Approach for the evaluation of speckle deformation measurements by application of the wavelet transformation

Berger, E.; Linden, Wolfgang von der; Dose, V.; Ruprecht, M.; Koch, Alexander W.

doi:10.1364/ao.36.007455

Cited by 12 publications

(5 citation statements)

References 2 publications

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“…On the other hand, the human eye is capable of detecting the fringe pattern, even in low contrast images. This is an indication of different length scales of noise and fringes, which have already been exploited by the use of wavelets for fringe detection 17 .…”

Section: Algorithmmentioning

confidence: 99%

Bayesian neural-networks-based evaluation of binary speckle data

2004

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We present a new method using Bayesian probability theory and neural networks for the evaluation of speckle interference patterns for an automated analysis of deformation and erosion measurements. The method is applied to the fringe pattern reconstruction of speckle measurements with a Twyman-Green interferometer. Given a binary speckle image, the method returns the fringe pattern without noise, thus removing the need for smoothing and allowing a straightforward unwrapping procedure and determination of the surface shape. Because no parameters have to be adjusted, the method is especially suited for continuous and automated monitoring of surface changes.

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Section: Algorithmmentioning

confidence: 99%

Bayesian neural-networks-based evaluation of binary speckle data

2004

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“…3. The figure shows a simulated data set designed to match binary images from phase shifting speckle interferometry of microscopically rough surfaces [14]. The clear separation of the interferometric pattern from the noise is a necessary prerequisite for the subsequent surface reconstruction from the interferogram.…”

Section: Examplesmentioning

confidence: 99%

Invariance priors for Bayesian feed-forward neural networks

2006

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Neural Networks are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand this flexibility can cause over-fitting and can hamper the generalization properties of neural networks. Many approaches to regularize NN have been suggested but most of them based on ad-hoc arguments. Employing the principle of transformation invariance we derive a general prior in accordance with the Bayesian probability theory for feedforward networks. An optimal network is determined by Bayesian model comparison verifying the applicability of this approach. Additionally the presented prior affords cell pruning.

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“…Fortunately, a wavelet transform can be used to overcome the disadvantages of the Fourier transform in fringe analysis. [12][13][14][15][16][17][18][19][20] The biggest difference between Fourier analysis and wavelet analysis is that the latter utilizes a pulsed function and the former utilizes a continuous function when filtering the signal. Therefore, the latter will maintain the characteristic of the local signal and the former will lose that localized information.…”

Section: Wavelet-based Image Processingmentioning

confidence: 99%

Experimental verification of improvement of phase shifting moire´ interferometry using wavelet-based image processing

Liu

2004

Opt. Eng

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Phase shifting interferometry is combined with wavelet-based image processing techniques to extract precise phase information for applications of moiré interferometry. Specifically, a diffraction grating identical to the specimen grating is used to introduce the additional phase shifts needed to implement phase shifting moiré interferometry. The phase map is calculated with the four-step phase shifting algorithm with 90-deg relative shifts between adjacent frames. Subsequently, continuous wavelet transform processing is applied to the derived phase map to remove the noise and extract precise phase information. This precise phase map is then used to calculate the in-plane strain of the specimen under loading. A curve-smoothing algorithm based on a discrete wavelet transform is applied to the resultant in-plane strain map to eliminate the noise generated through the derivative calculation. To demonstrate the usefulness of this technique, uniform vertical loading is applied to a three-layer ball grid array (BGA) package and the strain is experimentally obtained. The reconstructed phase and in-plane strains are compared to the simulation results obtained using Ansys ® .

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Approach for the evaluation of speckle deformation measurements by application of the wavelet transformation

Cited by 12 publications

References 2 publications

Bayesian neural-networks-based evaluation of binary speckle data

Bayesian neural-networks-based evaluation of binary speckle data

Invariance priors for Bayesian feed-forward neural networks

Experimental verification of improvement of phase shifting moire´ interferometry using wavelet-based image processing

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