Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloHow holographic imaging can improve machine learning
Anno di pubblicazione2019
Formato-
Autore/iMemmolo, Pasquale; Bianco, Vittorio; Carcagnì, Pierluigi; Merola, Francesco; Paturzo, Melania; Distante, Cosimo; Ferraro, Pietro
Affiliazioni autoriNational Reseach Council of Italy (CNR), Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello"; Universita del Salento
Autori CNR e affiliazioni
  • COSIMO DISTANTE
  • PIETRO FERRARO
  • PIERLUIGI CARCAGNI'
  • MELANIA PATURZO
  • FRANCESCO MEROLA
  • PASQUALE MEMMOLO
  • VITTORIO BIANCO
Lingua/e
  • inglese
AbstractNowadays, digital holography can be considered as one of the most powerful imaging modality in several research fields, from the 3D imaging for display purposes to quantitative phase image in microscopy and microfluidics. At the same time, Machine learning in imaging applications has been literally reborn to the point of being considered the most exploited field by optical imaging researchers. In fact, the use of deep convolutional neural networks has permitted to achieve impressive results in the classification of biological samples obtained by holographic imaging, as well as for solving inverse problems in holographic microscopy. Definitely, Machine learning approaches in digital holography has been used mainly to improve the performance of the imaging tool. Here we show a reverse modality in which holographic imaging boosts the performance of Machine leaning algorithms. In particular, we identify several descriptors solely related to the type of data to be classified, i.e. the holographic image. We provide some case studies which demonstrate how the holographic imaging can improve the performance of a plain classifier.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali-
RivistaProceedings of SPIE, the International Society for Optical Engineering
Attiva dal 1981
Editore: SPIE, The International Society for Optical Engineering - Bellingham, WA
Lingua: inglese
ISSN: 0277-786X
Titolo chiave: Proceedings of SPIE, the International Society for Optical Engineering
Titolo proprio: Proceedings of SPIE, the International Society for Optical Engineering.
Titolo abbreviato: Proc. SPIE Int. Soc. Opt. Eng.
Titoli alternativi:
  • Proceedings of SPIE
  • SPIE proceedings series
  • Proceedings of Society of Photo-optical Instrumentation Engineers
Numero volume della rivista11059
Fascicolo della rivista-
DOI10.1117/12.2527480
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85072649380)
Parole chiaveDigital holography, Machine learning, Microscopy
Link (URL, URI)http://www.scopus.com/record/display.url?eid=2-s2.0-85072649380&origin=inward
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ISASI — Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati