Plankton Recognition in Images with Varying Size

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU150368" target="_blank" >RIV/00216305:26230/21:PU150368 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-68780-9_11" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-68780-9_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-68780-9_11" target="_blank" >10.1007/978-3-030-68780-9_11</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Plankton Recognition in Images with Varying Size

Popis výsledku v původním jazyce

Monitoring plankton is important as they are an essential part of the aquatic food web as well as producers of oxygen. Modern imaging devices produce a massive amount of plankton image data which calls for automatic solutions. These images are characterized by a very large variation in both the size and the aspect ratio. Convolutional neural network (CNN) based classification methods, on the other hand, typically require a fixed size input. Simple scaling of the images into a common size contains several drawbacks. First, the information about the size of the plankton is lost. For human experts, the size information is one of the most important cues for identifying the species. Second, downscaling the images leads to the loss of fine details such as flagella essential for species recognition. Third, upscaling the images increases the size of the network. In this work, extensive experiments on various approaches to address the varying image dimensions are carried out on a challenging phytoplankton image dataset. A novel combination of methods is proposed, showing improvement over the baseline CNN.

Název v anglickém jazyce

Plankton Recognition in Images with Varying Size

Popis výsledku anglicky

Monitoring plankton is important as they are an essential part of the aquatic food web as well as producers of oxygen. Modern imaging devices produce a massive amount of plankton image data which calls for automatic solutions. These images are characterized by a very large variation in both the size and the aspect ratio. Convolutional neural network (CNN) based classification methods, on the other hand, typically require a fixed size input. Simple scaling of the images into a common size contains several drawbacks. First, the information about the size of the plankton is lost. For human experts, the size information is one of the most important cues for identifying the species. Second, downscaling the images leads to the loss of fine details such as flagella essential for species recognition. Third, upscaling the images increases the size of the network. In this work, extensive experiments on various approaches to address the varying image dimensions are carried out on a challenging phytoplankton image dataset. A novel combination of methods is proposed, showing improvement over the baseline CNN.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Lecture Notes in Computer Science

ISSN

0302-9743

e-ISSN

—

Svazek periodika

12666

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

11

Strana od-do

110-120

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85103240017