TY  - JOUR
EP  - 919
SN  - 13430130
PB  - Fuji Technology Press
N1  - cited By 1
SP  - 909
TI  - Global thresholding for scene understanding towards autonomous drone navigation
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072306354&doi=10.20965%2fjaciii.2019.p0909&partnerID=40&md5=b8cbf6faabe1a96c947df9742e4d0e5e
A1  - Lee, A.W.C.
A1  - Yong, S.-P.
A1  - Watada, J.
JF  - Journal of Advanced Computational Intelligence and Intelligent Informatics
VL  - 23
Y1  - 2019///
N2  - 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.
IS  - 5
KW  - Aircraft detection; Antennas; Navigation; Object detection; Object recognition
KW  -  Autonomous navigation; Gaussian filters; Global thresholding; Processing time; Regions of interest; Scene understanding; Thresholding; Thresholding algorithms
KW  -  Drones
ID  - scholars12064
ER  -