J. Shi, J. Jia, Hierarchical saliency detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1155-1162.
 . Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1597-1604. IEEE (2009)
 Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global contrast based salient region detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 409-416. IEEE (2011)
 Cui, X., Liu, Q., Metaxas, D.: Temporal spectral residual: fast motion saliency detection. In: Proceedings of the ACM International Conference on Multimedia (2009).
 B. X. Nie, P. Wei, and S.-C. Zhu, Monocular 3D human pose estimation by predicting depth on joints. in IEEE International Conference on Computer Vision, 2017
 D. Zhang, J. Han, C. Li, J. Wang, and X. Li, Detection of co-salient objects by looking deep and wide, International Journal of Computer Vision, vol. 120, no. 2, pp. 215-232, 2016.
 X. Dong et al., Occlusion-aware real-time object tracking, IEEE Trans. Multimedia, vol. 19, no. 4, pp. 763-771, Apr. 2017.
 X. Dong, J. Shen, L. Shao, and L. Van Gool, Sub-Markov random walk for image segmentation, IEEE Trans. Image Process., vol. 25, no. 2, pp. 516-527, Feb. 2016.
 J. Shen et al., Real-time superpixel segmentation by DBSCAN clustering algorithm, IEEE Trans. Image Process., vol. 25, no. 12, pp. 5933-5942, Dec. 2016.
 Y. Yuan, C. Li, J. Kim, W. Cai, D.D. Feng, Dense and sparse labeling with multidimensional features for saliency detection, IEEE Trans. Circuits Syst. Video Technol. 28 (5) (2018) 1130-1143.
 W. Wang, J. Shen, F. Guo, M.-M. Cheng, A. Borji, Revisiting video saliency: a large-scale benchmark and a new model, in: IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4894-4903.
 Li Q., Chen S., Zhang B. (2012) Predictive Video Saliency Detection. In: Liu CL., Zhang C., Wang L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg.
 Wang, Wenguan et al. Deep Learning For Video Saliency Detection. ArXiv abs/1702. 00871 (2017): n. pag.
 F. Guo et al., "Video Saliency Detection Using Object Proposals," in IEEE Transactions on Cybernetics, vol. 48, no. 11, pp. 3159-3170, Nov. 2018, doi: 10.1109/TCYB.2017.2761361.
 Karthik, A., MazherIqbal, J.L. Efficient Speech Enhancement Using Recurrent Convolution Encoder and Decoder. Wireless Pers Commun 119, 1959-1973 (2021).
 Yuming Fang, Xiaoqiang Zhang, Feiniu Yuan, NevrezImamoglu, Haiwen Liu, Video saliency detection by gestalt theory, Pattern Recognition, Volume 96,2019,106987, ISSN 0031-3203.
 https://docs.microsoft.com/en-us/cpp/build/reference/clr common language runtime compilation? View = msvc-160
 https://docs.microsoft.com/en-us/cpp/dotnet/walkthrough-compiling-a-cpp-program-that-targets-theclr- in-visual-studio?view=msvc-160
 Wang, Bofei et al. Object-based Spatial Similarity for Semi-supervised Video Object Segmentation. (2019).
 Li F., Kim T., Humayun A., Tsai D., Rehg J. M.,Video Segmentation byTracking Many Figure-Ground Segments In:IEEE International Conference onComputer Vision (ICCV), 2013.
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