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A Survey on Underwater Fish Species Detection and Classification

R. Fathima Syreen1 , K. Merriliance2

Section:Survey Paper, Product Type: Journal Paper
Volume-07 , Issue-08 , Page no. 95-98, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si8.9598

Online published on Apr 10, 2019

Copyright © R. Fathima Syreen, K. Merriliance . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: R. Fathima Syreen, K. Merriliance, “A Survey on Underwater Fish Species Detection and Classification,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.95-98, 2019.

MLA Style Citation: R. Fathima Syreen, K. Merriliance "A Survey on Underwater Fish Species Detection and Classification." International Journal of Computer Sciences and Engineering 07.08 (2019): 95-98.

APA Style Citation: R. Fathima Syreen, K. Merriliance, (2019). A Survey on Underwater Fish Species Detection and Classification. International Journal of Computer Sciences and Engineering, 07(08), 95-98.

BibTex Style Citation:
@article{Syreen_2019,
author = {R. Fathima Syreen, K. Merriliance},
title = {A Survey on Underwater Fish Species Detection and Classification},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {07},
Issue = {08},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {95-98},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=925},
doi = {https://doi.org/10.26438/ijcse/v7i8.9598}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i8.9598}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=925
TI - A Survey on Underwater Fish Species Detection and Classification
T2 - International Journal of Computer Sciences and Engineering
AU - R. Fathima Syreen, K. Merriliance
PY - 2019
DA - 2019/04/10
PB - IJCSE, Indore, INDIA
SP - 95-98
IS - 08
VL - 07
SN - 2347-2693
ER -

           

Abstract

Fish species recognition is a challenging task for research. Great challenges for fish recognition appear in the special properties of underwater videos and images. Due to the great demand for underwater object recognition, many machine learning and image processing algorithms have been proposed. Deep Learning has achieved a significant results and a huge improvement in visual detection and recognition. This paper mainly reviews some techniques proposed in past years for automatic fish species detection and classification.

Key-Words / Index Term

Fish Recognition;Fish Classification;Feature ExtractionImage Processing;Neural Network;Deep Learning

References

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