Classification of Fish Species Using Silhouettes

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Fish Species Using Silhouettes

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Classification of Fish Species Using Silhouettes

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/ED2.1.00%2F19.0380" target="_blank" >ED2.1.00/19.0380: Rozvoj centra CENAKVA</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

Počet stran výsledku

10

Strana od-do

310-319

Název nakladatele

Springer

Místo vydání

Grandada, Spain

Místo konání akce

Granada, Spain

Datum konání akce

6. 5. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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