Classification of Fish Species Using Silhouettes

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12520%2F20%3A43900938" target="_blank" >RIV/60076658:12520/20:43900938 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-45385-5_28" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-45385-5_28</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-45385-5_28" target="_blank" >10.1007/978-3-030-45385-5_28</a>

Result language
angličtina
Original language name
Classification of Fish Species Using Silhouettes
Original language description
The classification of the fish silhouettes allows a quick decision of the fish species presence and amount in the given scene. The classical approach of the machine learning is used to test the question of linear separability of fish species silhouettes classes. The preprocessing of images consisted of object to background segmentation and image registration. The classificator is trained using modified Rosenblatt algorithm for loss function of discriminant analysis. This article is disseminating the preliminary results of training and testing of six fish species classification. The images were of different quality and light conditions. The classificator with the possibility to undecide is introduced and compared. The results are discussed from the point of view of usability of classical methods, preprocessing conditioning, and parametrization of loss function. © Springer Nature Switzerland AG 2020.
Czech name
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Czech description
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Project
<a href="/en/project/ED2.1.00%2F19.0380" target="_blank" >ED2.1.00/19.0380: The CENAKVA Centre Development</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
978-3-030-45384-8
ISSN
0302-9743
e-ISSN
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Number of pages
10
Pages from-to
310-319
Publisher name
Springer
Place of publication
Grandada, Spain
Event location
Granada, Spain
Event date
May 6, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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