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.
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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
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