English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/211696
Share/Impact:
Statistics
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Deep Learning Versus Classic Methods for Multi-taxon Diatom Segmentation

AuthorsRuiz-Santaquitaria, Jesús; Pedraza, Anibal; Sánchez-Bueno, Carlos ; Libreros, José A.; Salido, Jesús; Deniz, Oscar; Blanco, Saul; Cristóbal, Gabriel ; Bueno, Gloria
Issue Date1-Jul-2019
PublisherSpringer Nature
Citation9th Iberian Conference on Pattern Recognition and Image Analysis (2019)
AbstractDiatom identification is a crucial process to estimate water quality, which is essential in biological studies. This process can be auto- mated with machine learning algorithms. For this purpose, a dataset with 10 common taxa is collected, with annotations provided by an expert diatomist. In this work, a comparison of the classical state-of-the-art gen- eral purpose methods along with two different deep learning approaches is carried out. The classical methods are based on Viola-Jones and scale and curvature invariant ridge object detectors. The deep learning based methods are Semantic Segmentation and YOLO. This is the first time that Viola-Jones and Semantic Segmentation techniques are applied and compared for diatom segmentation in microscopic images containing sev- eral taxon shells. While all methods provide relatively good results in specific species, the deep learning approaches are consistently better in terms of sensitivity and specificity (up to 0.99 for some taxa) and up to 0.86 precision.
DescriptionIbPRIA 2019,Madrid, Spain. July 1-4 2019. -- http://www.ibpria.org/2019/. -- Part of the Lecture Notes in Computer Science book series (LNCS, volume 11867) Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11867)
Publisher version (URL)http://dx.doi.org/10.1007/978-3-030-31332-6_30
URIhttp://hdl.handle.net/10261/211696
Identifiersdoi: 10.1007/978-3-030-31332-6_30
Appears in Collections:(CFMAC-IO) Comunicaciones congresos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,38 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.