eprintid: 12064
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/20/64
datestamp: 2023-11-10 03:26:36
lastmod: 2023-11-10 03:26:36
status_changed: 2023-11-10 01:16:47
type: article
metadata_visibility: show
creators_name: Lee, A.W.C.
creators_name: Yong, S.-P.
creators_name: Watada, J.
title: Global thresholding for scene understanding towards autonomous drone navigation
ispublished: pub
keywords: Aircraft detection; Antennas; Navigation; Object detection; Object recognition, Autonomous navigation; Gaussian filters; Global thresholding; Processing time; Regions of interest; Scene understanding; Thresholding; Thresholding algorithms, Drones
note: cited By 1
abstract: Unmanned aerial vehicles, more typically known as drones are flying aircrafts that do not have a pilot on-board. For drones to fly through an area without GPS signals, developing scene understanding algorithms to assist in autonomous navigation will be useful. In this paper, various thresholding algorithms are evaluated to enhance scene understanding in addition to object detection. Based on the results obtained, Gaussian filter global thresholding can segment regions of interest in the scene effectively and provide the least cost of processing time. © 2019 Fuji Technology Press. All rights reserved.
date: 2019
publisher: Fuji Technology Press
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072306354&doi=10.20965%2fjaciii.2019.p0909&partnerID=40&md5=b8cbf6faabe1a96c947df9742e4d0e5e
id_number: 10.20965/jaciii.2019.p0909
full_text_status: none
publication: Journal of Advanced Computational Intelligence and Intelligent Informatics
volume: 23
number: 5
pagerange: 909-919
refereed: TRUE
issn: 13430130
citation:   Lee, A.W.C. and Yong, S.-P. and Watada, J.  (2019) Global thresholding for scene understanding towards autonomous drone navigation.  Journal of Advanced Computational Intelligence and Intelligent Informatics, 23 (5).  pp. 909-919.  ISSN 13430130