| Abstract [eng] |
Data classification is a complex process in which the set of initial data is treated to create a system that allows the identification of automated objects and data flows. The paper presents and discusses the main methods of object identification, seeks to examine whether the modification of models increases the accuracy of object recognition. The aim is to analyze the methods of statistical object detection, offer a new hybrid method and verify its reliability. In order to achieve this objective, analytical part deals with methods of object identification and analyzes the object recognition problems. Further, the description of the proposed hybrid method is suggested and its theoretical benefits are indicated. Experimental part describes the whole research process, data treatment processing and summarizes the results. After evaluating the methods of object identification, hybrid method, that unifies linear discriminant and methods of neuron system, is proposed. Comparing the standard object recognition methods with the hybrid ones, it was demonstrated, that the hybrid methods allow for greater accuracy (approximately 10%) in the process of identification. Paper consists of 56 pages, inventory is enlivened by 25 pictures, 7 tables and 2 appendixes. |
