Abstract
The paper covers the current state of the art regarding the use of machine learning mechanisms, and in particular the deep convolutional neural networks used in the field of computer vision. In the article there has been presented the current definition of deep learning and specific dependencies between related fields such as machine learning and artificial intelligence. The practical part of the work consists of three components: the features of the structure of the convolutional neural network, the distinction of its key elements, the description of their actions, the compilation of information about available learning sets used in network testing and verification processes, and the review of the implementation of convolutional neural networks, which had a significant impact on development of discipline. To illustrate the great potential of the presented tools for solving computer vision tasks, the study highlites examples of their applications. The possibility of using convolutional neural networks for identification of technical objects in digital images is indicated.
Paweł Michalski, PhD. Eng., Assistant Professor; Bogdan Ruszczak, PhD. Eng., Assistant Professor; Michał Tomaszewski, PhD. Eng., Associate Professor.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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. 14–32. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13820-6_2
Bagloee, S.A., Tavana, M., Asadi, M., et al.: Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J. Mod. Transport. 24(4), 284–303 (2016). https://doi.org/10.1007/s40534-016-0117-3
Pal, S.K., Meher, S.K., Skowron, A.: Data science, big data and granular mining. Pattern Recogn. Lett. 67(2), 109–112 (2015). https://doi.org/10.1016/j.patrec.2015.08.001
Häne, 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
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)
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)
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’s, NL (2015). https://doi.org/10.13140/RG.2.1.1558.3762
Amodei, D., Olah, C., Steinhardt, J., Christiano,,P., Schulman, J., Man, D.: Concrete Problems in AI Safety (2016). arxiv.org/abs/1606.06565
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. IEEE 86(11), 2278–2324 (1998)
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)
Hinton, G.E.: To recognize shapes, first learn to generate images. Prog. Brain Res. 165, 535–547 (2007)
Bengio, Y.: Learning Deep Architectures for AI. Now Publishers, Boston (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems (2012)
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
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
Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
ImageNet Project. http://image-net.org
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
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
Marszalek, M., Schmid, C., Harzallah, H., Weijer, J.: Learning object representations for visual object class recognition. In: Visual Recognition Challange workshop, ICCV (2007)
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)
SpaceNet. http://explore.digitalglobe.com/spacenet
Papert, S., Minsky, M.: Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge (1988)
Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193–202 (1980). https://doi.org/10.1007/BF00344251
Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
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. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-scale Image Recognition (2014). arxiv.org/abs/1409.1556
Szegedy, C., et al.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (2016). arxiv.org/abs/1602.07261
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Michalski, P., Ruszczak, B., Tomaszewski, M. (2018). Convolutional Neural Networks Implementations for Computer Vision. In: Hunek, W., Paszkiel, S. (eds) Biomedical Engineering and Neuroscience. BCI 2018. Advances in Intelligent Systems and Computing, vol 720. Springer, Cham. https://doi.org/10.1007/978-3-319-75025-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-75025-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75024-8
Online ISBN: 978-3-319-75025-5
eBook Packages: EngineeringEngineering (R0)