Open AccessMULTIPLE CLASSIFICATION ALGORITHMS UNIMODAL AND MULTIMODAL TARGET RECOGNITION SYSTEMS
Author(s) -
Veaceslav Perju
Publication year - 2021
Publication title -
journal of engineering science
Language(s) - English
Resource type - Journals
eISSN - 2587-3482
pISSN - 2587-3474
DOI - 10.52326/jes.utm.2021.28(3).07
Subject(s) - computer science , robustness (evolution) , cognitive neuroscience of visual object recognition , reliability (semiconductor) , optimal distinctiveness theory , artificial intelligence , graph , pattern recognition (psychology) , algorithm , machine learning , object (grammar) , theoretical computer science , psychology , biochemistry , chemistry , power (physics) , physics , quantum mechanics , psychotherapist , gene
Target recognition is of great importance in military and civil applications – object detection, security and surveillance, access and border control, etc. In the article the general structure and main components of a target recognition system are presented. The characteristics such as availability, distinctiveness, robustness, and accessibility are described, which influence the reliability of a TRS. The graph presentations and mathematical descriptions of a unimodal and multimodal TRS are given. The mathematical models for a probability of correct target recognition in these systems are presented. To increase the reliability of TRS, a new approach was proposed – to use a set of classification algorithms in the systems. This approach permits the development of new kinds of systems - Multiple Classification Algorithms Unimodal and Multimodal Systems (MAUMS and MAMMS). The graph presentations, mathematical descriptions of the MAUMS and MAMMS are described. The evaluation of the correct target recognition was made for different systems. The conditions of systems' effectiveness were established. The modality of the algorithm's recognition probabilitymaximal value determination for an established threshold level of the system's recognition probability was proposed, which will describe the requirements for the quality and, respectively, the costs of the recognition algorithms. The proposed theory permits the system's design depending on a predetermined recognition probability.