DocumentCode
1427753
Title
Sampling for Shape from Focus in Optical Microscopy
Author
Muhammad, Mannan Saeed ; Choi, Tae-Sun
Author_Institution
Signal & Image Process. Lab., Gwangju Inst. of Sci. & Technol., Gwangju, South Korea
Volume
34
Issue
3
fYear
2012
fDate
3/1/2012 12:00:00 AM
Firstpage
564
Lastpage
573
Abstract
Shape from focus (SFF), which relies on image focus as a cue within sequenced images, represents a passive technique in recovering object shapes in scenes. Although numerous methods have been recently proposed, less attention has been paid to particular factors affecting them. In regard to SFF, one such critical factor impacting system application is the total number of images. A large data set requires a huge amount of computation power, whereas decreasing the number of images causes shape reconstruction to be crude and erroneous. The total number of images is inversely proportional to interframe distance or sampling step size. In this paper, interframe distance (or sampling step size) criteria for SFF systems have been formulated. In particular, light ray focusing is approximated by the use of a Gaussian beam followed by the formulation of a sampling expression using Nyquist sampling. Consequently, a fitting function for focus curves is also obtained. Experiments are performed on simulated and real objects to validate the proposed schemes.
Keywords
Gaussian processes; curve fitting; image sampling; image sequences; optical focusing; optical microscopy; shape recognition; Gaussian beam; Nyquist sampling; SFF sampling; focus curve fitting function; image focus; image sequence; interframe distance; light ray focusing; object shape recovery; optical microscopy; sampling step size; shape from focus sampling; shape reconstruction; Approximation methods; Lenses; Mathematical model; Optical imaging; Optical sensors; Shape; 3D shape recovery; Sampling step size; curve fitting.; optical microscopy; shape from focus; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Microscopy;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2011.144
Filename
6136523
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