Morphological Edge Detection Research Articles

Precision Ice Detection on Power Transmission Lines: A Novel Approach with Multi-Scale Retinex and Advanced Morphological Edge Detection Monitoring

Line icings on the power transmission lines are dangerous risks that may lead to situations like structural damage or power outages. The current techniques used for identifying ice have certain drawbacks, particularly when used in complex environments. This paper aims to detect lines on the top and bottom in PTLI with low illumination and complex backgrounds. The proposed method integrates multistage image processing techniques, including image enhancement, filtering, thresholding, object isolation, edge detection, and line identification. A binocular camera is used to capture images of PTLI. The effectiveness of the method is evaluated through a series of metrics, including accuracy, sensitivity, specificity, and precision, and compared with existing methods. It is observed that the proposed method significantly outperforms the existing methods of ice detection and thickness measurement. This paper uses average accuracy of detection and isolation of ice formations under various conditions at a percentage of 98.35, sensitivity at 91.63%, specificity at 99.42%, and precision of 96.03%. Furthermore, the accuracy of the ice thickness based on the thickness measurements is shown with a much smaller RMSE of 1.20 mm, MAE of 1.10 mm, and R-squared of 0.95. The proposed scheme for ice detection provides a more accurate and reliable method for monitoring ice formation on power transmission lines.

Multi-sensor imaging of winter buried lakes in the Greenland Ice Sheet

Recent studies have highlighted that meltwater in supraglacial lakes (SLs) can be buried during frozen season in the Greenland Ice Sheet (GrIS). Meltwater in buried lakes (BLs) can even persist through the winter, disturbing the englacial thermal regime and providing an important buffer against GrIS's contribution to sea-level rise. However, little is known about the inter-annual BL dynamics in the GrIS, and there is no quantitative statistic about the overall buried percentage. Here, we conduct a satellite-based study to automatically map the winter BLs over the GrIS during 2017–2022 using multi-source optical and synthetic aperture radar (SAR) images on the Google Earth Engine (GEE) platform. To eliminate the interferences from other weak microwave reflecting surfaces, summer SLs are first extracted from Landsat 8 and Sentinel-2 images to determine the potential BL searching areas on winter Sentinel-1 images. A self-adaptive thresholding algorithm is proposed to extract BLs within the dilated summer SLs using histogram-based morphological edge detectors. BLs extracted by the proposed method and visual interpretation show a substantial agreement with a precision of 0.82 and a Kappa coefficient of 0.70. On average, a total buried lake area of 182.27 km2 was observed each winter during the period 2017–2022. BLs were mainly distributed in the Center-West, South-West and North-East Basins, with the majority occurring at elevations between 800 and 1700 m. In 2019–2020, a sudden extension of BLs was observed over the GrIS, especially in the North-East Basin where abnormally high temperatures and surface runoff were recorded. In 2021–2022, a widespread distribution of BLs in the South-West Basin was observed after abnormal snowfall. Overall, about 13% of the GrIS summer SLs can persist through winter, suggesting the potential for meltwater hydrofracture in winter over large areas.

Combining the YOLOv4 Deep Learning Model with UAV Imagery Processing Technology in the Extraction and Quantization of Cracks in Bridges.

Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or over water, and is not easily accessible to a bridge inspector. Furthermore, poor lighting under bridges and a complex visual background can hinder inspectors in their identification and measurement of cracks. In this study, cracks on bridge surfaces were photographed using a UAV-mounted camera. A YOLOv4 deep learning model was used to train a model for identifying cracks; the model was then employed in object detection. To perform the quantitative crack test, the images with identified cracks were first converted to grayscale images and then to binary images the using local thresholding method. Next, the two edge detection methods, Canny and morphological edge detectors were applied to the binary images to extract the edges of the cracks and obtain two types of crack edge images. Then, two scale methods, the planar marker method, and the total station measurement method, were used to calculate the actual size of the crack edge image. The results indicated that the model had an accuracy of 92%, with width measurements as precise as 0.22 mm. The proposed approach can thus enable bridge inspections and obtain objective and quantitative data.

A method for detecting unbond defects of absorbing coatings

The nondestructive test (NDT) of microwave absorbing coatings plays a very important role in radar stealth and electromagnetic compatibility. In various NDT methods, microwave thermography has shown good potential. This method uses a thermal imager to measure the surface thermal profile of absorbing coatings which are heated by microwaves. In this paper, the application of microwave thermography in defect detection of absorbing coatings is presented. Specifically, the measurement principle of microwave thermography is studied by theoretical analysis and simulation, and the influence of thermal diffusion upon defect recognition has been discussed. On this basis, an improved morphological edge detection algorithm based on dual-threshold segmentation has been proposed to get defect features. Finally, research shows that this algorithm can reduce the influence of thermal diffusion in the detection of defect geometric features.

Morphological Edge Detection on CT Images

In this paper, a novel mathematical morphology edge detection algorithm is proposed to detect lungs CT medical image edge. It is a better method for edge information detecting and noise filtering than differential operation, which is sensitive to noise. And it is a better compromise method between noise smoothing and edge orientation, but the computation is more complex than general morphological edge detection algorithms

Evaluation of Geometric Occlusal Conditions Based on the Image Analysis of Dental Plaster Models

It has been proved that dimensions of the dental arch may change as a result of growth or orthodontic treatment. The most intense transformations can be observed at young age during an occurrence of a mixed dentition period. Based on diagnostic models in a form of plaster casts there is a possibility to measure following features: overbite, overjet, the curve of Spee, arch depth, arch width and Bolton’s ratios. Nowadays computer-aided tools can provide valuable information for dentists or orthodontists and simplify analysis, diagnosis and preparation of a treatment plan. Correct organization of the dental arch in the horizontal plane can be modelled as a parabola for a mandible and as a section of the ellipse for a jaw. A developed system based on the Python programming language provides automated analysis of plaster model images. Proposed methodology includes preprocessing of provided casts’ pictures and segmentation of dental arch using methods such as image morphological operations, edge detection and active contour algorithm. In this research there were also calculated semi-ellipse and parabola fitting functions of dental arches and measured the root mean squared error relative to the original curves. Preliminary results showed 1.29 mm RMSE level for correct occlusion and 2.39 mm in case of malocclusion. Proposed methods can be used for simplifying the analysis of dental arches’ shape and monitoring of changes during orthodontic treatment.

Morphological Edge Detection Algorithm of Colon Pathological Sections Based on Shearlet

This paper proposes an idea of combining the Meyer Shearlet and mathematical morphology to produce the edge detection of pathological sections of the colon. First, the method of constructing a class of sufficiently smooth sigmoid functions along with its relative scale function and Meyer wavelet function is provided in this paper. Based on those, in order to get the new Meyer wavelet function, we use the sigmoid function to construct more general scale functions. Next, taking sufficiently smooth sigmoid functions as examples, combining the relative Meyer wavelet and Shearlet to denoise some pathological sections of the colon leads a decent feedback. At last, this paper provides an improved algorithm for the edge detection of mathematical morphology with the background of multiscale and multistructure. This algorithm is used to carry out the edge detection of images after denoising yields a new edge detection algorithm that fuses the Meyer Shearlet denoising and mathematical morphology. According to the simulation results, the new algorithm is more beneficial for the observation and diagnosis of doctors since the edge noise of the colon pathological image detected by the new algorithm is smaller and provides more continuous and clear lines. Therefore, the fusion algorithm provided in this paper is an effective way to carry out the edge detection of an image.

An Image Recognition Framework for Oral Cancer Cells.

Oral squamous cell carcinoma (OSCC) is a common type of cancer of the oral cavity. Despite their great impact on mortality, sufficient screening techniques for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate recognition of OSCCs would lead to an improved curative result and a reduction in recurrence rates after surgical treatment. The introduction of image recognition technology into the doctor's diagnosis process can significantly improve cancer diagnosis, reduce individual differences, and effectively assist doctors in making the correct diagnosis of the disease. The objective of this study was to assess the precision and robustness of a deep learning-based method to automatically identify the extent of cancer on digitized oral images. We present a new method that employs different variants of convolutional neural network (CNN) for detecting cancer in oral cells. Our approach involves training the classifier on different images from the imageNet dataset and then independently validating on different cancer cells. The image is segmented using multiscale morphology methods to prepare for cell feature analysis and extraction. The method of morphological edge detection is used to more accurately extract the target, cell area, perimeter, and other multidimensional features followed by classification through CNN. For all five variants of CNN, namely, VGG16, VGG19, InceptionV3, InceptionResNetV2, and Xception, the train and value losses are less than 6%. Experimental results show that the method can be an effective tool for OSCC diagnosis.

Vector co‐occurrence morphological edge detection for colour image

Morphological edge detection is a principal component in pattern recognition and machine vision. Traditional edge detection operators only take pixel mutual into consideration. However, the edges are influenced not only by pixel mutual but also by the boundary characteristics. Here, the vector co-occurrence morphological edge detection operator is proposed, which takes the pixel and boundary information both into consideration. The vector co-occurrence algorithm is exploited to resist the influence of the noise points and detect the edges from the colour image rather than the grey image. And, we lead to define a precise definition of the manner of sorting high-dimensional data for the colour image. The experiment results always illustrate the advancement and practicability of our methods against the baseline method. In terms of experiments, the BSDS500 dataset is introduced to compare and analyse with other algorithms. Based on the standard benchmark index evaluation in the BSDS500 dataset, the ODS and AP of various algorithms are compared and analysed.

Patch-Based Weld Defect Segmentation and Classification Using Anisotropic Diffusion Image Enhancement Combined with Support-Vector Machine

Several factors such as low image quality, limited human eye resolution, and biased interpretation make the manual radiography testing defect detection erroneous. The aim of this paper is to design a semi-automatic but accurate feature extraction and classification framework, using radiography images of welded joints represented in the GDXray image database. We have trained our support-vector machine classifier with crack, porosity and lack of penetration as the three more frequent classes of radiography defects. The images are segmented after binarization followed by a two-stage image enhancement technique. The body of the two-stage method is made up of an anisotropic diffusion Gaussian filtering, morphological edge detection, and low-pass Gaussian filtering. A three-class support-vector machine based on One-vs.-All implementation of the binary support-vector machine is created, trained, and tested. The method also involves manual adjustments which makes it semi-automatic, and compared to other studies, it is proved to work best with GDXray, a freely available comprehensive radiography image database. The effect of incorporating different optimization algorithms for solving the inherent optimization problem in support-vector machine theory, and utilizing various image processing techniques on defect detection is studied. We show that the combination of image processing and support-vector machine would result in a better performance than the previous studies using the same database.

Pencil Drawing Generation Algorithm Based on GMED

The automatic generation of pencil drawings from natural images has been a hot spot in the field of nonphotorealistic rendering in recent years and has attracted the attention of many scholars. Aiming at the characteristics of single line drawings generated by the existing pencil drawing algorithm and stiff lines, this paper proposes a new edge generation Gradlent Morphological Edge Detector (GMED) algorithm, which uses the generated edge convolution to generate pencil line drawings, making stylized pictures more natural. First, the gradient map of the input image is extracted according to the feature of the tone rate of change around the line at the boundary. Second, to make up for the shortcomings of too thin and discontinuous lines in the gradient map, morphological operations are performed on the gradient map and convolved to generate a hand-drawn pencil line drawing. Finally, texture filling and tone mapping are performed, and the resulting line drawing is fused to obtain the final pencil drawing effect. Compared with the existing pencil drawing algorithm, the proposed GMED edge generation method is applied to the line drawing generation process to generate a hand-drawn style, fine and concise line drawing, and the texture simulation process is simple and efficient, combined with histogram matching. In the process, a more suitable GML method is used, so that the final pencil drawing not only has natural lines but also colors closer to the input image.

Gaussian Process Model of Surrounding Rock Classification Based on Digital Characterization of Rock Mass Structure and Its Application

As the important data basis of surrounding rock classification, rock mass structural information obtained by traditional image processing and feature extraction algorithms could not be quantitatively analyzed because of the uncertainty and geometric randomness of structural planes. In this paper, based on straight line detection, intelligent scissors, and morphological edge detection algorithms, the multiple interpretation system of rock mass image including linear bunching extraction, magnetic tracking extraction, and multiparameter characterization was researched and developed, and the actual distribution information and the related probability distribution model of structural planes could be obtained directly. On the basis of this, plenty of corresponding random rating-values meeting the probability distribution models of these evaluation indices were gained by Monte Carlo Simulation. The distribution probability affiliated with different rock mass grade was attained by inductive statistics, and the robust evaluation of surrounding rock classification could be carried out. Taking the robust results as learning samples, the response model of surrounding rock grade based on Gaussian process classification was established, making the evaluation of surrounding rock subclassification more rapid and robust.

Determining minimum number and placement of fault detectors in transmission network for fault location observability

This paper presents a new minimal placement strategy of fault detectors for fault-location on the transmission network. The proposed methodology includes two offline and online algorithms. First, the topology of a transmission network is analyzed, and the proposed offline algorithm is employed to determine the minimum number and location of detectors. Based on the arrival time of initial traveling wave-front acquired by the installed detectors, the online algorithm is introduced to identify the fault occurrence time as well as fault location. In order to extract the traveling wave from post-fault signals and construct the online algorithm, morphological edge detection (MED) filter is adopted. Extensive simulation studies based on the electromagnetic transients program and MATLAB Software have been carried out to evaluate the accuracy and reliability of the proposed method.

Study on fracture shape distribution characteristics and micromechanical properties of middling coal

In order to study the fracture shape distribution and micromechanical properties of middling coal containing fracture fillings, the fracture information of coal samples was obtained using the morphological edge detection method. The results showed that the number of Y-shaped fractures was much greater than that of the X-shaped fractures, and the number of single fractures accounted for a larger proportion of the surface fractures. The micromechanical characteristics of the middling coals were analyzed qualitatively based on the nanoindentation load–displacement curve by the surface of middling coal containing fracture fillings. The mechanical properties of the fracture filling zone were nonuniform owing to the presence of multiple minerals, and the crack breaks along the interface or the side of the matrix having poor mechanical properties under an applied force. The difference in mechanical properties at the mineral–mineral interfaces was determined through the nanoindentation test to judge the fracture mode.

Morphological edge detection and brain tumor segmentation in Magnetic Resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM) algorithm

The medical image processing has become indispensable with an increased demand for systematic and efficient detection of brain tumor in a short period of time. There are various techniques for medical image segmentation. Detecting a wide variety of brain images in terms of shape and intensity is a challenging and difficult task to bring out a reliable and authentic data for diagnosing brain tumor diseases. This paper presents an algorithm which combines Region of Interest (ROI), Region Growing and Morphological Operation (Dilation and Erosion). This method initially identifies the approximate Region Growing (RG). Region growing is a procedure that groups pixels into larger regions, which starts from the seed points. Region growing based techniques are better than the edge-based techniques in noisy images where edges are difficult to detect. The Morphological Edge Detection of the input image is done and the input image is reconstructed on the basis of dilation and erosion for the enhancement of the image. The proposed work is divided into preprocessing to reduce the noise, Fuzzy C-Means is used to Region growing, Morphological edge detection is to enhance the image. Then the morphological edge detection can be classified into two categories, one is dilation and another is Erosion. Finally apply Gaussian filter to get output. After that, Fuzzy C-Means clustering (FCM), followed by seeded region growing is applied to detect and segment the tumor from the brain MRI image.

Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing

The accuracy of defects detection for logistics packaging box is a critical factor to ensure the quality of goods under edge computing environment. Now, there are few works on this issue. This paper designs an image acquisition process system and then proposes a novel approach in addressing logistics packaging box defect detection (LPDD) on the basis of support vector machine (SVM). Firstly, this paper designs a new mean denoising template and Laplace sharpening template, which are more suitable for logistics packaging based on image preprocessing, image enhancement and other relevant technical theories. Then in the stage of noise removal, this paper proposes an improved morphological method and a gray morphological edge detection algorithm. The edge defect detection of a gray image is carried out by combining the above two methods. Hence, LPDD extracts the features of logistics packaging box by using scale-invariant feature transform (SIFT) algorithm and designs SVM classifiers to classify the logistics package defects. This paper uses a large number of samples to train, learn and test the designed SVM classifier. The simulation results show that the proposed LPDD method can accurately detect two common types of defects in logistics packaging boxes with higher accuracy and less computational costs, which meets the requirements of manufacturers on the classification and recognition of defects in machine vision detection system.

Automated Toll Booth using Morphological Edge Detection Algorithm

In order to reduce the traffic jam, save time and to diminish the money loss of 300 crores/year, an automated system is proposed; The intelligent traffic control management systems which explains the installation of automation in toll plazas, which is a step towards improving the monitoring of vehicles. The main purpose of this arrangement is to implement a system, which automatically identifies an approaching vehicle and records the vehicles number and time. If the vehicle belongs to an authorized person, the toll gate opens automatically and a predetermined amount is deducted from the user account, which leads to reduce the Traffic clogging at toll plazas. The vehicle number plate recognition is done only for authorized members. The proposed model consists of image digitization, edge detection, character detection and recognition with the payment transaction. The proposed method uses morphological edge detection , for the system to identify the character recognition and it uses template matching; it is done for license plates of 6 characters. The license plate character recognition system can be used in vehicle check -in and checkout monitoring system in hotels, malls and can be used to track vehicles on normal roads other than toll booths to avoid thefts.

A Study on Power Quality Transient Disturbance Location Method

Due to periodic variation of power signal and various disturbances present during sampling process, resulting background gradient affects the accuracy of disturbance detection. In order to solve this problem, this paper proposes a power quality transient disturbance location method based on morphological edge detection, and introduces a soft threshold quantitative evaluation method to improve its accuracy. Firstly, an evaluation method of filtering effect is proposed, and original signal is filtered by adaptively choosing size of structuring elements. Then, Top-Hat transform of morphological gradient is performed by flat structuring elements to suppress background gradient, and the location result is initially obtained. Finally, combined with soft threshold processing method, the location of power quality transient disturbance is realized. Simulation analysis of a series of power quality transient disturbances shows that this algorithm has accurate location results, strong capacity of resisting disturbance and good versatility.

Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator

Aiming at the conventional image edge detection algorithm, the first-order differential edge detection method is easy to lose the image details and the second-order differential edge detection method is more sensitive to noise. To deal with the problem, the Tikhonov regularization method is adopted to reconstruct the input coal-rock infrared images, so as to reduce the noise interference, and then, the reconstructed image is transformed by gray level. Finally, we consider the frequency characteristics and long memory properties of fractional differential, the classical first-order Sobel and second-order Laplacian edge detection algorithms are extended to fractional order pattern, and a new pattern of fractional order differential image edge detection is constructed to realize the coal-rock fracture edge features identification. The results show that, compared with integer order differential, the error rate and omission rate of fractional order differential algorithm are smaller, the quality factor is larger, and the execution time and memory footprint are smaller. From the point of view of location criteria and location accuracy, the fractional order differential algorithm is better than the integer order. In addition, the proposed method is compared with Canny algorithm, B-spline wavelet transform, and multidirection fuzzy morphological edge detection method, can detect more coal-rock fracture infrared image edge details, and is more robust to noise.

Integrating Morphological Edge Detection and Mutual Information for Nonrigid Registration of Medical Images.

Medical images are widely used within healthcare and medical research. There is an increased interest in precisely correlating information in these images through registration techniques for investigative and therapeutic purposes. This work proposes and evaluates an improved measure function for registration of carotid ultrasound and magnetic resonance images (MRI) taken at different times. To achieve this, a morphological edge detection operator has been designed to extract the vital edge information from images which is integrated with the Mutual Information (MI) to carry out the registration process. The improved performance of proposed registration measure function is demonstrated using four quality metrics: Correlation Coefficient (CC), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF) and Gradient Magnitude Similarity Deviation (GMSD). The qualitative validation has also been done through visual inspection of the registered image pairs by clinical radiologists. The experimental results showed that the proposed method outperformed the existing method (based on integrated MI and standard edge detection) for both ultrasound and MR images in terms of CC by about 4.67%, SSIM by 3.21%, VIF by 18.5%, and decreased GMSD by 37.01%. Whereas, in comparison to the standard MI based method, the proposed method has increased CC by 16.29%, SSIM by 16.13%, VIF by 52.56% and decreased GMSD by 66.06%, approximately. Thus, the proposed method improves the registration accuracy when the original images are corrupted by noise, have low intensity values or missing data.