{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T15:20:19Z","timestamp":1767453619920},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T00:00:00Z","timestamp":1630627200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T00:00:00Z","timestamp":1630627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s11554-021-01168-x","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T21:43:58Z","timestamp":1630705438000},"page":"1435-1439","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Real-time statistical image and video processing for remote sensing and surveillance applications"],"prefix":"10.1007","volume":"18","author":[{"given":"Mohammad R.","family":"Khosravi","sequence":"first","affiliation":[]},{"given":"Pooya","family":"Tavallali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"issue":"2","key":"1168_CR1","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"P Viola","year":"2004","unstructured":"Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137\u2013154 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"1168_CR2","doi-asserted-by":"crossref","unstructured":"G.-C. Luh, \u201cFace detection using combination of skin color pixel detection and viola-jones face detector,\u201d in 2014 International Conference on Machine Learning and Cybernetics, vol. 1. IEEE, 2014, pp. 364\u2013370.","DOI":"10.1109\/ICMLC.2014.7009143"},{"issue":"2","key":"1168_CR3","doi-asserted-by":"publisher","first-page":"2599","DOI":"10.1007\/s11042-018-6385-7","volume":"78","author":"P Tavallali","year":"2019","unstructured":"Tavallali, P., Yazdi, M., Khosravi, M.R.: Robust cascaded skin detector based on AdaBoost. Multimedia Tools Appl. 78(2), 2599\u20132620 (2019)","journal-title":"Multimedia Tools Appl."},{"key":"1168_CR4","doi-asserted-by":"publisher","unstructured":"Asadipooya, S. Samadi, M. Moradikia, and R. Mohseni, \u201cMajorization-minimization approach for real-time enhancement of sparsity driven SAR imaging,\u201d J. Real-Time Image Process. 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-021-01076-0","DOI":"10.1007\/s11554-021-01076-0"},{"key":"1168_CR5","doi-asserted-by":"publisher","unstructured":"P. Gunawardena, O. Amila, H. Sudarshana, R. Nawaratne, A. K. Luhach, D. Alahakoon, A. S. Perera, C. Chitraranjan, N. Chilamkurti, and D. D. Silva, \u201cReal-time automated video highlight generation with dual stream hierarchical growing self-organizing maps,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554- 020\u201300957\u20130","DOI":"10.1007\/s11554"},{"key":"1168_CR6","doi-asserted-by":"publisher","unstructured":"Y. He, H. Wang, L. Feng, and S. You, \u201cMotion-blurred star image restoration based on multi-frame superposition under high dynamic and long exposure conditions,\u201d J. Real-Time Image Process., 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-00965-0","DOI":"10.1007\/s11554-020-00965-0"},{"key":"1168_CR7","doi-asserted-by":"publisher","unstructured":"S. Shivanin, S. C. Patel, V. Arora, B. Sharma, A. Jolfaei, and G. Srivastava, \u201cReal-time cheating immune secret sharing for remote sensing images,\u201d J. Real-Time Image Process., 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-01005-7","DOI":"10.1007\/s11554-020-01005-7"},{"key":"1168_CR8","doi-asserted-by":"publisher","unstructured":"M. K. Moghimi and F. Mohanna, \u201cReal-time underwater image enhancement: a systematic review,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554- 020\u201301052\u20130","DOI":"10.1007\/s11554"},{"key":"1168_CR9","doi-asserted-by":"publisher","unstructured":"Y. Song, J. Qu, and C. Liu, \u201cReal-time registration of remote sensing images with a markov chain model,\u201d J. Real-Time Image Process., 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554- 020\u201301043\u20131","DOI":"10.1007\/s11554"},{"key":"1168_CR10","doi-asserted-by":"publisher","unstructured":"Y. Song and J. Qu, \u201cReal-time segmentation of remote sensing images with a combination of clustering and Bayesian approaches,\u201d J. Real-Time Image Process., 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-00990-z","DOI":"10.1007\/s11554-020-00990-z"},{"key":"1168_CR11","doi-asserted-by":"publisher","unstructured":"H. Hassan, A. K. Bashir, M. Ahmad, V. G. Menon, I. U. Afridi, R. Nawaz, B. Luo, \u201cReal\u2011time image dehazing by super-pixels segmentation and guidance filter,\u201d J. Real-Time Image Process., 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-00953-4","DOI":"10.1007\/s11554-020-00953-4"},{"key":"1168_CR12","doi-asserted-by":"publisher","unstructured":"Y. Wu, P. Han, Z. Zheng, \u201cInstant water body variation detection via analysis on remote sensing imagery, J. Real-Time Image Process.,\u201d 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-01062-y","DOI":"10.1007\/s11554-020-01062-y"},{"key":"1168_CR13","doi-asserted-by":"publisher","unstructured":"M. Das, \u201cReal-time prediction of spatial raster time series: a context-aware autonomous learning model,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-021-01099-7","DOI":"10.1007\/s11554-021-01099-7"},{"key":"1168_CR14","doi-asserted-by":"publisher","unstructured":"W. Jing, M. Zhang, and D. Tian, \u201cImproved u-net model for remote sensing image classification method based on distributed storage,\u201d J. Real-Time Image Process., 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-01028-0","DOI":"10.1007\/s11554-020-01028-0"},{"key":"1168_CR15","doi-asserted-by":"publisher","unstructured":"H. L. Kennedy, \u201cOn the realization and analysis of circular harmonic transforms for feature detection,\u201d J. Real-Time Image Process., 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-01040-4","DOI":"10.1007\/s11554-020-01040-4"},{"key":"1168_CR16","doi-asserted-by":"publisher","unstructured":"Z. Lai, L. Chen, G. Jeon, Z. Liu, R. Zhong, and X. Yang, \u201cReal-time and effective pan-sharpening for remote sensing using multi-scale fusion network,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-021-01080-4","DOI":"10.1007\/s11554-021-01080-4"},{"key":"1168_CR17","doi-asserted-by":"publisher","unstructured":"M. K. Moghimi and F. Mohanna, \u201cReal-time underwater image resolution enhancement using super-resolution with deep convolutional neural networks,\u201d J. Real-Time Image Process., 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-01024-4","DOI":"10.1007\/s11554-020-01024-4"},{"key":"1168_CR18","doi-asserted-by":"publisher","unstructured":"F. Rezaei and M. Yazdi, \u201cReal-time crowd behaviour recognition in surveillance videos based on deep learning methods,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-021-01116-9","DOI":"10.1007\/s11554-021-01116-9"},{"key":"1168_CR19","doi-asserted-by":"publisher","unstructured":"A. Mohan and V. M. Sundaram, \u201cV3o2: hybrid deep learning model for hyperspectral image classification using vanilla3d and octave2d convolution,\u201d Journal of Real-Time Image Processing, 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-00966-z","DOI":"10.1007\/s11554-020-00966-z"},{"key":"1168_CR20","doi-asserted-by":"publisher","unstructured":"T. D. Ngo, T. T. Bui, T. M. Pham, H. T. B. Thai, G. L. Nguyen, and T. N. Nguyen, \u201cImage de-convolution for optical small satellite with deep learning and real-time GPU acceleration,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-021-01113-y","DOI":"10.1007\/s11554-021-01113-y"},{"key":"1168_CR21","doi-asserted-by":"publisher","unstructured":"P. Singh and A. Shankar, \u201cA novel optical image denoising technique using convolutional neural network and anisotropic diffusion for real-time surveillance applications,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-01060-0","DOI":"10.1007\/s11554-020-01060-0"},{"key":"1168_CR22","doi-asserted-by":"publisher","unstructured":"N. Khan, A. Ullah, I. Haq, V. G. Menon, S. W. Baik, \u201cSD\u2011Net: understanding overcrowded scenes in real\u2011time via an efficient dilated convolutional neural network,\u201d J. Real-Time Image Process., 2020. [Online]. https:\/\/doi.org\/10.1007\/s11554-020-01020-8","DOI":"10.1007\/s11554-020-01020-8"},{"key":"1168_CR23","doi-asserted-by":"publisher","unstructured":"I. Ahmed, M. Ahmad, G. Jeon, \u201cA real-time efficient object segmentation system based on U-Net using aerial drone images,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-021-01166-z","DOI":"10.1007\/s11554-021-01166-z"},{"key":"1168_CR24","doi-asserted-by":"publisher","unstructured":"J. Pirgazi, A. G. Sorkhi, M. M. P. Kallehbasti, \u201cAn efficient robust method for accurate and real-time vehicle plate recognition,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-021-01118-7","DOI":"10.1007\/s11554-021-01118-7"},{"key":"1168_CR25","doi-asserted-by":"publisher","unstructured":"P. N. Srinivasu, A. K. Bhoi, R. H. Jhaveri, G. T. Reddy, M. Bilal, \u201cProbabilistic deep Q network for real-time path planning in\u00a0censorious robotic procedures using force sensors,\u201d J. Real-Time Image Process., 2021. [Online]. https:\/\/doi.org\/10.1007\/s11554-021-01122-x","DOI":"10.1007\/s11554-021-01122-x"},{"key":"1168_CR26","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.comcom.2020.02.017","volume":"153","author":"M Abbasi","year":"2021","unstructured":"Abbasi, M., Yaghoobikia, M., Rafiee, M., Jolfaei, A., Khosravi, M.R.: Efficient resource management and workload allocation in fog-cloud computing paradigm in IoT using learning classifier systems. Comput. Commun. 153, 217\u2013228 (2021)","journal-title":"Comput. Commun."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-021-01168-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-021-01168-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-021-01168-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T07:39:36Z","timestamp":1632209976000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-021-01168-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,3]]},"references-count":26,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["1168"],"URL":"https:\/\/doi.org\/10.1007\/s11554-021-01168-x","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,3]]},"assertion":[{"value":"3 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}