Spectrum decomposition in Gaussian scale space for uneven illumination image binarization

Although most images in industrial applications have fewer targets and simple image backgrounds, binarization is still a challenging task, and the corresponding results are usually unsatisfactory because of uneven illumination interference. In order to efficiently threshold images with nonuniform illumination, this paper proposes an efficient global binarization algorithm that estimates the inhomogeneous background surface of the original image constructed from the first k leading principal components in the Gaussian scale space (GSS). Then, we use the difference operator to extract the distinct foreground of the original image in which the interference of uneven illumination is effectively eliminated. Finally, the image can be effortlessly binarized by an existing global thresholding algorithm such as the Otsu method. In order to qualitatively and quantitatively verify the segmentation performance of the presented scheme, experiments were performed on a dataset collected from a nonuniform illumination environment. Compared with classical binarization methods, in some metrics, the experimental results demonstrate the effectiveness of the introduced algorithm in providing promising binarization outcomes and low computational costs.

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An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation

Entropy ◽

10.3390/e23111429 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1429

Author(s):

Yuncong Feng ◽

Wanru Liu ◽

Xiaoli Zhang ◽

Zhicheng Liu ◽

Yunfei Liu ◽

...

Keyword(s):

Image Segmentation ◽

Base Layer ◽

Multilevel Thresholding ◽

Original Image ◽

Mr Image ◽

Interval Iteration ◽

Otsu Method ◽

Thresholding Algorithm ◽

Mr Image Segmentation ◽

Fusion Scheme

In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid L1 − L0 layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.

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Study of pumping effect in flow-through electrolysers

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19832232 ◽

1983 ◽

Vol 48 (8) ◽

pp. 2232-2248 ◽

Cited By ~ 1

Author(s):

Ivo Roušar ◽

Michal Provazník ◽

Pavel Stuhl

Keyword(s):

Natural Convection ◽

Balance Equation ◽

Volume Fraction ◽

Industrial Applications ◽

Pumping Effect ◽

Flow Through ◽

The Mean ◽

The Difference

In electrolysers with recirculation, where a gas is evolved, the pumping of electrolyte from a lower to a higher level can be effected by natural convection due to the difference between the densities of the inlet electrolyte and the gaseous emulsion at the outlet. An accurate balance equation for calculation of the rate of flow of the pumped liquid is derived. An equation for the calculation of the mean volume fraction of bubbles in the space between the electrodes is proposed and verified experimentally on a pilot electrolyser. Two examples of industrial applications are presented.

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Remaining useful life estimation based on the joint use of an observer and a hidden Markov model

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211044343 ◽

2021 ◽

pp. 1748006X2110443

Author(s):

Toufik Aggab ◽

Pascal Vrignat ◽

Manuel Avila ◽

Frédéric Kratz

Keyword(s):

Markov Models ◽

Hidden Markov ◽

Practical Interest ◽

Markov Property ◽

Rolling Bearing ◽

Industrial Applications ◽

Remaining Useful Life ◽

Discrete Observations ◽

Useful Life ◽

The Difference

We propose an approach for failure prognosis based on the estimation of the Remaining Useful Life (RUL) of a system in a situation in which monitoring signals providing information about its degradation evolution are not measured and no operating model of the system is available. These conditions are of practical interest for industrial applications such as mechanical (e.g. rolling bearing) or electrical (e.g. wind turbine) devices or equipment-critical components (e.g. batteries) in which the addition of sensors to the system is not feasible (e.g. space limitations for sensors, cost, etc.). The approach is based on an estimation of the system degradation using residual generation (where the difference between the system and the observer outputs is processed) and Hidden Markov Models with discrete observations. The prediction of the system RUL is given by the Markov property concerning the mean time before absorption. The approach comprises two phases: a training phase to model the degradation behavior and an “on-line” use phase to estimate the remaining life of the system. Two case studies were conducted for RUL prediction to verify the effectiveness of the proposed approach.

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Enhancement of Satellite Imagesusing Image Sharpening Technique

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1096.0886s219 ◽

2019 ◽

Vol 8 (6S2) ◽

pp. 351-354

Keyword(s):

Image Processing ◽

Color Image ◽

Histogram Equalization ◽

Grayscale Image ◽

Original Image ◽

High Quality ◽

Wide Range ◽

Equalization Technique ◽

The Difference

In many image processing applications, a wide range of image enhancement techniques are being proposed. Many of these techniques demanda lot of critical and advance steps, but the resultingimage perception is not satisfactory. This paper proposes a novel sharpening method which is being experimented with additional steps. In the first step, the color image is transformed into grayscale image, then edge detection process is applied using Laplacian technique. Then deduct this image from the original image. The resulting image is as expected; After performing the enhancement process,the high quality of the image can be indicated using the Tenengrad criterion. The resulting image manifested the difference in certain areas, the dimension and the depth as well. Histogram equalization technique can also be applied to change the images color.

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Image-to-Image Translation with Multi-Path Consistency Regularization

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/413 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jianxin Lin ◽

Yingce Xia ◽

Yijun Wang ◽

Tao Qin ◽

Zhibo Chen

Keyword(s):

Machine Learning ◽

Computer Vision ◽

The Other ◽

Natural Images ◽

Original Image ◽

Target Domain ◽

Source Domain ◽

Face To Face ◽

Image Translation ◽

The Difference

Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from a source domain to a target domain as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images; the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version. In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation from source domain to target domain and indirect translation from source domain to an auxiliary domain to target domain, to regularize training. For multi-domain translation (at least, three) which focuses on building translation models between any two domains, at each training iteration, we randomly select three domains, set them respectively as the source, auxiliary and target domains, build the multi-path consistency loss and optimize the network. For two-domain translation, we need to introduce an additional auxiliary domain and construct the multi-path consistency loss. We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-to-face translation, paint-to-photo translation, and de-raining/de-noising translation.

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Numerical Simulations of Particle-Laden Flow Based on Given Friction Reynolds Number and Mean Reynolds Number Respectively

Volume 1A, Symposia: Advances in Fluids Engineering Education; Turbomachinery Flow Predictions and Optimization; Applications in CFD; Bio-Inspired Fluid Mechanics; Droplet-Surface Interactions; CFD Verification and Validation; Development and Applications of Immersed Boundary Methods; DNS, LES, and Hybrid RANS/LES Methods ◽

10.1115/fedsm2014-21332 ◽

2014 ◽

Cited By ~ 1

Author(s):

Zhenzhong Li ◽

Jinjia Wei ◽

Bo Yu

Keyword(s):

Numerical Simulation ◽

Reynolds Number ◽

Direct Numerical Simulation ◽

Wide Spectrum ◽

Natural World ◽

Industrial Applications ◽

Particle Parameters ◽

The Mean ◽

The Difference ◽

Particle Laden Flow

Multiphase flow with particles covers a wide spectrum of flow conditions in natural world and industrial applications. The experiments and the direct numerical simulation have become the most popular means to study the dilute particle-laden flow in the last two decades. In the experimental study, the mean Reynolds number is often adjusted to the value of single-phase flow for each set of particle conditions. However, the friction Reynolds number usually keeps invariable in the direct numerical simulation of the particle-laden flows for convenience. In this study the effect of the difference between given mean Reynolds number and friction Reynolds number was investigated. Two simulations were performed for each set of particle parameters, and the mean Reynolds number and friction Reynolds number were kept invariant respectively. From the results it can be found that the turbulence intensity and the dimensionless velocities are larger when keeping the friction Reynolds constant. And the results calculated from the cases of keeping the mean Reynolds number invariable agree with the experiment results better. In addition, the particle distribution along the wall-normal coordinate was found to be unchanged between two simulation conditions. As a suggestion, keeping the same mean Reynolds number in the direct numerical simulation of particle-laden flow is more appropriate.

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FEATURES OF DERIVATIVE CONTINUITY IN SHAPE

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001499000677 ◽

1999 ◽

Vol 13 (08) ◽

pp. 1181-1199

Author(s):

PHILIP F. HENSHAW

Keyword(s):

Time Series ◽

Image Recognition ◽

Scale Space ◽

Complex Processes ◽

Behavioral Dynamics ◽

Higher Derivative ◽

Smoothing Kernel ◽

Key Features ◽

Invariant Relationship ◽

Gaussian Scale Space

Derivative continuity is a distributed invariant relationship between parts of flowing shapes. The original techniques presented here were developed for making the behavioral dynamics of complex processes more recognizable, but are equally applicable to assisting in the recognition of shapes in images. Regularizing a sequence using a constraint of derivative continuity is equivalent to using a bimodal smoothing kernel, producing a distinct bias for reducing variation on higher derivative levels, sharply defining shape with minimal suppression of shape. To help determine where reconstructing shapes in this way is valid, a test was developed to help distinguish combinations of noise and smooth flows from random walks. This helps distinguish between illusory and genuine, data shapes but also exposes a flair in using this and other measures of scaling behavior for diagnostic purposes. Gaussian scale space techniques in use for some time in image recognition, for identifying reliable landmarks in the shapes of outlines, are demonstrated for use in identifying key features of shape in time series.

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Surface Chemistry of Mesoporous Materials: Effect of Nanopore Confinement

MRS Proceedings ◽

10.1557/proc-751-z3.40 ◽

2002 ◽

Vol 751 ◽

Author(s):

Yifeng Wang ◽

Charles Bryan ◽

Huifang Xu ◽

Huizhen Gao

Keyword(s):

Surface Chemistry ◽

Surface Acidity ◽

Scale Space ◽

Acid Base ◽

Acidity Constants ◽

Solution Interface ◽

Ion Sorption ◽

Alumina Particles ◽

The Difference ◽

Nanopore Confinement

AbstractAcid-base titration and metal sorption experiments were performed on both mesoporous alumina and alumina particles under various ionic strengths. It has been demonstrated that surface chemistry and ion sorption within nanopores can be significantly modified by a nano-scale space confinement. As the pore size is reduced to a few nanometers, the difference between surface acidity constants (ΔpK = pK2 – pK1) decreases, giving rise to a higher surface charge density on a nanopore surface than that on an unconfined solid-solution interface. The change in surface acidity constants results in a shift of ion sorption edges and enhances ion sorption on that nanopore surfaces.

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