Comparative Analysis of Feature and Intensity Based Image Registration Algorithms in Variable Agricultural Scenarios

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F22%3A10456571" target="_blank" >RIV/00216208:11310/22:10456571 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-12413-6_12" target="_blank" >https://doi.org/10.1007/978-3-031-12413-6_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-12413-6_12" target="_blank" >10.1007/978-3-031-12413-6_12</a>

Result language

angličtina

Original language name

Comparative Analysis of Feature and Intensity Based Image Registration Algorithms in Variable Agricultural Scenarios

Original language description

Image registration has widespread application in fields like medical imaging, satellite imagery and agriculture precision as it is essential for feature detection and extraction. The extent of this paper is focussed on analysis of intensity and feature-based registration algorithms over Blue and RedEdge multispectral images of wheat and cauliflower field under different altitudinal conditions i.e., drone imaging at 3 m for cauliflower and handheld imaging at 1 m for wheat crops. The overall comparison among feature and intensity-based algorithms is based on registration quality and time taken for feature matching. Intra-class comparison of feature-based registration is parameterized on type of transformation, number of features being detected, number of features matched, quality and feature matching time. Intra-class comparison of intensity-based registration algorithms is based on type of transformation, nature of alignment, quality and feature matching time. This study has considered SURF, MSER, KAZE, ORB for feature-based registration and Phase Correlation, Monomodal intensity and Multimodal intensity for intensity-based registration. Quantitatively, feature-based techniques were found superior to intensity-based techniques in terms of quality and computational time, where ORB and MSER scored highest. Among intensity-based methods, Monomodal intensity performed best in terms of registration quality. However, Phase Correlation marginally scored less in quality but fared well in terms of computational time.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10508 - Physical geography

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Third International Conference on Image Processing and Capsule Networks (ICIPCN 2022)

ISBN

978-3-031-12412-9

ISSN

2367-3370

e-ISSN

2367-3389

Number of pages

18

Pages from-to

143-160

Publisher name

Springer International Publishing AG

Place of publication

CHAM

Event location

ELECTR NETWORK, Bangkok

Event date

May 20, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000892628600012