Devising information technology for determining the redundant information content of a digital image
| Autor: | Pylyp Prystavka, Kseniia Dukhnovska, Oksana Kovtun, Olga Leshchenko, Olha Cholyshkina, Anhelina Zhultynska |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
image preprocessing
Applied Mathematics Mechanical Engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Energy Engineering and Power Technology HD2321-4730.9 Industrial and Manufacturing Engineering computer vision Computer Science Applications image recognition Control and Systems Engineering Management of Technology and Innovation T1-995 Industry Electrical and Electronic Engineering digital image informativeness Technology (General) |
| Zdroj: | Eastern-European Journal of Enterprise Technologies, Vol 6, Iss 2 (114), Pp 59-70 (2021) |
| ISSN: | 1729-4061 1729-3774 |
| Popis: | The information technology that implements evaluation of redundant information using the methods of preprocessing and segmentation of digital images has been devised. The metrics for estimating redundant information containing a photo image using the approach based on texture variability were proposed. Using the example of aerial photography data, practical testing and research into the proposed assessment were carried out. Digital images, formed by various optoelectronic facilities, are distorted under the influence of obstacles of various nature. These obstacles complicate both the visual analysis of images by a human and their automatic processing. A solution to the problem can be obtained through preprocessing, which will lead to an increase in the informativeness of digital image data at a general decrease in content. An experimental study of the dependence of image informativeness on the results of overlaying previous filters for processing digital images, depending on the values of parameters of methods, was carried out. It was established that the use of algorithms sliding window analysis can significantly increase the resolution of analysis in the time area while maintaining a fairly high ability in the frequency area. The introduced metrics can be used in problems of computer vision, machine and deep learning, in devising information technologies for image recognition. The prospect is the task of increasing the efficiency of processing the monitoring results by automating the processing of the received data in order to identify informative areas. This will reduce the time of visual data analysis. The introduced metrics can be used in the development of automated systems of air surveillance data recognition. |
| Databáze: | OpenAIRE |
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