Macroalgae recognition based on histogram oriented gradient

The distribution of the marine algae is an important indication of the biodiversity changes in the aquatic ecosystem which algae biologist normally monitors. One of the most prominent instances is the monitoring of the invasive alga, e.g. Caulerpa taxifolia, through conducting regular surveys. It us...

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Main Authors: Tan, C.S., Lau, P.Y., Low, T.J.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20311 /
Published: Springer Verlag 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992727042&doi=10.1007%2f978-981-10-1721-6_28&partnerID=40&md5=2cebb4266f0ddea13b4504923ee87c18
http://eprints.utp.edu.my/20311/
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spelling utp-eprints.203112018-04-23T01:04:04Z Macroalgae recognition based on histogram oriented gradient Tan, C.S. Lau, P.Y. Low, T.J. The distribution of the marine algae is an important indication of the biodiversity changes in the aquatic ecosystem which algae biologist normally monitors. One of the most prominent instances is the monitoring of the invasive alga, e.g. Caulerpa taxifolia, through conducting regular surveys. It usually involves highly trained algae biologist to annotate the obtained video in order to detect the location where the alga would be likely present within survey area. This may constitute to a lengthy and demanding task which could be prone to observer-induced error. Hence, a framework is proposed herein to automate the analysis of underwater image to deduce if it contains the targeted alga, which is Caulerpa taxifolia. The framework employed HOG feature descriptor for object detection. Its efficiency and reliable was verified by the experiments using our consolidated database. © Springer Science+Business Media Singapore 2017. Springer Verlag 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992727042&doi=10.1007%2f978-981-10-1721-6_28&partnerID=40&md5=2cebb4266f0ddea13b4504923ee87c18 Tan, C.S. and Lau, P.Y. and Low, T.J. (2017) Macroalgae recognition based on histogram oriented gradient. Lecture Notes in Electrical Engineering, 398 . pp. 257-266. http://eprints.utp.edu.my/20311/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description The distribution of the marine algae is an important indication of the biodiversity changes in the aquatic ecosystem which algae biologist normally monitors. One of the most prominent instances is the monitoring of the invasive alga, e.g. Caulerpa taxifolia, through conducting regular surveys. It usually involves highly trained algae biologist to annotate the obtained video in order to detect the location where the alga would be likely present within survey area. This may constitute to a lengthy and demanding task which could be prone to observer-induced error. Hence, a framework is proposed herein to automate the analysis of underwater image to deduce if it contains the targeted alga, which is Caulerpa taxifolia. The framework employed HOG feature descriptor for object detection. Its efficiency and reliable was verified by the experiments using our consolidated database. © Springer Science+Business Media Singapore 2017.
format Article
author Tan, C.S.
Lau, P.Y.
Low, T.J.
spellingShingle Tan, C.S.
Lau, P.Y.
Low, T.J.
Macroalgae recognition based on histogram oriented gradient
author_sort Tan, C.S.
title Macroalgae recognition based on histogram oriented gradient
title_short Macroalgae recognition based on histogram oriented gradient
title_full Macroalgae recognition based on histogram oriented gradient
title_fullStr Macroalgae recognition based on histogram oriented gradient
title_full_unstemmed Macroalgae recognition based on histogram oriented gradient
title_sort macroalgae recognition based on histogram oriented gradient
publisher Springer Verlag
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992727042&doi=10.1007%2f978-981-10-1721-6_28&partnerID=40&md5=2cebb4266f0ddea13b4504923ee87c18
http://eprints.utp.edu.my/20311/
_version_ 1741196351408963584
score 11.62408

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