The properties of computing processes in image analysis and machine learning tasks
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The solving process of any computer vision or machine learning task can be represented in form of some sequence of computational operations on input data. The feature of intelligent data analysis is significant input data heterogeneity which includes emissions, measurement uncertainty, and multimodality. Different types of computing operations respond differently to the presented types of mismatches. The quality of problem solution to a large extent depends on the properties and data mismatch stability of the basic operations. The article describes main types of computational operations, used in computer vision and machine learning algorithms, the analysis of their resistance to various types of mismatch in the data is provided. The information will be useful in designing visual objects descriptors, in the development of detection and tracking algorithms. Of particular value is using of the information in design and analysis of deep convolutional neural networks.
Computer vision, machine learning, convolutional neural networks, tracking, filtering
Короткий адрес: https://sciup.org/147155163
IDR: 147155163 | DOI: 10.14529/ctcr170114
Список литературы The properties of computing processes in image analysis and machine learning tasks
- Leibe, B. An implicit shape model for combined object categorization and segmentation/B. Leibe, A. Leonardis, B. Schiele. -Springer Berlin Heidelberg, 2006. -P. 508-524.
- Гонсалес, Р. Цифровая обработка изображений/Р. Гонсалес, Р. Вудс. -М.: Техносфера, 2005. -1072 с.
- Nagi, J. Max-pooling convolutional neural networks for vision-based hand gesture recognition/J. Nagi, F. Ducatelle, G.A. DiCaro et al.//IEEE International Conference on Signal and Image Processing Applications (ICSIPA) -IEEE, 2011. -P. 342-347.
- Magee, J.F. Decision trees for decision making/J.F. Magee//Harvard Business Review. -1964. -Vol. 42, no. 4. -pp. 126-138.
- Krizhevsky, A. Imagenet classification with deep convolutional neural networks/A. Krizhevsky, I. Sutskever, G.E. Hinton//NIPS 2012: Neural Information Processing Systems. -Lake Tahoe, Nevada, 2012. -P. 1097-1105.
- Simonyan, K. Very deep convolutional networks for large-scale image recognition/K. Simonyan, A. Zisserman//arXiv technical report:1409.1556. -2014.
- Karnowski, E. Alexnet visualization/E. Karnowski. -July 15, 2015. -https://jeremykarnowski. wordpress.com/2015/07/15/alexnet-visualization/
- Azimi, M. Skeletal joint smoothing white paper/M. Azimi. -https://msdn.microsoft.com/en-us/library/jj131429.aspx
- Fischler, M.A. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography/M.A. Fischler, R.C. Bolles//Communications of the ACM. -1981. -Vol. 24, no. 6. -P. 381-395.
