Dmytro Peleshko

Work place: Lviv Polytechnic National University, Lviv, Ukraine



Research Interests: Analysis of Algorithms, Image Processing, Machine Learning, Computer Vision, Artificial Intelligence


Dmytro Peleshko, Dr. Sc., Professor at Lviv Polytechnic National University, Lviv, Ukraine. He has published more than 100 papers in international and national scientific issues and journals and he is the author of several monographs. His scientific interests are image and video processing for the system of artificial intelligence.

Author Articles
Video-based Flame Detection using LBP-based Descriptor: Influences of Classifiers Variety on Detection Efficiency

By Oleksii Maksymiv Taras Rak Dmytro Peleshko

DOI:, Pub. Date: 8 Feb. 2017

Techniques to detect the flame at an early stage are necessary in order to prevent the fire and minimize the damage. The flame detection technique based on the physical sensor has limited disadvantages in detecting the fire early. This paper presents the results of using local binary patterns for solving flames detecting problem and proposes modifications to improve the quality of detector work. Experimentally found that using support vector machines classifier with a kernel based on Gaussian radial basis functions shows the best results compared to other SVM cores or classifier k-nearest neighbors.

[...] Read more.
Image Superresolution via Divergence Matrix and Automatic Detection of Crossover

By Dmytro Peleshko Taras Rak Ivan Izonin

DOI:, Pub. Date: 8 Dec. 2016

The paper describes the image superresolution method with aggregate divergence matrix and automatic detection of crossover. Formulation of the problem, building extreme optimization task and its solution for solving the automation determination of the crossover coefficient is presented. Different ways for building oversampling images algorithms based on the proposed method are shows. Based on practical experiments shows the effectiveness of the procedure of automatically the determination of the crossover coefficient. Experimentally established the effectiveness of the procedures oversampling images at high zoom resolution by the developed method.

[...] Read more.
Other Articles