(Вісник Національного Авіаційного Університету, 2017-12-27) Kharchenko, Volodymyr; Kukush, Alexander; Chyrka, Iurii; Харченко, В.П.; Кукуш, О.Г.; Чирка, Ю.Д.; Харченко, Володимир Петрович
Purpose: The represented research results are aimed to better understanding of computer vision methods
and their capabilities. The statistical approach of object detection and recognition allows processing of
typical objects with simple descriptors. Methods: Considered approach is grounded at probabilistic
methods, kernel density estimation and computer-based simulation as a verification tool. Results:
Considered approach for object detection and recognition has shown several advantages in comparison with
existing methods due to its simple realization and small time of data processing. Presented results of
experimental verification prove that the considered method can be applied for detection and classification of
objects with various shapes. Discussion: The approach can be implemented in a variety of computer vision
systems that observe objects in difficult noisy conditions.
