No abstract

PhD, Researcher Department of collection, processing and analysis of test results of robotic complexes, Main research and testing robotics centre of the Ministry of defence of the Russian Federation (MRTRC)

Today, there is a serious need to improve the performance of algorithms for detecting objects in images. This process can be accelerated with the help of preliminary processing, having found areas of interest on the images where the probability of object detection is high. To this end, it is proposed to use the algorithm for distinguishing the boundaries of objects using the Sobel operator and Kohonen self-organizing maps, described in this paper and shown by the example of determining zones of interest when searching and recognizing objects in satellite images. The presented algorithm allows 15-100 times reduction in the amount of data arriving at the convolutional neural network, which provides the final recognition. Also, the algorithm can significantly reduce the number of training images, since the size of the parts of the input image supplied to the convolution network is tied to the image scale and equal to the size of the largest recognizable object, and the object is centered in the frame. This allows to accelerate network learning by more than 5 times and increase recognition accuracy by at least 10 %, as well as halve the required minimum number of layers and neurons of the convolutional network, thereby increasing its speed.

Development of a mathematical model of scrambler-type speech-like interference generator for system of prevent speech information from leaking via acoustic and vibration channels. Technology Audit and Production Reserves, 5 (2 (49)), 19-26.

An integrated constrained fuzzy stochastic analytic hierarchy process method with application to the choice problem. Expert Systems with Applications,