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.
Recommended Citation
W. Cai et al., "Auto-Detection of Anisakid Larvae in Cod Fillets by Uv Fluorescent Imaging with Os-Elm," IEEE Region 10 Annual International Conference, Proceedings/TENCON, article no. 7373102, Institute of Electrical and Electronics Engineers, Jan 2016.
The definitive version is available at https://doi.org/10.1109/TENCON.2015.7373102
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Anisakid Larvae; Auto-detection; Online Sequential Extreme Learning Machine; Single Hidden Layer Feedforward Neural Networks; UV Fluorescent Imaging
International Standard Book Number (ISBN)
978-147998641-5
International Standard Serial Number (ISSN)
2159-3450; 2159-3442
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
05 Jan 2016
