A Proposed Registration Method Using Tracking Interest Features for Augmented Reality
An Augmented Reality (AR) incorporates a mix of genuine and PC created scene segments. AR frameworks enhance a client's impression of this present reality with information that is not entirely of the scene. A key test for making an expanded the truth is to keep up precise arrangement amongst ge...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Unviversity of Technology- Iraq
2016-06-01
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| Series: | Engineering and Technology Journal |
| Online Access: | https://etj.uotechnology.edu.iq/article_126836_00af1e6c956835edfd113ea57fcf0d04.pdf |
| Summary: | An Augmented Reality (AR) incorporates a mix of genuine and PC created scene segments. AR frameworks enhance a client's impression of this present reality with information that is not entirely of the scene. A key test for making an expanded the truth is to keep up precise arrangement amongst genuine and virtual thing.This exploration delineates a method to build up the enlistment extent of a dream based enlarged reality (AR) framework, also explores a simple method for detecting and tracking natural features in video stream. In this method, a reference image has been used as a tool to find the a proper position of an object. This method first uses Harris Corner Detector to detect the interest features and find the correspondences using cross-correlation method then it used the Random Sample Consensus (RANSAC) algorithm to find the Homography matrix .After acquiring keypoints in the video frame, a Kanade–Lucas–Tomasi (KLT) feature tracker optical flow tracking algorithm has been used to track the motion of these keypoints frame-by-frame. By maintaining the correspondence between the tracked keypoints and those on the clean marker image, a new homography for every frame has been computed. This allows tracking the orientation of the marker as it moves in the video frames.Experiments for assessing the possibility of the technique are implemented in order to illustrate the potential benefits of the method, in which result's that to the target registration error ( TRE) reach 0.0020 , root mean square error (RMSE ) is 0.003 and average time for whole dataset is 2.5 s |
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| ISSN: | 1681-6900 2412-0758 |