Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

A Framework for Generating Realistic Synthetic Sequences of Total Internal Reflection Fluorescence Microscopy Images

Date

Authors

Rezatofighi, Seyed

Pitkeathly, William T.E.

Gould, Stephen

Hartley, Richard

Mele, Katarina

Hughes, William E

Burchfield, James G.

Publisher

IEEE

Abstract

Since generation of reliable ground truth annotation of fluorescence microscopy sequences is usually a laborious and expensive task, many proposed detection and tracking methods have been evaluated using synthetic data with known ground truth. However, di

Keywords

Keywords: Background estimation; Detection and tracking; Image formation process; Shape deformation; Synthetic data; TIRFM; Total internal reflection fluorescence; Total internal reflection fluorescence microscopy; Fluorescence microscopy; Medical imaging; Refracti Background estimation; Dynamic model; Synthetic data; TIRFM; Vesicle shape deformation

URI

http://hdl.handle.net/1885/65973

Collections

ANU Research Publications

Source

A Framework for Generating Realistic Synthetic Sequences of Total Internal Reflection Fluorescence Microscopy Images

Type

Conference paper

DOI

10.1109/ISBI.2013.6556436

Restricted until

2037-12-31

Downloads

File

Description

01_Rezatofighi_A_Framework_for_Generating_2013.pdf (462.76 KB)

02_Rezatofighi_A_Framework_for_Generating_2013.pdf (431.61 KB)