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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
Author Correction: QUAREP-LiMi: a community endeavor to advance quality assessment and reproducibility in light microscopy
Ulrike Boehm,Glyn Nelson,Claire M. Brown,S. Bagley,Peter Bajcsy,Johanna Bischof,Aurélien Dauphin,Ian M. Dobbie,John E. Eriksson,Orestis Faklaris,Julia Fernandez-Rodriguez,Alexia Ferrand,Laurent Gelman,Ali Gheisari,Hella Hartmann,Christian Kukat,Alex Laude,Miso Mitkovski,Sebastian Munck,Alison J. North,Tobias M. Rasse,Ute Resch-Genger,Lucas Schuetz,Arne Seitz,Caterina Strambio-De-Castillia,Jason R. Swedlow,Ronald C. Nitschke +26 more
Book ChapterDOI
Object measurements from 2D microscopy images
TL;DR: This chapter addresses object measurements from 2D microscopy images by characterizing feature variations across Python scikit-image, CellProfiler, MaZda, ImageJ, and in-house Java libraries and quantifying numerical variability of image features and feature-based classification outcomes.
Proceedings ArticleDOI
Large field of view quantitative phase imaging of induced pluripotent stem cells and optical pathlength reference materials
Edward Kwee,Alexander W. Peterson,Jeffrey Stinson,Michael Halter,Liya Yu,Michael Majurski,Joe Chalfoun,Peter Bajcsy,John T. Elliott +8 more
TL;DR: A quantitative phase imaging workflow which includes acquisition, processing, and stitching multiple adjacent image tiles across a large field of view (LFOV) of a culture vessel can provide non-destructive traceable imaging method for novel iPSC heterogeneity characterization.
Proceedings ArticleDOI
Automated ranking of stem cell colonies by translating biological rules to computational models
TL;DR: This paper defines a new feature set that uniquely characterizes the visual clues from images of the colonies and biological rules experts use to rank colonies from image data, and outlines a method for establishing relationships between the commonly used Haralick features and custom-designed features.
Journal ArticleDOI
Multicore speedup for automated stitching of large images
Peter Bajcsy,Rob Kooper +1 more
TL;DR: This work deployed existing image-pyramid stitching methods onto multicore and parallel architectures to benchmark how performance improves with the addition of computing nodes and explored the benefits of multiple hardware architectures and parallel computing to reduce the time needed to stitch very large images.