Plankton Recognition in Images with Varying Size
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Plankton Recognition in Images with Varying Size
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Lecture Notes in Computer Science
ISSN
0302-9743
e-ISSN
—
Volume of the periodical
12666
Issue of the periodical within the volume
2
Country of publishing house
DE - GERMANY
Number of pages
11
Pages from-to
110-120
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-85103240017