Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Object classification is a problem which has attracted a lot of research attention in recent years. Traditional approach to this problem is built on a shallow trainable architecture that was meant to detect handcrafted features. That approach works poorly and introduces many complications in situations where one is to work with more than a couple types of objects in an image with a large resolution. That is why in the past few years convolutional and residual neural networks have experienced a tremendous rise in popularity. In this paper, we provide a review on topics related to artificial neural networks and a brief overview of our research. Our review begins with a short introduction to the topic of computer vision. Afterwards we cover briefly the concepts of neural networks, convolutional and residual neural networks and their commonly used models. Then we provide a comparative performance analysis of the previously mentioned models in a binary and multi-label classification problem. Finally, multiple conclusions are drawn, which are to serve as guidelines for future computer vision systems implementations.
Rocznik
Tom
Strony
43--56
Opis fizyczny
Bibliogr. 14 poz., rys., wykr.
Twórcy
autor
- Siedlce University of Natural Sciences and Humanities, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland
autor
- Siedlce University of Natural Sciences and Humanities, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland
Bibliografia
- 1. B. Jahne (Ed.): Computer vision and applications: a guide for students and practitioners. Elsevier, 2000.
- 2. D. G. Lowe: Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004.
- 3. H. Bay, A. Ess, T. Tuytelaars, and L. van Gool: Speeded-up robust features (surf). Computer vision and image understanding, 110(3):346-359, 2008.
- 4. P. F. Alcantarilla, J. Nuevo, and A. Bartoli: Fast explicit diffusion for accelerated features in nonlinear scale spaces. In British Machine Vision Conf. (BMVC), 2013.
- 5. W. S. McCulloch, W. Pitts: A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4):115-133, 1943.
- 6. M. Minsky, S. A. Papert. Perceptrons: An introduction to computational geometry. MIT press, 2017.
- 7. B. Kröse, P. van der Smagt: An introduction to neural networks. 1993.
- 8. K. He, X. Zhang, S. Ren, and J. Sun: Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
- 9. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen: MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510-4520, 2018.
- 10. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna: Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016.
- 11. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi” Inception-v4, Inception-Resnet and the impact of residual connections on learning. arXiv preprint:1602.07261, 2016
- 12. G. Amato, F. Carrara, F. Falchi, C. Gennaro, C. Meghini, and C. Vairo: Deep learning for decentralized parking lot occupancy detection. Expert Systems with Applications, 72:327-334, 2017.
- 13. G. Amato, F. Carrara, F. Falchi, C. Gennaro, and C. Vairo: Car parking occupancy detection using smart camera networks and deep learning. In Computers and Communication (ISCC), 2016 IEEE Symposium, pp. 1212-1217. IEEE, 2016.
- 14. A. Krizhevsky, G. Hinton, et al.: Learning multiple layers of features from tiny images. 2009.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a01caa2d-fc69-4a85-b140-08503d432510