Details preserved unsupervised depth estimation by fusing traditional stereo knowledge from laparoscopic images (Unbekannt)
Freier Zugriff
- Neue Suche nach: Huoling Luo
- Neue Suche nach: Qingmao Hu
- Neue Suche nach: Fucang Jia
- Neue Suche nach: Huoling Luo
- Neue Suche nach: Qingmao Hu
- Neue Suche nach: Fucang Jia
In:
Healthcare Technology Letters (2019)
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2019
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ISSN:
- Aufsatz (Zeitschrift) / Elektronische Ressource
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Titel:Details preserved unsupervised depth estimation by fusing traditional stereo knowledge from laparoscopic images
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Beteiligte:
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Erschienen in:
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Verlag:
- Neue Suche nach: Wiley
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Erscheinungsdatum:2019
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ISSN:
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DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Unbekannt
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Schlagwörter:stereo image processing , surgery , image reconstruction , unsupervised learning , medical image processing , phantoms , image motion analysis , convolutional neural nets , traditional stereo method , proxy disparity labels , unreliable depth measurements , confidence measure , stereo accuracy , disparity images , rectified stereo images , proxy labels , smooth depth surface , unsupervised depth estimation , traditional stereo knowledge , laparoscopic images , vision-based laparoscope surgical navigation systems , truth depth , unsupervised learning depth estimation approach , dual encoder-decoder convolutional neural network , loss function , principled mask , parallax effects , neighbourhood smoothness term , constrain neighbouring pixels , partial nephrectomy da vinci surgery dataset , heart phantom data , hamlyn centre , Medical technology , R855-855.5
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Datenquelle:
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