{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:14:58Z","timestamp":1772910898726,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319750248","type":"print"},{"value":"9783319750255","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","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":[[2018]]},"DOI":"10.1007\/978-3-319-75025-5_10","type":"book-chapter","created":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T00:02:57Z","timestamp":1517875377000},"page":"98-110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Convolutional Neural Networks Implementations for Computer Vision"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3644-7536","authenticated-orcid":false,"given":"Pawe\u0142","family":"Michalski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-1778","authenticated-orcid":false,"given":"Bogdan","family":"Ruszczak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6672-3971","authenticated-orcid":false,"given":"Micha\u0142","family":"Tomaszewski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,2,7]]},"reference":[{"key":"10_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-319-13820-6_2","volume-title":"Big Data Analytics","author":"S Batra","year":"2014","unstructured":"Batra, S., Sachdeva, S.: Suitability of data models for electronic health records database. In: Srinivasa, S., Mehta, S. (eds.) BDA 2014. LNCS, vol. 8883, pp. 14\u201332. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-13820-6_2"},{"issue":"4","key":"10_CR2","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1007\/s40534-016-0117-3","volume":"24","author":"SA Bagloee","year":"2016","unstructured":"Bagloee, S.A., Tavana, M., Asadi, M., et al.: Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J. Mod. Transport. 24(4), 284\u2013303 (2016). https:\/\/doi.org\/10.1007\/s40534-016-0117-3","journal-title":"J. Mod. Transport."},{"key":"10_CR3","doi-asserted-by":"publisher","unstructured":"Pal, S.K., Meher, S.K., Skowron, A.: Data science, big data and granular mining. Pattern Recogn. Lett. 67(2), 109\u2013112 (2015). https:\/\/doi.org\/10.1016\/j.patrec.2015.08.001","DOI":"10.1016\/j.patrec.2015.08.001"},{"key":"10_CR4","doi-asserted-by":"publisher","unstructured":"H\u00e4ne, C., Sattler, T., Pollefeys, M.: Obstacle detection for self-driving cars using only monocular cameras and wheel odometry. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems, IROS. Hamburg (2015). https:\/\/doi.org\/10.1109\/IROS.2015.7354095","DOI":"10.1109\/IROS.2015.7354095"},{"key":"10_CR5","unstructured":"Salman, Y.D., Ku-Mahamud, K.R., Kamioka, E.: Distance measurement for self-driving cars using stereo camera. In: Proceedings of the 6th International Conference on Computing and Informatics, ICOCI 2017, Kuala Lumpur (2017)"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Hohm, A., Lotz, F., Fochler, O., Lueke, S., Winner, H.: Automated Driving in Real Traffic: from Current Technical Approaches towards Architectural Perspectives. SAE Technical Paper (2014)","DOI":"10.4271\/2014-01-0159"},{"key":"10_CR7","doi-asserted-by":"publisher","unstructured":"Karami, E., Prasad, S., Shehata, M.: Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. In: Newfoundland Electrical and Computer Engineering Conference, IEEE, Newfoundland and Labrador Section At St. John\u2019s, NL (2015). https:\/\/doi.org\/10.13140\/RG.2.1.1558.3762","DOI":"10.13140\/RG.2.1.1558.3762"},{"key":"10_CR8","unstructured":"Amodei, D., Olah, C., Steinhardt, J., Christiano,,P., Schulman, J., Man, D.: Concrete Problems in AI Safety (2016). arxiv.org\/abs\/1606.06565"},{"key":"10_CR9","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"issue":"11","key":"10_CR10","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"IEEE"},{"key":"10_CR11","volume-title":"A Field Guide to Dynamical Recurrent Neural Networks","author":"S Hochreiter","year":"2001","unstructured":"Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: Kremer, S.C., Kolen, J.F. (eds.) A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press, Hoboken (2001)"},{"key":"10_CR12","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/S0079-6123(06)65034-6","volume":"165","author":"GE Hinton","year":"2007","unstructured":"Hinton, G.E.: To recognize shapes, first learn to generate images. Prog. Brain Res. 165, 535\u2013547 (2007)","journal-title":"Prog. Brain Res."},{"key":"10_CR13","doi-asserted-by":"crossref","DOI":"10.1561\/9781601982957","volume-title":"Learning Deep Architectures for AI","author":"Y Bengio","year":"2009","unstructured":"Bengio, Y.: Learning Deep Architectures for AI. Now Publishers, Boston (2009)"},{"key":"10_CR14","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems (2012)"},{"key":"10_CR15","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks (2014). arxiv.org\/abs\/1406.2661"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision (2015). arxiv.org\/abs\/1502.01852","DOI":"10.1109\/ICCV.2015.123"},{"issue":"3","key":"10_CR17","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vis."},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10_CR19","unstructured":"ImageNet Project. http:\/\/image-net.org"},{"key":"10_CR20","doi-asserted-by":"publisher","unstructured":"Cao, J., et al.: A parallel Adaboost-Backpropagation neural network for massive image dataset classification, Sci. Rep. 6(38201) (2016). https:\/\/doi.org\/10.1038\/srep38201","DOI":"10.1038\/srep38201"},{"key":"10_CR21","doi-asserted-by":"publisher","unstructured":"Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.222","DOI":"10.1109\/CVPR.2014.222"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Marszalek, M., Schmid, C., Harzallah, H., Weijer, J.: Learning object representations for visual object class recognition. In: Visual Recognition Challange workshop, ICCV (2007)","DOI":"10.1109\/CVPR.2007.383272"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Yan, S., Dong, J., Chen, Q., Song, Z., Pan, Y., Xia, W., Huang, Z., Hua, Y., Shen, S.: Generalized hierarchical matching for sub-category aware object classification. In: Visual Recognition Challenge workshop, ECCV (2012)","DOI":"10.1109\/CVPR.2013.112"},{"key":"10_CR24","unstructured":"SpaceNet. http:\/\/explore.digitalglobe.com\/spacenet"},{"key":"10_CR25","volume-title":"Perceptrons: An Introduction to Computational Geometry","author":"S Papert","year":"1988","unstructured":"Papert, S., Minsky, M.: Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge (1988)"},{"key":"10_CR26","doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1007\/BF00344251","journal-title":"Biol. Cybern."},{"issue":"1","key":"10_CR27","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR28","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"},{"key":"10_CR29","unstructured":"Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-scale Image Recognition (2014). arxiv.org\/abs\/1409.1556"},{"key":"10_CR30","unstructured":"Szegedy, C., et al.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (2016). arxiv.org\/abs\/1602.07261"},{"key":"10_CR31","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR32","unstructured":"Yong-Deok, K., Eunhyeok, P., Sungjoo, Y., Taelim, C., Lu, Y., Dongjun, S.: Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications (2016). arxiv.org\/abs\/1511.06530"}],"container-title":["Advances in Intelligent Systems and Computing","Biomedical Engineering and Neuroscience"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-75025-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T11:36:04Z","timestamp":1603798564000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-75025-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319750248","9783319750255"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-75025-5_10","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}