SIMPLE OBJECTS DETECTION AND RECOGNITION BY THE PROBABILISTIC APPROACH - Kharchenko, Volodymyr; Kukush, Alexander; Chyrka, Iurii (Proceedings of National Aviation University (archive), 2017)
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.