Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM

被引：22

作者：

Hidalgo, Franco ^{[1
]}

Braunl, Thomas ^{[2
]}

机构：

[1] Univ San Ignacio de Loyola, Fac Ingn, Lima 15024, Peru

[2] Univ Western Australia, Dept Elect Elect & Comp Engn, Perth, WA 6009, Australia

来源：

SENSORS | 2020年 / 20卷 / 15期

关键词：

vSLAM; detector; descriptor; underwater video; monocular underwater; underwater robots; SIFT; SURF; ORB; AKAZE; BRISK; LOCALIZATION; DESCRIPTORS; DETECTORS; SIFT; SURF;

D O I：

10.3390/s20154343

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Modern visual SLAM (vSLAM) algorithms take advantage of computer vision developments in image processing and in interest point detectors to create maps and trajectories from camera images. Different feature detectors and extractors have been evaluated for this purpose in air and ground environments, but not extensively for underwater scenarios. In this paper (I) we characterize underwater images where light and suspended particles alter considerably the images captured, (II) evaluate the performance of common interest points detectors and descriptors in a variety of underwater scenes and conditions towards vSLAM in terms of the number of features matched in subsequent video frames, the precision of the descriptors and the processing time. This research justifies the usage of feature detectors in vSLAM for underwater scenarios and present its challenges and limitations.

引用

页码：1 / 16

页数：16

共 50 条

[21] Towards Quantifying the Entropy of Fingervein Patterns across Different Feature Extractors
Krivokuca, Vedrana
Marcel, Sebastien
2018 IEEE 4TH INTERNATIONAL CONFERENCE ON IDENTITY, SECURITY, AND BEHAVIOR ANALYSIS (ISBA), 2018,
[22] Evaluation and analysis of feature point detection methods based on vSLAM systems
Xie, Chenyang
Liu, Qiang
Chen, Baojia
Hao, Zhiqiang
IMAGE AND VISION COMPUTING, 2024, 146
[23] A comparison of regional feature detectors in panoramic images
Huebner, Kai
Westhoff, Daniel
Zhang, Jianwei
2006 IEEE INTERNATIONAL CONFERENCE ON INFORMATION ACQUISITION, VOLS 1 AND 2, CONFERENCE PROCEEDINGS, 2006, : 666 - 671
[24] Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Guerin, Joris
Thiery, Stephane
Nyiri, Eric
Gibaru, Olivier
Boots, Byron
NEUROCOMPUTING, 2021, 423 (423) : 551 - 571
[25] Evaluation of Keypoint Detectors and Descriptors in Arthroscopic Images for Feature-Based Matching Applications
Marmol, Andres
Peynot, Thierry
Eriksson, Anders
Jaiprakash, Anjali
Roberts, Jonathan
Crawford, Ross
IEEE ROBOTICS AND AUTOMATION LETTERS, 2017, 2 (04): : 2135 - 2142
[26] Evaluation of Local Feature Detectors for the Comparison of Thermal and Visual Low Altitude Aerial Images
Lakshmi, K. Divya
Muthaiah, R.
Kannan, K.
Tapas, Anand M.
DEFENCE SCIENCE JOURNAL, 2018, 68 (05) : 473 - 479
[27] Performance Analysis of Feature Detectors and Descriptors in Underwater and Polar Environments
Shah, Vikrant
Nir, Jagatpreet
Kaveti, Pushyami
Singh, Hanumant
OCEANS 2021: SAN DIEGO - PORTO, 2021,
[28] An evaluation of hierarchical articulatory feature detectors
Rajamanohar, M
Fosler-Lussier, E
2005 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 2005, : 59 - 64
[29] Evaluation of Feature Detectors and Descriptors on the Iris
De Zanet, Sandro
Rueegsegger, Michael
Rudolph, Tobias
Wolf, Sebastian
Kowal, Jens
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2013, 54 (15)
[30] Pre-trained CNNs as Visual Feature Extractors: A Broad Evaluation
Holliday, Andrew
Dudek, Gregory
2020 17TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV 2020), 2020, : 78 - 84

← 1 2 3 4 5 →