Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048842371> ?p ?o ?g. }
Showing items 1 to 97 of
97
with 100 items per page.
- W3048842371 endingPage "015406" @default.
- W3048842371 startingPage "015406" @default.
- W3048842371 abstract "Abstract Conventional image processing for a particle shadow image is usually time-consuming and suffers degraded image segmentation when dealing with images consisting of complex-shaped and clustered particles with varying backgrounds. In this paper, we introduce a robust learning-based method using a single convolution neural network for analyzing particle shadow images. Our approach employs a two-channel-output U-net model to generate a binary particle image and a particle centroid image. The binary particle image is subsequently segmented through a marker-controlled watershed approach with the particle centroid image as the marker image. The assessment of this method on both synthetic and experimental bubble images has exhibited a better performance compared to the state-of-art non-machine-learning method. The proposed machine learning shadow image processing approach provides a promising tool for real-time particle image analysis in industrial applications." @default.
- W3048842371 created "2020-08-18" @default.
- W3048842371 creator A5005244980 @default.
- W3048842371 creator A5063334544 @default.
- W3048842371 creator A5087376315 @default.
- W3048842371 date "2020-10-28" @default.
- W3048842371 modified "2023-10-12" @default.
- W3048842371 title "Machine learning shadowgraph for particle size and shape characterization" @default.
- W3048842371 cites W1901129140 @default.
- W3048842371 cites W1965713543 @default.
- W3048842371 cites W1979688424 @default.
- W3048842371 cites W2005594652 @default.
- W3048842371 cites W2013985817 @default.
- W3048842371 cites W2080953448 @default.
- W3048842371 cites W2087257880 @default.
- W3048842371 cites W2088331383 @default.
- W3048842371 cites W2091334856 @default.
- W3048842371 cites W2101049293 @default.
- W3048842371 cites W2106713850 @default.
- W3048842371 cites W2127160357 @default.
- W3048842371 cites W2253115641 @default.
- W3048842371 cites W2323352002 @default.
- W3048842371 cites W2533800772 @default.
- W3048842371 cites W2770150521 @default.
- W3048842371 cites W2952471288 @default.
- W3048842371 cites W2963730812 @default.
- W3048842371 cites W2984851161 @default.
- W3048842371 cites W2998464080 @default.
- W3048842371 cites W3000484198 @default.
- W3048842371 cites W33239177 @default.
- W3048842371 doi "https://doi.org/10.1088/1361-6501/abae90" @default.
- W3048842371 hasPublicationYear "2020" @default.
- W3048842371 type Work @default.
- W3048842371 sameAs 3048842371 @default.
- W3048842371 citedByCount "22" @default.
- W3048842371 countsByYear W30488423712021 @default.
- W3048842371 countsByYear W30488423712022 @default.
- W3048842371 countsByYear W30488423712023 @default.
- W3048842371 crossrefType "journal-article" @default.
- W3048842371 hasAuthorship W3048842371A5005244980 @default.
- W3048842371 hasAuthorship W3048842371A5063334544 @default.
- W3048842371 hasAuthorship W3048842371A5087376315 @default.
- W3048842371 hasBestOaLocation W30488423712 @default.
- W3048842371 hasConcept C104317675 @default.
- W3048842371 hasConcept C111368507 @default.
- W3048842371 hasConcept C115961682 @default.
- W3048842371 hasConcept C117797892 @default.
- W3048842371 hasConcept C127313418 @default.
- W3048842371 hasConcept C146599234 @default.
- W3048842371 hasConcept C153180895 @default.
- W3048842371 hasConcept C154945302 @default.
- W3048842371 hasConcept C15744967 @default.
- W3048842371 hasConcept C193828747 @default.
- W3048842371 hasConcept C2778517922 @default.
- W3048842371 hasConcept C31972630 @default.
- W3048842371 hasConcept C41008148 @default.
- W3048842371 hasConcept C542102704 @default.
- W3048842371 hasConcept C9417928 @default.
- W3048842371 hasConceptScore W3048842371C104317675 @default.
- W3048842371 hasConceptScore W3048842371C111368507 @default.
- W3048842371 hasConceptScore W3048842371C115961682 @default.
- W3048842371 hasConceptScore W3048842371C117797892 @default.
- W3048842371 hasConceptScore W3048842371C127313418 @default.
- W3048842371 hasConceptScore W3048842371C146599234 @default.
- W3048842371 hasConceptScore W3048842371C153180895 @default.
- W3048842371 hasConceptScore W3048842371C154945302 @default.
- W3048842371 hasConceptScore W3048842371C15744967 @default.
- W3048842371 hasConceptScore W3048842371C193828747 @default.
- W3048842371 hasConceptScore W3048842371C2778517922 @default.
- W3048842371 hasConceptScore W3048842371C31972630 @default.
- W3048842371 hasConceptScore W3048842371C41008148 @default.
- W3048842371 hasConceptScore W3048842371C542102704 @default.
- W3048842371 hasConceptScore W3048842371C9417928 @default.
- W3048842371 hasFunder F4320337345 @default.
- W3048842371 hasIssue "1" @default.
- W3048842371 hasLocation W30488423711 @default.
- W3048842371 hasLocation W30488423712 @default.
- W3048842371 hasLocation W30488423713 @default.
- W3048842371 hasOpenAccess W3048842371 @default.
- W3048842371 hasPrimaryLocation W30488423711 @default.
- W3048842371 hasRelatedWork W1593730969 @default.
- W3048842371 hasRelatedWork W2002351209 @default.
- W3048842371 hasRelatedWork W2081985452 @default.
- W3048842371 hasRelatedWork W2356087891 @default.
- W3048842371 hasRelatedWork W2561491196 @default.
- W3048842371 hasRelatedWork W2574769845 @default.
- W3048842371 hasRelatedWork W2775496002 @default.
- W3048842371 hasRelatedWork W3107295340 @default.
- W3048842371 hasRelatedWork W3186605777 @default.
- W3048842371 hasRelatedWork W4386504616 @default.
- W3048842371 hasVolume "32" @default.
- W3048842371 isParatext "false" @default.
- W3048842371 isRetracted "false" @default.
- W3048842371 magId "3048842371" @default.
- W3048842371 workType "article" @default.