2007
DOI: 10.1016/j.imavis.2006.03.009
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Computer vision methods for optical microscopes

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Cited by 31 publications
(16 citation statements)
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References 7 publications
(4 reference statements)
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“…The spatial position of the AFM-tip is determined from the position of the micromanipulator on the assumption that the x−y−z piezoelectric positioning stage is very precise (closedloop control) with a nanometer positioning accuracy. In order to recover the position of the AFM-tip with high accuracy, we choose the image normalized correlation technique [20] which is fast, invariant to linear radiometric changes and can be implemented in a context of Kalman filtering (Gaussian noise assumptions) [23] or particle filtering (nonGaussian noise assumptions) [24]. Methods such as wavelet decomposition may be used to improve the tracking precision with a multi-resolution and auto-focusing approach [25].…”
Section: B High-accuracy Target Constructionmentioning
confidence: 99%
“…The spatial position of the AFM-tip is determined from the position of the micromanipulator on the assumption that the x−y−z piezoelectric positioning stage is very precise (closedloop control) with a nanometer positioning accuracy. In order to recover the position of the AFM-tip with high accuracy, we choose the image normalized correlation technique [20] which is fast, invariant to linear radiometric changes and can be implemented in a context of Kalman filtering (Gaussian noise assumptions) [23] or particle filtering (nonGaussian noise assumptions) [24]. Methods such as wavelet decomposition may be used to improve the tracking precision with a multi-resolution and auto-focusing approach [25].…”
Section: B High-accuracy Target Constructionmentioning
confidence: 99%
“…The technique of SFF is primarily useful in places where there are space constraints and installing more than one camera is not feasible. The technique has been successfully used in the area of microrobotics [13], medical diagnostics, and petrological and mineralogical research [14] [15]. Sahay et al [16] [17] recently extended the SFF framework for super resolving the depth profile and focused image.…”
Section: Introductionmentioning
confidence: 99%
“…The SFF technique has been successfully utilized in many industrial applications, i.e. microelectronics [29], industrial inspection [33], medical diagnostics [5], 3D cameras [23], TFT-LCD color filter manufacturing [2], and comparison of polymers [26].…”
Section: Introductionmentioning
confidence: 99%