Mathematical Morphology Algorithm Research Articles

A Magnetic Flux Leakage Detector for Ferromagnetic Pipeline Welds with a Magnetization Direction Perpendicular to the Direction of Travel.

For the sake of realizing the safety detection of natural gas and petroleum pipeline welds, this paper designs a ferromagnetic pipeline weld magnetic flux leakage detector based on the calculation of the magnetic circuit of the detection probe, with the magnetization direction perpendicular to the traveling direction. The traditional pipeline magnetic flux leakage detection device uses a detection system mode in which the magnetization direction is parallel to the direction of travel. However, due to the structural characteristics of the weld, the traditional detection system mode is not applicable. Since the weld magnetic flux leakage detector needs to travel along the direction of the weld, the detector designed in this paper rotates the magnetizer 90 degrees along the direction of the weld seam so that the magnetization direction is perpendicular to the direction of travel, breaking through the technical barrier that make traditional magnetic flux leakage detection devices unsuitable for weld detection. The detection device includes a magnetizing structure, a data sampling device, and a driving and traveling device. The magnetic flux leakage signal collected by the detector is converted into a digital image in the form of a grayscale matrix. Using mathematical morphology and chain code algorithms in image processing technology, a pipeline weld defect inversion software system is developed, and a preliminary quantitative analysis of pipeline weld defects is achieved. The application of this technology enables the inspection and protection of oil and gas pipeline welds throughout their life cycle, broadens the scope of existing inspection objects, and is of great safety significance for ensuring national public security.

Solar magnetic flux rope identification with GUITAR: GUI for Tracking and Analysing flux Ropes

Modelling the early evolution of magnetic flux ropes (MFRs) in the solar atmosphere is crucial for understanding their destabilization and eruption mechanism. Identifying the relevant magnetic field lines in simulation data, however, is not straightforward. In previous work an extraction and tracking method was developed to facilitate this task. Here, we present the corresponding graphical user interface (GUI), called GUITAR (GUI for Tracking and Analysing flux Ropes), with the aim to offer a variety of tools to the community for identifying and tracking MFRs. The starting point is a map of a selected proxy parameter for MFRs, e.g., a map of the twist-parameter Tw, current density, etc. We showcase how the GUITAR tools can be used to disentangle a multi-MFR system and facilitate in-depth analysis of their properties and evolution by applying them on a time-dependent data-driven magnetofrictional model (TMFM) simulation of solar active region AR12473. We show the MFR extraction using Tw maps, together with targeted use of mathematical morphology algorithms and discuss the evolution of the system.

The automatic identification and tracking of coronal flux ropes

Context. Constructing the relevant magnetic field lines from active region modelling data is crucial to understanding the underlying instability mechanisms that trigger the corresponding eruptions. Aims. We present a magnetic flux rope (FR) extraction tool for solar coronal magnetic field modelling data that builds upon a recent methodology. The newly developed method is then compared against its previous iteration. Furthermore, we apply the scheme to magnetic field simulations of active regions AR12473 (similar to our previous study) and AR11176. We compare the method to its predecessor and study the 3D movement of the newly extracted FRs up to heights of 200 and 300 Mm, respectively. Methods.The extraction method is based on the twist parameter Tw and a variety of mathematical morphology (MM) algorithms, including the opening transform and the morphological gradient. We highlight the differences between the methods by investigating the circularity of the FRs in the plane we extract from. The simulations for the active regions are carried out with a time-dependent data-driven magnetofrictional model (TMFM). We investigate the FR trajectories by tracking their apex throughout the full simulation time span. Results. Comparing the newly developed method to the previous extraction scheme, we demonstrate that this upgrade provides the user with more tools and less a priori assumptions about the FR shape, which in turn leads to a more accurate set of field lines. Despite some differences, both the newly extracted FR of AR12473 and the FR derived with the old iteration of the method show a similar general appearance, confirming that the two methods indeed extract the same structure. The methods differ the most in their emergence and formation stages, where the newly extracted FR deviates significantly from a perfectly circular cross-section (which was the basic assumption of the initial method). The propagation analysis yields that the erupting FR from AR12473 indeed shows stronger dynamics than the AR11176 FR and a significant deflection during its ascent through the domain. The modelling results are also verified with observations: AR12473 is dynamic and eruptive, while AR11176 only features an eruption outside of our simulation time window. Conclusions. We implemented a FR extraction method, incorporating mathematical morphology algorithms for 3D solar magnetic field simulations of active region FRs. This scheme was applied to AR12473 and AR11176. We find that the clearly eruptive FR of AR12473 experiences significant deflection during its rise. The AR11176 FR appears more stable, though there is still a notable deflection. This confirms that at these low coronal heights, FRs undergo significant changes in the direction of their propagation even for less dynamic cases.

Stable first-arrival picking through mathematical morphology and edge detection

SUMMARY First-arrival picking is a fundamental and challenging task in seismic data processing. Existing algorithms such as the cross-correlation algorithm (CCT) struggle to achieve satisfactory noise immunity while maintaining picking efficiency. In this paper, we propose the first-arrival picking through mathematical morphology and edge detection (FPME) algorithm with three steps. The converting step transforms the original data into a binary image though a filtering technique, where the binary image represents the global energy distribution of high-energy and low-energy samples. The rendering step delimits the signal zone in the binary image through morphological operations, where the signal zone eliminates the noise in the upper part. The picking step detects first arrivals in the signal zone through the edge detection methods, where an evaluation function is designed to select the optimal detection results as first arrivals. Experiments were performed on four field data sets. The results demonstrated that FPME is more stable and reliable than five classic and popular algorithms.

Extraction of farmland shelterbelts from remote sensing imagery based on a belt-oriented method

IntroductionFarmland shelterbelts play a positive role in ensuring food security and ecological safety. The absence or degradation of shelterbelt structures can lead to fragmentation of the remotely extracted results. Conversely, shelterbelt maintenance and management system considers these shelterbelts as entire units, even if they are divided into several parts by the gaps in them. It is essential to propose a remote extraction method to fill in fragmented results and accurately represent the distribution of farmland shelterbelts.MethodsIn this study, random forest algorithm was employed to classify land cover from ZY-3 (ZiYuan-3 satellite from China) imagery. Then, a thinning algorithm of mathematical morphology was applied to extract farmland shelterbelts, and the straight-line connection algorithm was used to connect central lines belonging to the same belt. Finally, the result was validated using nine uniformly distributed training sample areas across the entire region.Results and discussionThis method achieved a correct identification rate of 94.9% within the training areas. Among the different regions, the highest identification accuracy recorded was 98.4% and the lowest was 87.7%. In conjunction with cropland information and the shape index of forest patches, it was possible to remove information for non-farmland shelterbelts without introducing external information. This approach achieved a more refined extraction of forestland information. The combination of the thinning algorithm and straight-line connection algorithm addressed the issue of fragmented results in farmland shelterbelt extraction, compensating for the limitations of relying solely on mathematical morphology for belt connectivity. The research method can provide technical support for the monitoring and management of farmland shelterbelts.

Paper Defect Detection Algorithm Based on the Mathematical Morphology and Computer Vision

A method for detecting paper’s surface defects is proposed, which is based on mathematical morphology algorithm and computer vision technique. Firstly, the mean filtering and wavelet denoising in image pre-processing are used to eliminate the image noise, and wavelet analysis can achieve image enhancement and increase the contrast between the target area and the background. And then, an improved threshold Canny operator is used to segment the image to extract the complete scratch line and other defect edges. Finally, the target region features are coloured by mathematical morphology processing. The method’s stimulating result is verified in the Matlab software, from which we can conclude that the method’s detecting effect is very effective.

Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation

Background and objectiveThe automated detection of atrial activations (AAs) recorded from intracardiac electrograms (IEGMs) during atrial fibrillation (AF) is challenging considering their various amplitudes, morphologies and cycle length. Activation time estimation is further complicated by the constant changes in the IEGM active zones in complex and/or fractionated signals. We propose a new method which provides reliable automatic extraction of intracardiac AAs recorded within the pulmonary veins during AF and an accurate estimation of their local activation times.MethodsFirst, two recently developed algorithms were evaluated and optimized on 118 recordings of pulmonary vein IEGM taken from 35 patients undergoing ablation of persistent AF. The adaptive mathematical morphology algorithm (AMM) uses an adaptive structuring element to extract AAs based on their morphological features. The relative-energy algorithm (Rel-En) uses short- and long-term energies to enhance and detect the AAs in the IEGM signals. Second, following the AA extraction, the signal amplitude was weighted using statistics of the AA sequences in order to reduce over- and undersensing of the algorithms. The detection capacity of our algorithms was compared with manually annotated activations and with two previously developed algorithms based on the Teager–Kaiser energy operator and the AF cycle length iteration, respectively. Finally, a method based on the barycenter was developed to reduce artificial variations in the activation annotations of complex IEGM signals.ResultsThe best detection was achieved using Rel-En, yielding a false negative rate of 0.76% and a false positive rate of only 0.12% (total error rate 0.88%) against expert annotation. The post-processing further reduced the total error rate of the Rel-En algorithm by 70% (yielding to a final total error rate of 0.28%).ConclusionThe proposed method shows reliable detection and robust temporal annotation of AAs recorded within pulmonary veins in AF. The method has low computational cost and high robustness for automatic detection of AAs, which makes it a suitable approach for online use in a procedural context.

Infrared Image Segmentation for Power Equipment Using Linear Spectral Clustering and Maximal Similarity-based Region Merging

<p>The diagnostic method of power equipment based on infrared images is widely used because it has the advantages of non-contact and does not affect the online operation of power equipment. However, in actual using, the power equipment diagnosis method based on infrared image still relies on manual judgment, that is, the detection personnel can judge the fault according to the obtained infrared image of power equipment by experience. This process consumes a lot of time, and the subjectivity is strong, misjudgment rate is higher, which cannot meet the requirements of modern smart grid development. Infrared image of power equipment contains a lot of noise, and the edge is fuzzy. In this paper, we propose a new infrared image segmentation method for power equipment by using linear spectral clustering and maximal similarity-based region merging under complex backgrounds. In this method, the linear spectral clustering algorithm (LSC) is used to segment the image into super-pixels, and the pixels with similar color and distance are clustered to the same center. The calculated OTSU threshold based on the global image is used to pre-label the background of each super-pixel block. The maximum similarity-based region merging algorithm (MSRM) is utilized to merge the super-pixel blocks. Meanwhile, it obtains the target equipment, the over-segmentation and under-segmentation rates are reduced effectively. Finally, the mathematical morphology algorithm is used to post-process the image. Experimental results show that, compared with other algorithms, this new method can obtain more accurate and complete target equipment under complex background. </p> <p> </p>

New Hybrid Method for Left Ventricular Ejection Fraction Assessment from Radionuclide Ventriculography Images.

In this paper, we propose a new efficient method of radionuclide ventriculography image segmentation to estimate the left ventricular ejection fraction. This parameter is an important prognostic factor for diagnosing abnormal cardiac function. The proposed method combines the Chan-Vese and the mathematical morphology algorithms. It was applied to diastolic and systolic images obtained from the Nuclear Medicine Department of Salah AZAIEZ Institute. In order to validate our proposed method, we compare the obtained results to those of two methods present in the literature. The first one is based on mathematical morphology, while the second one uses the basic Chan-Vese algorithm. To evaluate the quality of segmentation, we compute accuracy, positive predictive value and area under the ROC curve. We also compare the left ventricle ejection fraction estimated by our method to that of the reference given by the software of the gamma-camera and validated by the expert, using Pearson's correlation coefficient, ANOVA test and linear regression. Static results show that the proposed method is very efficient for the detection of the left ventricle. The accuracy was 98.60%, higher than that of the other two methods (95.52% and 98.50%). Likewise, the positive predictive value was the highest (86.40% vs. 83.63% 71.82%). The area under the ROC curve was also the most important (0.998% vs. 0.926% 0.919%). On the other hand, Pearson's correlation coefficient was the highest (99% vs. 98% 37%). The correlation was significantly positive (p<0.001).

Detecting degraded, prone and transition ecosystems by environmental thresholds and spectral functions

For determining of the desert ecosystem based on transition zones, a quantitative approach was applied in Khorasan Razavi province in northeast of Iran. The spectral and morphological algorithms were implemented in remote-sensing images of MODIS surface reflectance in order to determine the ecogeomorphic thresholds between the transition zones of the semi-arid ecosystems. Furthermore, the spectral angle mapper analysis was applied for the surface conditions and the mathematical morphology algorithm of dilation was used for identifying the recovery and erodibility potential trends in the heterogeneous land surface covers. A detailed assessment of the mapping was achieved by implementation of an iterative self-organized clustering technique (ISODATA) and calculation of separability of formed typologies classes using the transformed divergence algorithm. The assessment of NDVI (normalized difference vegetation index) in each cluster indicates an increasing trend of standard deviation, which shows maximum values near desert thresholds. According to the results, several desert thresholds were detected in susceptible ecosystems to desertification in Khorasan Razavi that were affected by natural and anthropogenic factors. Further evaluation shows the location of the transition zones and the thresholds under changing climatic conditions. In synopsis, in prone areas with the high potential to desertification, the ecosystem is susceptiblt to shift to desert state.

Method for constructing a network of urban roads using satellite images

An automatic method for recognizing the urban network of highways on high-resolution satellite images has been developed and implemented, based on the use of the Mask-RCNN convolutional neural network. The results of the neural network operation are post-processed by the Hough transformation and algorithms of mathematical morphology. The proposed method has acceptable accuracy and high speed of detection of asphalt urban roads.

A Mathematical Morphology Algorithm for Detection and Counting of Nuclear Tracks

Solid state nuclear track detection plays an extremely important role in the field of nuclear technology, but artificial cognition is extremely time-consuming and labor-consuming. In this paper, an automatic measurement system of solid state nuclear track is proposed, in which the microscopic image of the nuclear track is obtained by the camera and processed digitally, and then use software to identify the image content. Based on the image restoration theory, this paper preprocesses such as noise elimination and luminance correction, and the mathematical morphology algorithm is used for track counting and parameter estimation. The comparison results between automatic recognition and manual recognition show that the algorithm can effectively identify track points from images under different conditions and improve work efficiency.

Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

Wind energy plays an increasingly important role in economic development. In this study, we propose a hybrid short-term wind-speed forecasting model comprising multiscale mathematical morphological decomposition (MMMD), K-means clustering algorithm, and stacked denoising autoencoder (SDAE) networks. First, in contrast to traditional signal-decomposing tools, the original wind-speed sequence is decomposed into a series of subsequences with different frequencies and fluctuant levels using the adaptive multiscale mathematical morphological algorithm directly in the time domain. The signal does not need to be transferred from the time domain to the frequency domain; hence, the accuracy can be considerably improved. Moreover, this is the first study that uses a time domain signal-decomposing tool in a hybrid wind forecasting model. Next, the data are split into different clusters of similar frequencies and fluctuant level subsequences using the K-means algorithm. The characteristics of each cluster are then captured using the SDAE as the core forecasting unit. Finally, the predictions of all subsequences are aggregated to obtain the final wind speed. The data from two real wind turbines are used to evaluate the performance of the proposed model, and the forecasting results are compared with five different benchmark models, namely, backpropagation neural network (BPNN), stacked denoising autoencoder (SDAE), mathematical morphology-backpropagation, mathematical morphology-SDAE, and K-means-SDAE for multiple scales, and two novel hybrid wind forecasting models namely, wavelet transform (WT)-K-means-SDAE and variation mode decomposition (VMD)-K-means-long short-term memory networks (LSTMs). The results of the comparison demonstrate that the proposed model provides a short-term wind-speed forecasting method whose prediction accuracy decreases with time; however, the proposed model achieves a better performance in comparison with other exiting models. At same time, the proposed model significantly increases the prediction accuracy of wind-speed forecasting and can be a reference for future research in this area.

Image Restoration by Learning Morphological Opening-Closing Network

Abstract Mathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris.

Geosynchronous orbit (GSO) is the ideal orbit for communication, navigation, meteorology and other satellites, but the space of GSO is limited, and there are still a large number of space debris threatening the safety of spacecraft. Therefore, real-time detection of GSO debris is necessary to avoid collision accidents. Because radar is limited by transmitting power and operating distance, it is difficult to detect GSO debris, so photoelectric detection becomes the mainstream way to detect GSO debris. This paper presents an adaptive real-time detection algorithm for GSO debris in the charge coupled device (CCD) images. The main work is as follows: An image adaptive fast registration algorithm and an enhanced dilation difference algorithm are proposed. Combining with mathematical morphology, threshold segmentation and global nearest neighbor (GNN) multi-target tracking algorithm, the functions of image background suppression, registration, suspected target extraction and multi-target tracking are realized. The processing results of a large number of measured data show that the algorithm can detect dim geostationary earth orbit (GEO) and non-GEO debris in GSO belt stably and efficiently, and the processing speed meets the real-time requirements, with strong adaptive ability, and has high practical application value.

A secured distributed control system for future interconnected smart grids

The reliable performance of the smart grid is a function of the configuration and cyber-physical nature of its constituting systems. Recent public reports showed that there is an increasing rate of attacks on the power systems and that the savvy attackers are now capable of obscuring themselves from conventional intrusion detection systems, bringing about serious threats. Therefore, this work proposes a secured distributed control framework for future smart grids. The work presents a distributed control framework that is secured by means of signal processing tools and consensus protocol. The work models the physical and cyber systems, showing the effect of the different types of cyber-attacks and their mitigation. The proposed framework is based on the use of graph theory and consensus protocol to achieve a global control objective among the different agents in the system. Also, the proposed algorithm is equipped with the mathematical morphology algorithm as a distributed security observer that can analyze the system behavior, and detect and mitigate malicious actions. The proposed cyber-physical system and algorithm are modeled and implemented through MATLAB/Simulink. The results showed the ability of the proposed algorithm in achieving the global power objective and voltage profiles while mitigating a different kind of attack.

Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images

Exudates are a serious complication causing blindness in diabetic retinopathy patients. The main objective of this paper is to develop a novel method to detect exudates lesions in color retinal images by using a morphology mean shift algorithm. The proposed method start with a normalization of the retinal image, contrast enhancement, noise removal, and the localization of the OD. Then, a coarse segmentation method by using mean shift provides a set of exudates and non-exudates candidates. Finally, a classification using the mathematical morphology algorithm (MMA) procedure is applied in order to keep only exudates pixels. The optimal value parameters of the MMA will facilitate an increase of the accuracy results from the solely MSA method by 13.10%. Based on a comparison between the results and ground truth images, the proposed method obtained an average sensitivity, specificity, and accuracy of detecting exudates as 98.40%, 98.13%, and 98.35%, respectively.

A Road Extraction Method Based on Region Growing and Mathematical Morphology from Remote Sensing Images

Road traffic is the important driving factor for economic and social development. With the rapid increase of vehicle population, road traffic problems such as traffic jam and traffic accident have become the bottleneck which restricts economic development. In recent years, natural disasters frequently occur in China. Therefore, it is essential to extract road information to compute the degree of road damage for traffic emergency management. A road extraction method based on region growing and mathematical morphology from remote sensing images is proposed in this paper. According to the road features, the remote sensing image is preprocessed to separate road regions from non-road regions preliminarily. After image thresholding, region growing algorithm is used to extract connected regions. Then we sort connected regions by area to exclude the small regions which are probably non-road objects. Finally, the mathematical morphology algorithm is used to fill the holes inside the road regions. The experimental results show that the method proposed can effectively extract roads from remote sensing images. This research also has broad prospects in dealing with traffic emergency management by the government.

A passive method for privacy protection in the perceptual layer of IoTs

PurposeThe purpose of this paper is to detect the existence of unknown wireless devices which could result negative means to the privacy. The perceptual layer of internet of things (IoTs) suffers the most significant privacy disclosing because of limited hardware resources, huge quantity and wide varieties of sensing equipment. Determining whether there are unknown wireless devices in the communicating environment is an effective method to implement the privacy protection for the perceptual layer of IoTs.Design/methodology/approachThe authors use horizontal hierarchy slicing (HHS) algorithm to extract the morphology feature of signals. Meanwhile, partitioning around medoids algorithm is used to cluster the HHS curves and agglomerative hierarchical clustering algorithm is utilized to distinguish final results. Link quality indicator (LQI) data are chosen as the network parameters in this research.FindingsNowadays data encryption and anonymization are the most common methods to protect private information for the perceptual layer of IoTs. However, these efforts are ineffective to avoid privacy disclosure if the communication environment exists unknown wireless nodes which could be malicious devices. How to detect these unknown wireless devices in the communication environment is a valuable topic in the further research.Originality/valueThe authors derive an innovative and passive unknown wireless devices detection method based on the mathematical morphology and machine learning algorithms to detect the existence of unknown wireless devices which could result negative means to the privacy. The simulation results show their effectiveness in privacy protection.

Using sub-resolution features for self-compensation of the modulation transfer function in remote sensing.

Space smart optical orbiting payloads integrated with attitude and position (SSPIAP) are emerging as an essential tool that is extensively used in microsatellites. The on-orbit imaging link of SSPIAPs includes atmospheric disturbances, defocusing, and relative motion, and other noises, thereby resulting in low modulation transfer function (MTF) and poor image quality. The introduction of MTF compensations has pushed the limits of optical imaging, enabling high-resolution on-orbit dynamic imaging. However, the external targets for compensating MTF are limited by space and time because the availability and access to external targets are infrequently easy when a remote sensor is working on-orbit. Here, a new and robust MTF self-compensation method for a SSPIAP is proposed. In comparison with conventional methods with external targets, this method utilizes multiple natural sub-resolution features (SRFs), occupying several pixels on a uniform background, as observation targets which makes MTFC more maneuverable, robust and authentic. A mathematical morphology algorithm is used to extract SRFs. Moreover, the method relies on a regularization total variation energy function, a sparse prior framework, to invert the MTF. Experimental measurements confirm that the proposed method is effective and convenient to implement. This technique does not rely on specific external targets to compensate the MTF, making it potentially suitable for on-orbit dynamic long-range imaging.