Auto-detection of Anisakid larvae in Cod Fillets by UV fluorescent imaging with OS-ELM

Wenqiang Cai; Limin Cao; Hong Lin; Jianxin Sui; Rui Nian; Jidong Hu; Amaury Lendasse

doi:10.1109/TENCON.2015.7373102

Auto-detection of Anisakid larvae in Cod Fillets by UV fluorescent imaging with OS-ELM

Wenqiang Cai, Limin Cao, Hong Lin, Jianxin Sui, Rui Nian, Jidong Hu, Amaury Lendasse

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

In this paper, one auto-detection scheme of Anisakid larvae in cod fillets is developed on the basis of online sequential extreme learning machine (OS-ELM) in a single hidden layer feedforward neural networks (SLFN). One UV fluorescent imaging system is first set up to collect and extract the typical image patches with and without Anisakid larvae inside the fish muscles, the UV fluorescent image patches are then fed into SLFN sequentially to learn how to nondestructively identify the parasites in real-time, particularly for a growing size of the training set with new observations arrived again and again. It has been shown in the simulation experiments that the developed nondestructive approach could get online auto-detection performance in both good accuracy and efficiency during the test, even for those Anisakid larvae deeply embedded in the cod fillets.

Original language	English
Title of host publication	TENCON 2015 - 2015 IEEE Region 10 Conference
Publisher	IEEE
Number of pages	5
Volume	2016-January
ISBN (Electronic)	978-1-4799-8641-5
ISBN (Print)	978-1-4799-8639-2
DOIs	https://doi.org/10.1109/TENCON.2015.7373102
Publication status	Published - 5 Jan 2016
MoE publication type	A4 Article in a conference publication
Event	IEEE Region 10 Conference - Macau, Macao Duration: 1 Nov 2015 → 4 Nov 2015 Conference number: 35 http://www.ieeetencon2015.org/

Publication series

Name
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	IEEE Region 10 Conference
Abbreviated title	TENCON
Country	Macao
City	Macau
Period	01/11/2015 → 04/11/2015
Internet address	http://www.ieeetencon2015.org/

Keywords

Anisakid Larvae
Auto-detection
Online Sequential Extreme Learning Machine
Single Hidden Layer Feedforward Neural Networks
UV Fluorescent Imaging

Access to Document

10.1109/TENCON.2015.7373102

Link to publication in Scopus

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Cite this

@inproceedings{4a855938045f45b3b171642beb0b04e7,

title = "Auto-detection of Anisakid larvae in Cod Fillets by UV fluorescent imaging with OS-ELM",

abstract = "In this paper, one auto-detection scheme of Anisakid larvae in cod fillets is developed on the basis of online sequential extreme learning machine (OS-ELM) in a single hidden layer feedforward neural networks (SLFN). One UV fluorescent imaging system is first set up to collect and extract the typical image patches with and without Anisakid larvae inside the fish muscles, the UV fluorescent image patches are then fed into SLFN sequentially to learn how to nondestructively identify the parasites in real-time, particularly for a growing size of the training set with new observations arrived again and again. It has been shown in the simulation experiments that the developed nondestructive approach could get online auto-detection performance in both good accuracy and efficiency during the test, even for those Anisakid larvae deeply embedded in the cod fillets.",

keywords = "Anisakid Larvae, Auto-detection, Online Sequential Extreme Learning Machine, Single Hidden Layer Feedforward Neural Networks, UV Fluorescent Imaging",

author = "Wenqiang Cai and Limin Cao and Hong Lin and Jianxin Sui and Rui Nian and Jidong Hu and Amaury Lendasse",

year = "2016",

month = jan,

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doi = "10.1109/TENCON.2015.7373102",

language = "English",

isbn = "978-1-4799-8639-2 ",

volume = "2016-January",

publisher = "IEEE",

booktitle = "TENCON 2015 - 2015 IEEE Region 10 Conference",

address = "United States",

note = "IEEE Region 10 Conference, TENCON ; Conference date: 01-11-2015 Through 04-11-2015",

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Auto-detection of Anisakid larvae in Cod Fillets by UV fluorescent imaging with OS-ELM. / Cai, Wenqiang; Cao, Limin; Lin, Hong; Sui, Jianxin; Nian, Rui; Hu, Jidong; Lendasse, Amaury.

TENCON 2015 - 2015 IEEE Region 10 Conference. Vol. 2016-January IEEE, 2016. 7373102.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Auto-detection of Anisakid larvae in Cod Fillets by UV fluorescent imaging with OS-ELM

AU - Cai, Wenqiang

AU - Cao, Limin

AU - Lin, Hong

AU - Sui, Jianxin

AU - Nian, Rui

AU - Hu, Jidong

AU - Lendasse, Amaury

N1 - Conference code: 35

PY - 2016/1/5

Y1 - 2016/1/5

N2 - In this paper, one auto-detection scheme of Anisakid larvae in cod fillets is developed on the basis of online sequential extreme learning machine (OS-ELM) in a single hidden layer feedforward neural networks (SLFN). One UV fluorescent imaging system is first set up to collect and extract the typical image patches with and without Anisakid larvae inside the fish muscles, the UV fluorescent image patches are then fed into SLFN sequentially to learn how to nondestructively identify the parasites in real-time, particularly for a growing size of the training set with new observations arrived again and again. It has been shown in the simulation experiments that the developed nondestructive approach could get online auto-detection performance in both good accuracy and efficiency during the test, even for those Anisakid larvae deeply embedded in the cod fillets.

AB - In this paper, one auto-detection scheme of Anisakid larvae in cod fillets is developed on the basis of online sequential extreme learning machine (OS-ELM) in a single hidden layer feedforward neural networks (SLFN). One UV fluorescent imaging system is first set up to collect and extract the typical image patches with and without Anisakid larvae inside the fish muscles, the UV fluorescent image patches are then fed into SLFN sequentially to learn how to nondestructively identify the parasites in real-time, particularly for a growing size of the training set with new observations arrived again and again. It has been shown in the simulation experiments that the developed nondestructive approach could get online auto-detection performance in both good accuracy and efficiency during the test, even for those Anisakid larvae deeply embedded in the cod fillets.

KW - Anisakid Larvae

KW - Auto-detection

KW - Online Sequential Extreme Learning Machine

KW - Single Hidden Layer Feedforward Neural Networks

KW - UV Fluorescent Imaging

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