P
Peter Bajcsy
Researcher at National Institute of Standards and Technology
Publications - 167
Citations - 2066
Peter Bajcsy is an academic researcher from National Institute of Standards and Technology. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 22, co-authored 159 publications receiving 1812 citations. Previous affiliations of Peter Bajcsy include University of Illinois at Urbana–Champaign & American Dental Association.
Papers
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Journal ArticleDOI
Digging into data using new collaborative infrastructures supporting humanities-based computer science research
TL;DR: It is revealed that digital humanities collaboration requires the creation and deployment of tools for sharing that function to improve collaboration involving large–scale data repository analysis among multiple sites, academic disciplines, and participants through data sharing, software sharing, and knowledge sharing practices.
Posted ContentDOI
Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model.
Mathias Hammer,Maximiliaan Huisman,Alessandro Rigano,Ulrike Boehm,James J. Chambers,Nathalie Gaudreault,Alison J. North,Jaime A. Pimentel,Damir Sudar,Peter Bajcsy,Claire M. Brown,Alexander D. Corbett,Orestis Faklaris,Judith Lacoste,Alex Laude,Glyn Nelson,Roland Nitschke,Farzin Farzam,Carlas Smith,David Grunwald,Caterina Strambio-De-Castillia +20 more
TL;DR: The 4D Nucleome Initiative (4DN) and the BioImaging North America (BINA)-OME (NBO namespace) as mentioned in this paper have proposed a set of metadata specifications for light microscopy data that scale with experimental intent and with the complexity of the instrumentation and analytical requirements.
Journal ArticleDOI
Accurate and interpretable classification of microspectroscopy pixels using artificial neural networks.
TL;DR: The problem of accurate and interpretable labeling of spectral images is addressed by designing a supervised classifier from a tandem of Artificial Neural Network models that identify relevant features in raw spectra and achieve high classification accuracy.
Proceedings ArticleDOI
3D medical volume reconstruction using Web services
TL;DR: The 3D volume reconstruction problem requirements, architecture of the developed prototype system and the tradeoffs of the system design are presented.
Journal ArticleDOI
Semantic middleware for e-Science knowledge spaces
Joe Futrelle,Jeff Gaynor,J. Plutchak,James D. Myers,Robert E. McGrath,Peter Bajcsy,Jason Kastner,Kailash Kotwani,Jong Sung Lee,Luigi Marini,Rob Kooper,Terry McLaren,Yong Liu +12 more
TL;DR: Tupelo has enabled the recent work creating e‐Science cyberenvironments to serve distributed, active scientific communities, allowing researchers to develop, coordinate and share datasets, documents, and computational models, while preserving process documentation and other contextual information needed for distribution and archiving.