A Comparative Review on Image Analysis with Machine Learning for Extended Reality (XR) Applications

Abstract

Progressions in Medical, Industrial 4.0, and training require more client communication with the information in reality. Extended reality (XR) development could be conceptualized as a wise advancement and a capable data variety device fitting for far off trial and error in image processing. This innovation includes utilizing the Head Mounted Devices (HMD) built-in with a functionalities like data collection, portability and reproducibility. This article will help to understand the different methodologies that are used for 3Dimensional (3D) mode of interaction with the particular data set in industries to refine the system and uncovers the bugs in the machineries. To identify the critical medical issues, the future technology can give a comfort for the medic to diagnose it quickly. Educators currently utilizing video animation are an up-rising pattern. Important methods used for various applications like, Improved Scale Invariant Feature Transform (SIFT), Block Orthogonal Matching Pursuit (BOMP), Oriented fast and Rotated Brief (ORB) feature descriptor and Kanade-Lucas-Tomasi (KLT), Semi-Global Block Matching (SGBM) algorithm etc., In high-speed real time camera, the position recognition accuracy of an object is less than 65.2% as an average in motion due to more noise interferences in depth consistency. So, more optimization is needed in the algorithm of depth estimation. Processing time of a target tracking system is high (10.670 s), that must be reduced to provide increased performance in motion tracking of an object in real-time. XR is a key innovation that is going to work with a change in perspective in the manner in which clients collaborate with information and has just barely as of late been perceived as a feasible answer for tackling numerous basic requirements.