Visual detection in omnidirectional view sensors

Nguan Soon Chong, Yau Hee Kho, Mou Ling Dennis Wong

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In recent years, the use of omnidirectional view (OV) sensors has gained popularity in robotics. The main reason behind this growth is due to the large field of view (FOV) that spans offered by these sensors under a catadioptric configuration. The large FOV addresses several shortcomings of a conventional perspective imaging sensor by allowing simultaneous monitoring of surrounding environment under a single image compilation. Feature detection is one of the fundamental components in visual robotics applications that enable intelligent vision system with advanced features such as object, scene, and human detection, localisation, simultaneous localisation and mapping, and odometry. In this paper, the adaptation of visual detection algorithm in omnidirectional vision is reviewed by investigating the recent works and the underlying supporting mechanism. Furthermore, state-of-the-art vision detection algorithms and important factors of OV sensors, such as hardware requirements, fundamental theories, cost, and usability, are also investigated in order to explain the adaptation involved. To conclude this work, a case study related to OV mapping transform is presented, and insights on possible future research direction are provided.

Original languageEnglish
Pages (from-to)923-940
Number of pages18
JournalSignal, Image and Video Processing
Volume9
Issue number4
DOIs
Publication statusPublished - 2015
Externally publishedYes

Fingerprint

Sensors
Robotics
Hardware
Imaging techniques
Monitoring
Costs

Keywords

  • Feature detection
  • Machine Vision
  • Omnidirectional view sensor
  • View unwrapping

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

Cite this

Visual detection in omnidirectional view sensors. / Chong, Nguan Soon; Kho, Yau Hee; Wong, Mou Ling Dennis.

In: Signal, Image and Video Processing, Vol. 9, No. 4, 2015, p. 923-940.

Research output: Contribution to journalArticle

Chong, Nguan Soon ; Kho, Yau Hee ; Wong, Mou Ling Dennis. / Visual detection in omnidirectional view sensors. In: Signal, Image and Video Processing. 2015 ; Vol. 9, No. 4. pp. 923-940.
@article{a37d5636c8d1442ebb1b402e054cfe1a,
title = "Visual detection in omnidirectional view sensors",
abstract = "In recent years, the use of omnidirectional view (OV) sensors has gained popularity in robotics. The main reason behind this growth is due to the large field of view (FOV) that spans offered by these sensors under a catadioptric configuration. The large FOV addresses several shortcomings of a conventional perspective imaging sensor by allowing simultaneous monitoring of surrounding environment under a single image compilation. Feature detection is one of the fundamental components in visual robotics applications that enable intelligent vision system with advanced features such as object, scene, and human detection, localisation, simultaneous localisation and mapping, and odometry. In this paper, the adaptation of visual detection algorithm in omnidirectional vision is reviewed by investigating the recent works and the underlying supporting mechanism. Furthermore, state-of-the-art vision detection algorithms and important factors of OV sensors, such as hardware requirements, fundamental theories, cost, and usability, are also investigated in order to explain the adaptation involved. To conclude this work, a case study related to OV mapping transform is presented, and insights on possible future research direction are provided.",
keywords = "Feature detection, Machine Vision, Omnidirectional view sensor, View unwrapping",
author = "Chong, {Nguan Soon} and Kho, {Yau Hee} and Wong, {Mou Ling Dennis}",
year = "2015",
doi = "10.1007/s11760-013-0528-0",
language = "English",
volume = "9",
pages = "923--940",
journal = "Signal, Image and Video Processing",
issn = "1863-1703",
publisher = "Springer London",
number = "4",

}

TY - JOUR

T1 - Visual detection in omnidirectional view sensors

AU - Chong, Nguan Soon

AU - Kho, Yau Hee

AU - Wong, Mou Ling Dennis

PY - 2015

Y1 - 2015

N2 - In recent years, the use of omnidirectional view (OV) sensors has gained popularity in robotics. The main reason behind this growth is due to the large field of view (FOV) that spans offered by these sensors under a catadioptric configuration. The large FOV addresses several shortcomings of a conventional perspective imaging sensor by allowing simultaneous monitoring of surrounding environment under a single image compilation. Feature detection is one of the fundamental components in visual robotics applications that enable intelligent vision system with advanced features such as object, scene, and human detection, localisation, simultaneous localisation and mapping, and odometry. In this paper, the adaptation of visual detection algorithm in omnidirectional vision is reviewed by investigating the recent works and the underlying supporting mechanism. Furthermore, state-of-the-art vision detection algorithms and important factors of OV sensors, such as hardware requirements, fundamental theories, cost, and usability, are also investigated in order to explain the adaptation involved. To conclude this work, a case study related to OV mapping transform is presented, and insights on possible future research direction are provided.

AB - In recent years, the use of omnidirectional view (OV) sensors has gained popularity in robotics. The main reason behind this growth is due to the large field of view (FOV) that spans offered by these sensors under a catadioptric configuration. The large FOV addresses several shortcomings of a conventional perspective imaging sensor by allowing simultaneous monitoring of surrounding environment under a single image compilation. Feature detection is one of the fundamental components in visual robotics applications that enable intelligent vision system with advanced features such as object, scene, and human detection, localisation, simultaneous localisation and mapping, and odometry. In this paper, the adaptation of visual detection algorithm in omnidirectional vision is reviewed by investigating the recent works and the underlying supporting mechanism. Furthermore, state-of-the-art vision detection algorithms and important factors of OV sensors, such as hardware requirements, fundamental theories, cost, and usability, are also investigated in order to explain the adaptation involved. To conclude this work, a case study related to OV mapping transform is presented, and insights on possible future research direction are provided.

KW - Feature detection

KW - Machine Vision

KW - Omnidirectional view sensor

KW - View unwrapping

UR - http://www.scopus.com/inward/record.url?scp=84925291760&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84925291760&partnerID=8YFLogxK

U2 - 10.1007/s11760-013-0528-0

DO - 10.1007/s11760-013-0528-0

M3 - Article

VL - 9

SP - 923

EP - 940

JO - Signal, Image and Video Processing

JF - Signal, Image and Video Processing

SN - 1863-1703

IS - 4

ER -