Перегляд за Автор "Yakovleva, Olena Vladimirovna"
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Документ Forecasting the results of football matches on the Internet based information(НТУ "ХПИ", 2017) Klyuchka, Yaroslav Aleksandrovich; Cherednichenko, Olga Yurevna; Vasylenko, Artem Viktorovich; Yakovleva, Olena VladimirovnaThe purpose of the article is making a model of results forecasting for football matches, which works better than bookmakers organizations. Lately the popularity of football forecasting has been increased. The existing statistic approaches show difficult and low prediction. The developed model for predicting the results of football matches uses information about the previous results of the teams. The forecast is based on forecasting factors. Although it is difficult to consider all the factors that influence the results of matches the model makes an attempt to find the most important ones. The described model of forecasting takes into consideration such characteristics as: place in the tournament table; total points; goal difference; total number of players, skipping the match; points home / away; home match; "defence strength"; "attack strength"; team form. Testing shows that forecasting and actual results of football matches coincide. The offered model could be used in commercial computer programs for forecasting results of football matches in bookmakers organizations.Документ Research of descriptor based image normalization and comparative analysis of SURF, SIFT, BRISK, ORB, KAZE, AKAZE(Національний технічний університет "Харківський політехнічний інститут", 2020) Yakovleva, Olena Vladimirovna; Nikolaieva, KaterynaThe subject of research is image normalization based on key points analysis. The purpose is development of mathematical models and their software implementation for normalization of image geometric transformations based on the analysis of SIFT, SURF, ORB, BRISK, KAZE, AKAZE descriptors; the model application for comparative analysis of descriptors based on expert assessments of normalization quality, time costs and other indicators; construction and usage in experiments the own dataset with 100 real image pairs which contains scenes of five types: buildings, plane images outside, plane images inside, natural and artificial textures; making conclusions about the performance of the considered descriptors to solve the normalization problem. Such methods are applied: SIFT, SURF, ORB, BRISK, KAZE, AKAZE descriptors for describing key points, the Nearest Neighbor Distance Ratio method or symmetric method for search of corresponding pairs of key points from different images, the RANSAC method for rejecting false correspondences and obtaining a homography matrix, similarity measures, software modeling. The results obtained: experimental normalization results by SIFT, SURF, ORB, BRISK, KAZE, AKAZE descriptors for 100 real pairs of own dataset (normalized images, their overlaps, quantitative descriptor evaluation, precision and recall estimation, time costs estimation, expert quality assessment, conversion of all indicator values to an 8-point rating scale); summary diagrams and conclusions about advantages and weaknesses of the compared descriptors; recommendations about the most-suitable-algorithm selection for solving normalization problem in specific cases.