Кафедра авіаційних комп'ютерно-інтегрованих комплексів (НОВА)
Permanent URI for this communityhttp://er.nau.edu.ua/handle/NAU/58724
News
Відповідальний за розділ: Провідний фахівець кафедри авіаційних комп'ютерно-інтегрованих комплексів інституту інформаційно-діагностичних систем Шугалєй Людмила Петрівна. E-mail: shugaley2005@ukr.net
Browse
Browsing Кафедра авіаційних комп'ютерно-інтегрованих комплексів (НОВА) by Author "Barkulova, I. V."
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
Item ACCURACY RESEARCH METHOD OF THE MODIFIED ALGORITHM FOR DETECTING LINEAR LANDMARKS(Київ «Освіта України», 2018-06) Mukhina, M. P.; Tkachenko, O. Yu.; Barkulova, I. V.A search algorithm for the most extended landmark by which unmanned aerial vehicle can be followed by and implemented flight correction was proposed. The software was developed based on the Python language. The functionality of this software is to detect the linear landmarks from images of geophysical field, received from unmanned aerial vehicle in real time. Images were processed by Hough Line Transform method. As a result, obtained visualization of the object detection with the greatest length, as linear landmark, which allows to estimate unmanned aerial vehicle location. The visual analysis of the effectiveness of this algorithm for inertial navigation system correction shown that the algorithmic software is appropriate for use on unmanned aerial vehicle board and due to applying computer vision systems, gives as correct results of location determining as possible.Item ALGORITHM OF VARIATIVE FEATURE DETECTION AND PREDICTION IN CONTEXT-DEPENDENT RECOGNITION(Київ «Освіта України», 2018-03) Mukhina, M. P.; Barkulova, I. V.Application of context-dependent classification for recognition tasks is proposed. In the context-free classification, the starting point was the Bayesian classifier. Morphological features such as object form, area, and eccentricity were considered through context-dependent classification. As result, dependences which can be used for object recognition have been obtained, and further they can be used together with interesting point detectors. The procedure of prediction of object variative features was developed.Item STRUCTURE OF AIDED CLASSIFICATION OF GROUND OBJECTS BY VIDEO OBSERVATION(«Освіта України», 2017-12) Mukhina, M. P.; Barkulova, I. V.Analysis of classification structure by video observation has been done. It was formulated, that for feature extraction and their classification, normalized hypothesis for object feature detection, taking into account camera orientation and flight height, have being obtained. The system with aided classification based on probabilistic models, such as Bayesian classifier and Markov chain model, is proposed. The applied algorithm was used for detection by only two features related to Binary Large Objects (BLOB) analyses. Classification was done by two main feature parameters: area and center of mass. Feature vector contains the most informative components and allows the minimization of decision risks. Results have proven the reliability of classification during a number of video frames in the condition of non-full data descriptive space.