Devising information technology for determining the redundant information content of a digital image

Abstract

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.

Recognition of aerial photography objects based on data sets with different aggregation of classes

“…Currently, a large number of scientific papers address the development of information technologies of computer vision. In [4], the results of experimental studies into the dependence of informativeness of images on the results of overlaying previous filters for processing digital images, depending on the values of the parameters of the methods, are given. In this paper, it is established that the use of analysis algorithms using a sliding window can significantly increase the resolution of analysis in the time domain while maintaining a sufficiently high ability in the frequency domain.…”