Superpixel segmentations for thin sections: Evaluation of methods to enable the generation of machine learning training data sets-Reference-Cited by-同舟云学术

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Superpixel segmentations for thin sections: Evaluation of methods to enable the generation of machine learning training data sets

Published:2023-01 Issue: Volume:170 Page:105232
ISSN:0098-3004
Container-title:Computers & Geosciences
language:en
Short-container-title:Computers & Geosciences

Author:

Yu Jiaxin,Wellmann Florian^ORCID,Virgo Simon,von Domarus Marven^ORCID,Jiang Mingze,Schmatz Joyce,Leibe Bastian

Funder

EIT RawMaterials

European Institute of Innovation and Technology

RWTH Aachen University

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Information Systems

Reference76 articles.

1. SLIC Superpixels;Achanta,2010

2. SLIC superpixels compared to state-of-the-art superpixel methods;Achanta;IEEE Trans. Pattern Anal. Mach. Intell.,2012

3. Seeds: Superpixels extracted via energy-driven sampling;Van den Bergh,2012

4. Pattern Recognition and Machine Learning;Bishop,2006

5. Bokeh: Python library for interactive visualization;Bokeh Development Team,2018

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