A. V. Zaprivoda (Bauman Moscow State Technical University) :
Articles:
Valishin A. A. (Bauman Moscow State Technical University), Zaprivoda A. V. (Bauman Moscow State Technical University), Klonov A. S. (Bauman Moscow State Technical University)
doi: 10.18698/2309-3684-2024-1-93109
With the development of forecasting methods, the exclusion of random effects from the initial information and the studied processes becomes essential. These effects are associated not only with the impossibility of taking into account all factors, but also with the fact that some of them are often not taken into account at all. It is important not to forget about random measurement errors. In the predicted values, due to these effects, a kind of random offset or "noise" is created. Filtering (exclusion) of noise should, of course, increase the reliability and justifiability of forecasts. This article discusses the principles of real-time data filtering. The problem statement is given, as well as the main evaluation criteria that must be met to obtain a satisfactory result. In addition, the principle of operation of the two most common types of filters – absolutely optimal and conditionally optimal - is analyzed, their advantages and disadvantages are described. The application of Kalman and Pugachev filters to a model with two sensors is considered. Some conclusions and recommendations are presented on in which cases it is better to use one or another filter.
Валишин А.А., Запривода А.В., Клонов А.С. Математическое моделирование и сравнительный анализ численных методов решения задачи непрерывнодискретной фильтрации случайных процессов в реальном времени. Математическое моделирование и численные методы, 2024, № 1, с. 93–109.
519.7 Modeling and efficiency analysis of perceptual hash functions for segmented image search
Valishin A. A. (Bauman Moscow State Technical University), Zaprivoda A. V. (Bauman Moscow State Technical University), Tsukhlo S. S. (Bauman Moscow State Technical University)
doi: 10.18698/2309-3684-2024-2-4667
This research paper explores the use of perceptual hash functions to improve the retrieval efficiency of aerial photography and satellite remote sensing images segmented by a convolutional neural network. This paper describes three hashing algorithms. The first algorithm is based on the use of a low-pass filter and is aimed at reducing image detail in order to highlight the most stable image features. The second algorithm uses a two-dimensional discrete cosine transform to create an image hash. The third algorithm is based on the Radon transform, which allows you to extract information about the directions of lines in the image, as well as provide maximum invariance to the rotation transformation of the input image. The article also tests these algorithms, including analysis of their invariance to transformations for rotation, scaling and shifting of the source image. Test results show that the algorithm based on the Radon transform exhibits good rotation invariance, but is sensitive to the quality of segmentation, which can lead to frequent collisions when searching for similar images. Algorithms using a two-dimensional discrete cosine transform and an algorithm using a low-pass filter turned out to be more stable and have a smaller spread of values. However, it should be noted that algorithms using a low-pass filter and 2D discrete cosine transform may not be applicable to rotated images. Based on the results of analysis and comparison of the performance of the algorithms, it is recommended to give preference to either the second or third algorithm, because each of them has its own advantages and disadvantages, and the decision to use a specific algorithm in the task of finding the most similar image must take into account the specific conditions and limitations of the problem, as well as the requirements for the quality of image comparison.
Валишин А.А., Запривода А.В., Цухло С.С. Моделирование и сравнительный анализ эффективности перцептивных хеш-функций для поиска сегментированных изображений. Математическое моделирование и численные методы, 2024, № 2, с. 46-67.