izvorni znanstveni rad
Suspected Hazardous Area extent is a very important task. It can be done by using advanced computer vision methods and artificial intelligence algorithms on airborne and space imagery in order to extract new information of SHA and to detect indicators of mine presence. We introduce the concept and describe a procedure of strong mine presence indicators detection by using convolutional neural networks and rule-based inference. Also, we propose a recommender system that improves detection quality with interactive relevance feedback. Such a system may also assist in post-processing procedures and classification of indicators after their detection.
Strong mine presence indicators, DNN, Rule Based System
Razvojem virtualizacijskih tehnologija raste kompleksnost IT sustava, dok korisnici istovremeno zahtijevaju brži pristup resursima, napredne integracije i jednostavnost korištenja. Jedno od ključnih rješenja za ove izazove primjena je…
Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…