Comparison of Different Quadratic Regularization for Electrical Impedance Tomography

Zhou, Zhou; Malone, Emma; Santos, Gustavo Sato dos; Li, Nan; Xu, Hui; Holder, David

doi:10.1007/978-3-319-11128-5_50

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…We emphasize that these simulations employed the Gauss-Newton 1-step inversion, the most commonly used for tactile sensing, with the Laplace prior, the associated default in EIDORS, which influence how noise affects the image (for examples of the effects of other algorithms see e.g. [29,30]). Figure 2 shows how, for a given set of simulation parameters, an image first loses fidelity and eventually breaks up completely as the noise amplitude increases.…”

Section: Resultsmentioning

confidence: 99%

EIT for tactile sensing: considering artefacts in hyperparameter selection

Smela

2022

Eng. Res. Express

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Electrical impedance tomography (EIT) is employed in tactile sensing to create an image of impedance changes within a continuous sensor using electrodes placed only at the perimeter. Noise destabilizes EIT images, and the onset of instability is associated with the appearance of artefacts, which are spurious image features that are not associated with sensor responses to contacts. Artefacts are detrimental because the essential features of contacts, or targets, must be correctly represented, including how many there are and their approximate shapes and locations, yet their presence has not previously been used as a performance measure. Regularization, the extent of which is determined by the hyperparameter λ, is used to manage the destabilization, but it results in spatially non-uniform defocusing of image features. We therefore introduce an efficient criterion for evaluating tactile sensor image quality based on the onset of artefacts. Using simulated data and the one-step Gauss-Newton reconstruction algorithm with the Laplace prior, the noise level at which artefacts first appear at a given hyperparameter, or noise threshold Nth(λ), is found. How the relationship depends on target characteristics and other factors is shown, and Nth can vary by orders of magnitude. The conceptually similar BestRes method and the classical L-curve and generalized cross-validation (GCV) methods for determining an optimal hyperparameter are evaluated using the criterion of artefact-free images. The L-curve generates hyperparameters that are well matched to the onset of artefacts, except at high noise; the other two result in artefacts. For high dynamic range tactile inputs, setting the threshold at a fixed value using a method such as Nth is not advisable, and automatic regularization tailored to the input may be needed using a method such as the L-curve or GCV, provided that the computational overhead is tolerable.

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Section: Resultsmentioning

confidence: 99%

EIT for tactile sensing: considering artefacts in hyperparameter selection

Smela

2022

Eng. Res. Express

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“…It only defines the image preliminary characteristics Also, sample images might have unnecessary characteristics like with high noise, low Figure. 1 Thermal imaging helps find/solve problems in process equipment with abnormal heat flow contrast, background interferences etc. [4]. Therefore an appropriate algorithm to facilitate these existing drawbacks including the thermal imager to enhance the quality of extracted thermal images.…”

Section: Introductionmentioning

confidence: 99%

Color Enhancement Algorithm using the Integrated K-means Clustering and Inverted Otsu Method for Thermal Object Characterization

Park¹,

Kim²

2018

IJIES

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Thermal imaging plays an important role in numerous applications like in defense, security, military, and other industry that requires a non-contact temperature measurement, etc. This proposed algorithm improves the thermal image especially during the thresholding process in which the desired object is separated from the background and can easily be identified. The separate extracted images produced by K-means clustering and inverted Otsu methods were processed by Canny edge detection and color mapping to highlight the necessary characteristics of the sampled thermal images. This work is synthesized with Xilinx Zync 7000 ZED ZC702. The experimental results of this blended combination of K-means and inverted Otsu methods show significant distinguishable features in terms of edge and color. It outperforms the other color correction method in terms of processing time. Moreover, this implementation reduced resource utilization, and minimizes the misclassified pixel in different noise variance.

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“…A thermal imager equipment alone is enough, it only defines the image preliminary characteristics, because sample images have unnecessary characteristics like with high noise, low contrast, background interferences etc. [4]. Implementing an appropriate algorithm to facilitate these existing drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…1) The response to the temperature of an object, as shown in equation 4 (4) Where W (, T) is the radiance for a wavelength  and measures in m while T stands for temperature in Kelvin, e() is the emissivity, c1 and c2 are the constants for this Planck's equation. The radiance is given in units of watts m -2 steradians -1 m [41].…”

mentioning

confidence: 99%

FPGA-based color enhancement algorithm for thermal object characterization

Park¹,

Kim²

2018

IJET

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Thermal imaging is used in numerous applications, especially in security, medical and other industry which requires a non-contact temperature measurement. This proposed algorithm improves the thermal image and makes more visible the separation of the sampled object from its background. The extracted image is produced by the following techniques: the pre-processing techniques are the combination of K-means clustering, and inverted Otsu method; canny edge detection and color mapping are used for highlighting the necessary characteristics of the sampled thermal image. The experimental results of this proposed algorithm show significant distinguishable features in terms of edge and color enhancement. It outperforms the other color correction method in terms of processing time, and the implementation reduced the resource utilization. Moreover, it minimizes the misclassified pixel in different noise variance. This work is synthesized with Xilinx Zync 7000 ZED ZC702.

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Comparison of Different Quadratic Regularization for Electrical Impedance Tomography

Cited by 3 publications

References 5 publications

EIT for tactile sensing: considering artefacts in hyperparameter selection

EIT for tactile sensing: considering artefacts in hyperparameter selection

Color Enhancement Algorithm using the Integrated K-means Clustering and Inverted Otsu Method for Thermal Object Characterization

FPGA-based color enhancement algorithm for thermal object characterization

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