DocumentCode :
2506073
Title :
Fast level set estimation from projection measurements
Author :
Krishnamurthy, Kalyani ; Bajwa, Waheed U. ; Willett, Rebecca ; Calderbank, Robert
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
fYear :
2011
fDate :
28-30 June 2011
Firstpage :
585
Lastpage :
588
Abstract :
Estimation of the level set of a function (i.e., regions where the function exceeds some value) is an important problem with applications in digital elevation maps, medical imaging, and astronomy. In many applications, however, the function of interest is acquired through indirect measurements, such as tomographic projections, coded-aperture measurements, or pseudo-random projections associated with compressed sensing. This paper describes a new methodology and associated theoretical analysis for rapid and accurate estimation of the level set from such projection measurements. The proposed method estimates the level set from projection measurements without an intermediate function reconstruction step, thereby leading to significantly faster computation. In addition, the coherence of the projection operator and McDiarmid´s inequality are used to characterize the estimator´s performance.
Keywords :
data compression; estimation theory; signal reconstruction; McDiarmid inequality; astronomy; coded-aperture measurements; compressed sensing; digital elevation maps; fast level set estimation; intermediate function reconstruction; medical imaging; projection measurements; projection operator; pseudo-random projections; tomographic projections; Biomedical imaging; Coherence; Estimation; Extraterrestrial measurements; Image reconstruction; Level set; Noise; Compressed sensing; coherence; level sets; performance bounds; segmentation; thresholding;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2011 IEEE
Conference_Location :
Nice
ISSN :
pending
Print_ISBN :
978-1-4577-0569-4
Type :
conf
DOI :
10.1109/SSP.2011.5967766
Filename :
5967766
Link To Document :
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