{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T08:10:17Z","timestamp":1783239017028,"version":"3.54.6"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"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":["SIViP"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s11760-020-01746-9","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T11:04:07Z","timestamp":1595502247000},"page":"241-246","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Parametric rectified nonlinear unit (PRenu) for convolution neural networks"],"prefix":"10.1007","volume":"15","author":[{"given":"Ilyas","family":"El Jaafari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayoub","family":"Ellahyani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Said","family":"Charfi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"key":"1746_CR1","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.neunet.2018.01.005","volume":"99","author":"\u00c1 Arcos-Garc\u00eda","year":"2018","unstructured":"Arcos-Garc\u00eda, \u00c1., \u00c1lvarez Garc\u00eda, J.A., Soria-Morillo, L.M.: Deep neural network for traffic sign recognition systems: an analysis of spatial transformers and stochastic optimisation methods. Neural Netw. 99, 158\u2013165 (2018). https:\/\/doi.org\/10.1016\/j.neunet.2018.01.005","journal-title":"Neural Netw."},{"key":"1746_CR2","doi-asserted-by":"publisher","unstructured":"Chen, B., Jung, C.: Patch-based stereo matching using 3d convolutional neural networks. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3633\u20133637 (2018). https:\/\/doi.org\/10.1109\/ICIP.2018.8451527","DOI":"10.1109\/ICIP.2018.8451527"},{"issue":"106","key":"1746_CR3","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1016\/j.patcog.2019.07.006","volume":"96","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Ho, P.H.: Global-connected network with generalized relu activation. Pattern Recogn. 96(106), 961 (2019). https:\/\/doi.org\/10.1016\/j.patcog.2019.07.006","journal-title":"Pattern Recogn."},{"key":"1746_CR4","unstructured":"Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). 1511.07289 (2015)"},{"key":"1746_CR5","doi-asserted-by":"crossref","unstructured":"Dong, X., Shen, J., Wang, W., Liu, Y., Shao, L., Porikli, F.: Hyperparameter optimization for tracking with continuous deep q-learning. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 518\u2013527 (2018)","DOI":"10.1109\/CVPR.2018.00061"},{"key":"1746_CR6","unstructured":"Dong, X., Shen, J., Wang, W., Shao, L., Ling, H., Porikli, F.: Dynamical hyperparameter optimization via deep reinforcement learning in tracking. IEEE Trans. Pattern Anal. Mach. Intell., pp 1\u20131 (2019)"},{"issue":"7","key":"1746_CR7","doi-asserted-by":"publisher","first-page":"3516","DOI":"10.1109\/TIP.2019.2898567","volume":"28","author":"X Dong","year":"2019","unstructured":"Dong, X., Shen, J., Wu, D., Guo, K., Jin, X., Porikli, F.: Quadruplet network with one-shot learning for fast visual object tracking. IEEE Trans. Image Process. 28(7), 3516\u20133527 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"1746_CR8","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Gordon, G., Dunson, D., Dud\u00edk, M. (eds) Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR, Fort Lauderdale, FL, USA, Proceedings of Machine Learning Research, vol\u00a015, pp 315\u2013323 (2011)"},{"key":"1746_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J. (2015). Delving deep into rectifiers: surpassing human-level performance on imagenet classification, 1502.01852","DOI":"10.1109\/ICCV.2015.123"},{"key":"1746_CR10","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neucom.2016.02.010","volume":"194","author":"IE Jaafari","year":"2016","unstructured":"Jaafari, I.E., Ansari, M.E., Koutti, L., Mazoul, A., Ellahyani, A.: Fast spatio-temporal stereo matching for advanced driver assistance systems. Neurocomputing 194, 24\u201333 (2016). https:\/\/doi.org\/10.1016\/j.neucom.2016.02.010","journal-title":"Neurocomputing"},{"key":"1746_CR11","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s11760-016-0932-3","volume":"11","author":"IE Jaafari","year":"2017","unstructured":"Jaafari, I.E., Ansari, M.E., Koutti, L.: Fast edge-based stereo matching approach for road applications. Signal Image Video Process. 11, 267\u2013274 (2017)","journal-title":"Signal Image Video Process."},{"key":"1746_CR12","unstructured":"Maas, A. L.: Rectifier nonlinearities improve neural network acoustic models (2013)"},{"key":"1746_CR13","unstructured":"Nair, V., Hinton, G. E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, Omnipress, USA, ICML\u201910, pp 807\u2013814 (2010)"},{"key":"1746_CR14","first-page":"1447","volume":"2","author":"ME Nilsback","year":"2006","unstructured":"Nilsback, M.E., Zisserman, A.: A visual vocabulary for flower classification. IEEE Conf. Comput. Vis. Pattern Recogn. 2, 1447\u20131454 (2006)","journal-title":"IEEE Conf. Comput. Vis. Pattern Recogn."},{"key":"1746_CR15","unstructured":"Shen, J., Tang, X., Dong, X., Shao, L. (2019) Visual object tracking by hierarchical attention Siamese network. IEEE Trans. Cybern., pp 1\u201313"},{"key":"1746_CR16","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.proeng.2017.09.594","volume":"201","author":"A Shustanov","year":"2017","unstructured":"Shustanov, A., Yakimov, P.: Cnn design for real-time traffic sign recognition. Proc. Eng. 201, 718\u2013725 (2017). https:\/\/doi.org\/10.1016\/j.proeng.2017.09.594","journal-title":"Proc. Eng."},{"key":"1746_CR17","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s11760-018-1335-4","volume":"13","author":"FC Soon","year":"2019","unstructured":"Soon, F.C., Khaw, H.Y., Chuah, J.H., Kanesan, J.: Vehicle logo recognition using whitening transformation and deep learning. Signal Image Video Process. 13, 111\u2013119 (2019)","journal-title":"Signal Image Video Process."},{"key":"1746_CR18","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1016\/j.jvcir.2016.08.022","volume":"40","author":"Z Wang","year":"2016","unstructured":"Wang, Z., Zhu, S., Li, Y., Cui, Z.: Convolutional neural network based deep conditional random fields for stereo matching. J. Vis. Commun. Image Represent. 40, 739\u2013750 (2016). https:\/\/doi.org\/10.1016\/j.jvcir.2016.08.022","journal-title":"J. Vis. Commun. Image Represent."},{"key":"1746_CR19","unstructured":"Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. 1505.00853 (2015)"},{"key":"1746_CR20","unstructured":"Yin, W., Kann, K., Yu, M., Sch\u00fctze, H.: Comparative study of cnn and rnn for natural language processing. 1702.01923 (2017)"},{"issue":"65","key":"1746_CR21","first-page":"1","volume":"17","author":"J \u017dbontar","year":"2016","unstructured":"\u017dbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. 17(65), 1\u201332 (2016)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-020-01746-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-020-01746-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-020-01746-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:01:41Z","timestamp":1626998501000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-020-01746-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,23]]},"references-count":21,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["1746"],"URL":"https:\/\/doi.org\/10.1007\/s11760-020-01746-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,23]]},"assertion":[{"value":"14 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}