{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:08:56Z","timestamp":1743142136786,"version":"3.40.3"},"publisher-location":"Cham","reference-count":68,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030680275"},{"type":"electronic","value":"9783030680282"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68028-2_6","type":"book-chapter","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T19:02:55Z","timestamp":1611946975000},"page":"113-134","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neurocognitive\u2013Inspired Approach for Visual Perception in Autonomous Driving"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8567-0553","authenticated-orcid":false,"given":"Alice","family":"Plebe","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6619-9484","authenticated-orcid":false,"given":"Mauro","family":"Da Lio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1\u2013127 (2009)","journal-title":"Found. Trends Mach. Learn."},{"unstructured":"Bojarski, M., et al.: Explaining how a deep neural network trained with end-to-end learning steers a car. CoRR abs\/1704.07911 (2017)","key":"6_CR2"},{"doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. CoRR abs\/1511.06349 (2015)","key":"6_CR3","DOI":"10.18653\/v1\/K16-1002"},{"key":"6_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-8963-5","volume-title":"Fourier Analysis and Imaging","author":"R Bracewell","year":"2003","unstructured":"Bracewell, R.: Fourier Analysis and Imaging. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-1-4419-8963-5"},{"key":"6_CR5","doi-asserted-by":"crossref","first-page":"5339","DOI":"10.1007\/s11229-018-01949-1","volume":"195","author":"C Buckner","year":"2018","unstructured":"Buckner, C.: Empiricism without magic: transformational abstraction in deep convolutional neural networks. Synthese 195, 5339\u20135372 (2018)","journal-title":"Synthese"},{"doi-asserted-by":"crossref","unstructured":"Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. CoRR abs\/1405.3531 (2014)","key":"6_CR6","DOI":"10.5244\/C.28.6"},{"unstructured":"Chui, M., et al.: Notes from the AI frontier: insights from hundreds of use cases. Technical report, April, McKinsey Global Institute (2018)","key":"6_CR7"},{"key":"6_CR8","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0010-0277(89)90005-X","volume":"33","author":"A Damasio","year":"1989","unstructured":"Damasio, A.: Time-locked multiregional retroactivation: a systems-level proposal for the neural substrates of recall and recognition. Cognition 33, 25\u201362 (1989)","journal-title":"Cognition"},{"key":"6_CR9","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neuroimage.2016.10.001","volume":"152","author":"M Eickenberg","year":"2017","unstructured":"Eickenberg, M., Gramfort, A., Varoquaux, G., Thirion, B.: Seeing it all: convolutional network layers map the function of the human visual system. NeuroImage 152, 184\u2013194 (2017)","journal-title":"NeuroImage"},{"key":"6_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/cercor\/1.1.1","volume":"1","author":"DJ Felleman","year":"1991","unstructured":"Felleman, D.J., Van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cerebr. Cortex 1, 1\u201347 (1991)","journal-title":"Cerebr. Cortex"},{"key":"6_CR11","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4737.001.0001","volume-title":"Modularity of Mind: and Essay on Faculty Psychology","author":"J Fodor","year":"1983","unstructured":"Fodor, J.: Modularity of Mind: and Essay on Faculty Psychology. MIT Press, Cambridge (1983)"},{"key":"6_CR12","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1126\/science.291.5502.312","volume":"291","author":"DJ Freedman","year":"2001","unstructured":"Freedman, D.J., Riesenhuber, M., Poggio, T., Miller, E.K.: Categorical representation of visual stimuli in the primate prefrontal cortex. Science 291, 312\u2013316 (2001)","journal-title":"Science"},{"key":"6_CR13","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1038\/nrn2787","volume":"11","author":"K Friston","year":"2010","unstructured":"Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127\u2013138 (2010)","journal-title":"Nat. Rev. Neurosci."},{"key":"6_CR14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/NECO_a_00912","volume":"29","author":"K Friston","year":"2017","unstructured":"Friston, K., Fitzgerald, T., Rigoli, F., Schwartenbeck, P., Pezzulo, G.: Active inference: a process theory. Neural Comput. 29, 1\u201349 (2017)","journal-title":"Neural Comput."},{"key":"6_CR15","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s11229-007-9237-y","volume":"159","author":"K Friston","year":"2007","unstructured":"Friston, K., Stephan, K.E.: Free-energy and the brain. Synthese 159, 417\u2013458 (2007)","journal-title":"Synthese"},{"key":"6_CR16","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193\u2013202 (1980)","journal-title":"Biol. Cybern."},{"key":"6_CR17","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1038\/280120a0","volume":"280","author":"CD Gilbert","year":"1979","unstructured":"Gilbert, C.D., Wiesel, T.N.: Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex. Nature 280, 120\u2013125 (1979)","journal-title":"Nature"},{"unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010)","key":"6_CR18"},{"key":"6_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0079-6123(03)43001-X","volume":"143","author":"S Grillner","year":"2004","unstructured":"Grillner, S., Wall\u00e9n, P.: Innate versus learned movements - a false dichotomy. Progress Brain Res. 143, 1\u201312 (2004)","journal-title":"Progress Brain Res."},{"key":"6_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1003724","volume":"10","author":"U G\u00fc\u00e7l\u00fc","year":"2014","unstructured":"G\u00fc\u00e7l\u00fc, U., van Gerven, M.A.J.: Unsupervised feature learning improves prediction of human brain activity in response to natural images. PLoS Comput. Biol. 10, 1\u201316 (2014)","journal-title":"PLoS Comput. Biol."},{"key":"6_CR21","doi-asserted-by":"crossref","first-page":"10005","DOI":"10.1523\/JNEUROSCI.5023-14.2015","volume":"35","author":"U G\u00fc\u00e7l\u00fc","year":"2015","unstructured":"G\u00fc\u00e7l\u00fc, U., van Gerven, M.A.J.: Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35, 10005\u201310014 (2015)","journal-title":"J. Neurosci."},{"key":"6_CR22","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.neuron.2017.06.011","volume":"95","author":"D Hassabis","year":"2017","unstructured":"Hassabis, D., Kumaran, D., Summerfield, C., Botvinick, M.: Neuroscience-inspired artificial intelligence. Neuron 95, 245\u2013258 (2017)","journal-title":"Neuron"},{"doi-asserted-by":"crossref","unstructured":"Hazelwood, K., et al.: Applied machine learning at Facebook: a datacenter infrastructure perspective. In: IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 620\u2013629 (2018)","key":"6_CR23","DOI":"10.1109\/HPCA.2018.00059"},{"key":"6_CR24","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.brainres.2011.06.026","volume":"1428","author":"G Hesslow","year":"2012","unstructured":"Hesslow, G.: The current status of the simulation theory of cognition. Brain 1428, 71\u201379 (2012)","journal-title":"Brain"},{"unstructured":"Hinton, G., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Advances in Neural Information Processing Systems, pp. 3\u201310 (1994)","key":"6_CR25"},{"unstructured":"Hinton, G.E., McClelland, J.L., Rumelhart, D.E.: Distributed representations. In: Rumelhart and McClelland [53], pp. 77\u2013109","key":"6_CR26"},{"key":"6_CR27","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","volume":"28","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 28, 504\u2013507 (2006)","journal-title":"Science"},{"key":"6_CR28","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","volume":"160","author":"D Hubel","year":"1962","unstructured":"Hubel, D., Wiesel, T.: Receptive fields, binocular interaction, and functional architecture in the cat\u2019s visual cortex. J. Physiol. 160, 106\u2013154 (1962)","journal-title":"J. Physiol."},{"key":"6_CR29","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1113\/jphysiol.1968.sp008455","volume":"195","author":"D Hubel","year":"1968","unstructured":"Hubel, D., Wiesel, T.: Receptive fields and functional architecture of mokey striate cortex. J. Physiol. 195, 215\u2013243 (1968)","journal-title":"J. Physiol."},{"key":"6_CR30","doi-asserted-by":"crossref","first-page":"S103","DOI":"10.1006\/nimg.2001.0832","volume":"14","author":"M Jeannerod","year":"2001","unstructured":"Jeannerod, M.: Neural simulation of action: a unifying mechanism for motor cognition. NeuroImage 14, S103\u2013S109 (2001)","journal-title":"NeuroImage"},{"key":"6_CR31","first-page":"136","volume":"1","author":"W Jones","year":"2017","unstructured":"Jones, W., Alasoo, K., Fishman, D., Parts, L.: Computational biology: deep learning. Emerg. Top. Life Sci. 1, 136\u2013161 (2017)","journal-title":"Emerg. Top. Life Sci."},{"unstructured":"Khan, S., Tripp, B.P.: One model to learn them all. CoRR abs\/1706.05137 (2017)","key":"6_CR32"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of International Conference on Learning Representations (2014)","key":"6_CR33"},{"unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Proceedings of International Conference on Learning Representations (2014)","key":"6_CR34"},{"key":"6_CR35","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/3653.001.0001","volume-title":"Image and Brain: the Resolution of the Imagery Debate","author":"SM Kosslyn","year":"1994","unstructured":"Kosslyn, S.M.: Image and Brain: the Resolution of the Imagery Debate. MIT Press, Cambridge (1994)"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1090\u20131098 (2012)","key":"6_CR36"},{"unstructured":"Kulkarni, T.D., Whitney, W.F., Kohli, P., Tenenbaum, J.B.: Deep convolutional inverse graphics network. In: Advances in Neural Information Processing Systems, pp. 2539\u20132547 (2015)","key":"6_CR37"},{"key":"6_CR38","first-page":"1","volume":"1","author":"H Larochelle","year":"2009","unstructured":"Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 1, 1\u201340 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR39","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s42154-018-0009-9","volume":"1","author":"J Li","year":"2018","unstructured":"Li, J., Cheng, H., Guo, H., Qiu, S.: Survey on artificial intelligence for vehicles. Autom. Innov. 1, 2\u201314 (2018)","journal-title":"Autom. Innov."},{"key":"6_CR40","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11\u201326 (2017)","journal-title":"Neurocomputing"},{"doi-asserted-by":"crossref","unstructured":"Marblestone, A.H., Wayne, G., Kording, K.P.: Toward an integration of deep learning and neuroscience. Front. Comput. Neurosci. 10 (2016). Article 94","key":"6_CR41","DOI":"10.3389\/fncom.2016.00094"},{"key":"6_CR42","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.tins.2009.04.002","volume":"32","author":"K Meyer","year":"2009","unstructured":"Meyer, K., Damasio, A.: Convergence and divergence in a neural architecture for recognition and memory. Trends Neurosci. 32, 376\u2013382 (2009)","journal-title":"Trends Neurosci."},{"key":"6_CR43","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1098\/rstb.2008.0314","volume":"364","author":"ST Moulton","year":"2009","unstructured":"Moulton, S.T., Kosslyn, S.M.: Imagining predictions: mental imagery as mental emulation. Philos. Trans. Roy. Soc. B 364, 1273\u20131280 (2009)","journal-title":"Philos. Trans. Roy. Soc. B"},{"volume-title":"The Oxford Handbook of 4E Cognition","year":"2018","unstructured":"Newen, A., Bruin, L.D., Gallagher, S. (eds.): The Oxford Handbook of 4E Cognition. Oxford University Press, Oxford (2018)","key":"6_CR44"},{"key":"6_CR45","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.cogsys.2017.03.005","volume":"44","author":"JS Olier","year":"2017","unstructured":"Olier, J.S., Barakova, E., Regazzoni, C., Rauterberg, M.: Re-framing the characteristics of concepts and their relation to learning and cognition in artificial agents. Cogn. Syst. Res. 44, 50\u201368 (2017)","journal-title":"Cogn. Syst. Res."},{"key":"6_CR46","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/TITS.2019.2896375","volume":"21","author":"A Plebe","year":"2019","unstructured":"Plebe, A., Da Lio, M., Bortoluzzi, D.: On reliable neural network sensorimotor control in autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 21, 711\u2013722 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"doi-asserted-by":"crossref","unstructured":"Plebe, A., Don\u00e0, R., Rosati Papini, G.P., Da Lio, M.: Mental imagery for intelligent vehicles. In: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems, pp. 43\u201351. INSTICC, SciTePress (2019)","key":"6_CR47","DOI":"10.5220\/0007657500430051"},{"unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: Xing, E.P., Jebara, T. (eds.) Proceedings of Machine Learning Research, pp. 1278\u20131286 (2014)","key":"6_CR48"},{"key":"6_CR49","volume-title":"Computational Neuroscience of Vision","author":"E Rolls","year":"2002","unstructured":"Rolls, E., Deco, G.: Computational Neuroscience of Vision. Oxford University Press, Oxford (2002)"},{"doi-asserted-by":"crossref","unstructured":"Ros, G., Vazquez, L.S.J.M.D., Lopez, A.M.: The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3234\u20133243 (2016)","key":"6_CR50","DOI":"10.1109\/CVPR.2016.352"},{"key":"6_CR51","volume-title":"Digital Picture Processing","author":"A Rosenfeld","year":"1982","unstructured":"Rosenfeld, A., Kak, A.C.: Digital Picture Processing, 2nd edn. Academic Press, New York (1982)","edition":"2"},{"key":"6_CR52","first-page":"1","volume-title":"Backpropagation: Theory, Architectures and Applications","author":"DE Rumelhart","year":"1995","unstructured":"Rumelhart, D.E., Durbin, R., Golden, R., Chauvin, Y.: Backpropagation: The basic theory. In: Chauvin, Y., Rumelhart, D.E. (eds.) Backpropagation: Theory, Architectures and Applications, pp. 1\u201334. Lawrence Erlbaum Associates, Mahwah (1995)"},{"volume-title":"Parallel Distributed Processing: Explorations in the Microstructure of Cognition","year":"1986","unstructured":"Rumelhart, D.E., McClelland, J.L. (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge (1986)","key":"6_CR53"},{"key":"6_CR54","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"doi-asserted-by":"crossref","unstructured":"Schwarting, W., Alonso-Mora, J., Rus, D.: Planning and decision-making for autonomous vehicles. Ann. Rev. Control Robot. Auton. Syst. 1, 8.1\u20138.24 (2018)","key":"6_CR55","DOI":"10.1146\/annurev-control-060117-105157"},{"key":"6_CR56","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1146\/annurev.neuro.051508.135546","volume":"33","author":"CA Seger","year":"2010","unstructured":"Seger, C.A., Miller, E.K.: Category learning in the brain. Ann. Rev. Neurosci. 33, 203\u2013219 (2010)","journal-title":"Ann. Rev. Neurosci."},{"doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, J., et al. (eds.) Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 240\u2013248 (2017)","key":"6_CR57","DOI":"10.1007\/978-3-319-67558-9_28"},{"doi-asserted-by":"crossref","unstructured":"Tripp, B.P.: Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks. In: International Joint Conference on Neural Networks, pp. 3551\u20133560 (2017)","key":"6_CR58","DOI":"10.1109\/IJCNN.2017.7966303"},{"unstructured":"Tschannen, M., Lucic, M., Bachem, O.: Recent advances in autoencoder-based representation learning. In: NIPS Workshop on Bayesian Deep Learning (2018)","key":"6_CR59"},{"key":"6_CR60","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1126\/science.aau6595","volume":"363","author":"S Ullman","year":"2019","unstructured":"Ullman, S.: Using neuroscience to develop artificial intelligence. Science 363, 692\u2013693 (2019)","journal-title":"Science"},{"key":"6_CR61","volume-title":"The Visual Neurosciences","author":"DC Van Essen","year":"2003","unstructured":"Van Essen, D.C.: Organization of visual areas in macaque and human cerebral cortex. In: Chalupa, L., Werner, J. (eds.) The Visual Neurosciences. MIT Press, Cambridge (2003)"},{"key":"6_CR62","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371\u20133408 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR63","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-28820-1","volume-title":"The Variational Bayes Method in Signal Processing","author":"V \u0160m\u00eddl","year":"2005","unstructured":"\u0160m\u00eddl, V., Quinn, A.: The Variational Bayes Method in Signal Processing. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/3-540-28820-1"},{"key":"6_CR64","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1038\/nrn3112","volume":"12","author":"DM Wolpert","year":"2011","unstructured":"Wolpert, D.M., Diedrichsen, J., Flanagan, R.: Principles of sensorimotor learning. Nat. Rev. Neurosci. 12, 739\u2013751 (2011)","journal-title":"Nat. Rev. Neurosci."},{"key":"6_CR65","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 7\u201315 (2010)","key":"6_CR66","DOI":"10.1109\/CVPR.2010.5539957"},{"doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: International Conference on Computer Vision, pp. 6\u201314 (2011)","key":"6_CR67","DOI":"10.1109\/ICCV.2011.6126474"},{"unstructured":"Zhao, J., Mathieu, M., Goroshin, R., LeCun, Y.: Stacked what-where auto-encoders. In: International Conference on Learning Representations, pp. 1\u201312 (2016)","key":"6_CR68"}],"container-title":["Communications in Computer and Information Science","Smart Cities, Green Technologies and Intelligent Transport Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68028-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T05:04:29Z","timestamp":1614920669000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68028-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030680275","9783030680282"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68028-2_6","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"VEHITS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Vehicle Technology and Intelligent Transport Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion, Crete","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icvti2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.vehits.org\/?y=2019","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"90","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}