Weak Edges Research Articles

An approximation of balanced score in neutrosophic graphs with weak edge weights

Neutrosophic concept is known undirected graph theory to involve with complex logistic networks, not clearly given and unpredictable real life situations, where fuzzy logic malfunctions to model. The transportation objective is to ship all logistic nodes in the network. The logistic network mostly experiences in stable condition, but for some edges found to be volatile. The weight of these erratic edges may vary at random (bridge-lifting/bascule, ad hoc accident on road, traffic condition) In this article, we propose an approximation algorithm for solving minimum spanning tree (MST) of an undirected neutrosophic graphs (UNG), in which the edge weights represent neutrosophic values. The approximation upon the balanced score calculation is introduced for all known configurations in alternative MST. As the result, we further compute decisive threshold value for the weak weights amid minimum cost pre-computation. If the threshold triggers then the proper MST can direct the decision and avoid post-computation. The proposed algorithm is also related to other existing approaches and a numerical analysis is presented.

Superpixel-based active contour model via a local similarity factor and saliency

The region-based active contour models could present difficulties because of undesired initial contour, noise distribution and image weak edges. In order to overcome the above problems, this paper proposes a superpixel-based via a local similarity factor and saliency (SLSFS). Firstly, the initial contour is generated by combining super-pixel and fuzzy c-means clustering. Secondly, the difference between local space and local intensity is used to improve the segmentation accuracy under noise. Finally, the weak edge information is protected by improved saliency detection. In addition, a gradient similarity constraint is used to remove the redundant regions. SLSFS model can generate adaptive initial contour around the target, and protect the weak edge information of the target on the premise of ensuring certain noise robustness. Experiments show that the average dice of SLSFS is 14% higher than that of the optimal comparison model and 19% on gray images.

Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network

The defects of solar cell component (SCC) will affect the service life and power generation efficiency. In this paper, the defect images of SCC were taken by the photoluminescence (PL) method and processed by an advanced lightweight convolutional neural network (CNN). Firstly, in order to solve the high pixel SCC image detection, each silicon wafer image was segmented based on local difference extremum of edge projection (LDEEP). Secondly, in order to detect the defects with small size or weak edges in the silicon wafer, an improved lightweight CNN model with deep backbone feature extraction network structure was proposed, as the enhancing feature fusion layer and the three-scale feature prediction layer; the model provided more feature detail. The final experimental results showed that the improved model achieves a good balance between the detection accuracy and detection speed, with the mean average precision (mAP) reaching 87.55%, which was 6.78% higher than the original algorithm. Moreover, the detection speed reached 40 frames per second (fps), which meets requirements of precision and real-time detection. The detection method can better complete the defect detection task of SCC, which lays the foundation for automatic detection of SCC defects.

Phase congruency-based filtering approach combined with a convolutional network for lung CT image analysis

ABSTRACT Computed tomography (CT) imaging was confirmed as one of the important evolving radiological features of disease diagnosis. In this work, we propose an image filtering method for CT images that fuses the spatial and the transform domain. Filters in the transform domain work well in restoring low-contrast details that are usually smoothed by spatial domain filters. The new filter is based on a modified anisotropic diffusion approach combined with the phase congruency (PC) feature. The PC feature is a scale-invariant compared to the classical gradient, where weak edges are usually omitted and undetected with gradient-based feature detectors. This feature is incorporated in the diffusion function to enhance image edges while eliminating noise and texture background. In a further step, a U-net convolutional network is used to segment the lung-infected area. Comparative tests and results show the efficiency of our method, which permits to enhance image features while removing noise perfectly.

Variability in higher order structure of noise added to weighted networks

The complex behavior of many real-world systems depends on a network of both strong and weak edges. Distinguishing between true weak edges and low-weight edges caused by noise is a common problem in data analysis, and solutions tend to either remove noise or study noise in the absence of data. In this work, we instead study how noise and data coexist, by examining the structure of noisy, weak edges that have been synthetically added to model networks. We find that the structure of low-weight, noisy edges varies according to the topology of the model network to which it is added, that at least three qualitative classes of noise structure emerge, and that these noisy edges can be used to classify the model networks. Our results demonstrate that noise does not present as a monolithic nuisance, but rather as a nuanced, topology-dependent, and even useful entity in characterizing higher-order network interactions.

A Study on Weak Edge Detection of COVID-19's CT Images Based on Histogram Equalization and Improved Canny Algorithm.

The coronavirus disease 2019 (COVID-19) is a substantial threat to people's lives and health due to its high infectivity and rapid spread. Computed tomography (CT) scan is one of the important auxiliary methods for the clinical diagnosis of COVID-19. However, CT image lesion edge is normally affected by pixels with uneven grayscale and isolated noise, which makes weak edge detection of the COVID-19 lesion more complicated. In order to solve this problem, an edge detection method is proposed, which combines the histogram equalization and the improved Canny algorithm. Specifically, the histogram equalization is applied to enhance image contrast. In the improved Canny algorithm, the median filter, instead of the Gaussian filter, is used to remove the isolated noise points. The K-means algorithm is applied to separate the image background and edge. And the Canny algorithm is improved continuously by combining the mathematical morphology and the maximum between class variance method (OTSU). On selecting four types of lesion images from COVID-CT date set, MSE, MAE, SNR, and the running time are applied to evaluate the performance of the proposed method. The average values of these evaluation indicators are 1.7322, 7.9010, 57.1241, and 5.4887, respectively. Compared with other three methods, these values indicate that the proposed method achieves better result. The experimental results prove that the proposed algorithm can effectively detect the weak edge of the lesion, which is helpful for the diagnosis of COVID-19.

Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation

Medical image segmentation is the most complex and important task in the field of medical image processing and analysis, as it is linked to disease diagnosis accuracy. However, due to the medical image's high complexity and noise, segmentation performance is limited. We propose a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network model architecture to address the aforementioned issues (QPFC-DA). It has mechanisms for channel and spatial edge attention, which guide the content and edge segmentation branches, respectively. The bi-directional long short-term memory network was added after the two content segmentation branches to better integrate multi-scale features and prevent the loss of important features. Furthermore, the fuzzy C-means algorithm guided by the quadratic polynomial can better distinguish the image's weak edge regions and has a degree of noise resistance, resulting in a membership matrix with less ambiguity and a more reliable segmentation result. We also conducted comparison and ablation experiments on three medical data sets. The experimental results show that this method is superior to several other well-known methods.

Nuchal Translucency Thickness Measurement in Fetal Ultrasound Images to Analyze Down Syndrome

Down syndrome (DS) is a chromosomal condition associated with intellectual disability and developmental delay in infants. The most salient pre-natal marker to identify DS during the early stages of gestation is the Nuchal translucency (NT) thickness. Accurate NT measurement from ultrasound (US) images becomes challenging due to the presence of speckle noise, weak edges and other artifacts. This paper proposes a semi-automated approach for early identification and calibration of NT thickness by estimating the distance between the edges of NT. A comparative study is performed on six traditional filters to determine the effective denoising filter for US datasets. This paper also presents an evaluation of two popular segmentation algorithms, the Active Contour and Chan-Vese-based segmentation. The study reveals that Wiener filter outperformed other filters based on the performance metrics and achieved peak signal noise ratio of 42.88 dB and mean squared error of −3.45. The prime feature NT region has been segmented effectively using enhanced double region-based active contour segmentation and obtained Dice Similarity Index (DSI- 84%) and Jaccard Index (76%). This technique has been validated by using Bland Altman’s plot to determine the correlation between clinical and proposed method.

Compression, bending, energy absorption properties, and failure modes of composite Kagome honeycomb sandwich structure reinforced by PMI foams

Owing to its high strength and modulus, convenient processing, and low cost, the composite interlocked Kagome honeycomb sandwich structure exhibits immense potential in several fields including naval and aeronautical applications. However, the main defect of this structure is the easy debonding between the face and core, and the weak edges greatly reduce the stiffness and strength. The innovation of this study is to fill the high-strength and high-modulus polymethacrylimide (PMI) foams into the composite interlocked Kagome honeycomb structure to form a new type of sandwich structure. It is compared with the isolated PMI foam and composite interlocked Kagome honeycomb sandwich structure through three-point bending (TPB) and edge compression (EC) tests. The compression, bending, energy absorption characteristics, and failure modes of the new structure are analyzed by experiments along with the finite element method (FEM). A numerical model is established for the equivalent in-plane stiffness of the combined sandwich structure. The results shows that the filled PMI foam greatly improves the overall strength, stiffness, and energy absorption with a small increase in the weight. Further, the limitation of the weak edges of the interlocked Kagome honeycomb structure is effectively surmounted. The proposed structure also shows significantly enhanced anti-debonding ability.

UTILIZATION OF MULTITEMPORAL LANDSAT DATA FOR COASTLINE CHANGE ANALYSIS USING CANNY EDGE DETECTOR

The development of remote sensing technology has experienced significant progress. This progress is marked by the availability of images of various resolutions with complete channels, open-source, and their development in various image classification techniques such as edge detection techniques. This study aims to examine changes in the coastline in Banmaleng Village, Gili Raja Island, Sumenep Regency using the Canny edge detection algorithm on multitemporal Landsat 7 and 8 imagery in 2005 - 2020. The edging process includes (a) minimize noise by applying a Gaussian filter, (b) determine the image intensity gradient, (c) apply non-maximum emphasis to eliminate spurious responses to objects, (d) define multiple thresholds for determining potential image edges, (e) Track edge hysteresis by pressing all other weak edges that are not connected to firm edges. Canny edge detection produces sharp edges to analyze coast line changes quite well. Every five years, the coastline of Banmaleng Village experienced shifting from 0 , 4 to 2 , 3 meters in several areas. Keywords : Edge Detection, Landsat, Coastline, Canny Algorithm, Gili Raja

A classifier design for micro bubble generators based on deep learning technique

This paper aims to study the sizes of bubbles generated by the micro-bubble generator device (MBGs) in the water because of its impact on the dissolved oxygen percentage in the water and we find this in aquaculture where oxygen is important for marine life and in many applications. Where we have designed a classifier for micro-bubbles of different sizes and the effectiveness of the device has been proven in generating bubbles whose size ranges from 20 to 50 and calculating their numbers in the image by converting the image from (RGB) to (HSV), it has proven its effectiveness in maintaining lighting and neglecting color information Which is not important in identifying bubbles in the image, as well as increasing the processing speed, and then finding edges in the image, using canny edge detection, which has proven its great effectiveness in finding weak and strong edges. Where we then used the (Circular Hough Transform) algorithm to identify the tiny bubbles in the image, calculate their average diameter and numbers in the image, and find the overlapping bubbles between them by using (Two Thresholds) one for the edge and the other for the center of the bubble and then making (Segmentation) for these bubbles and inserting them on (Gaussian Normal Distribution) Do not neglect the dark bubbles, because in most cases they are unreal bubbles and may be distortions in the image or reflections in the lighting. And then make (zero paddings) for the extracted images to make them equal in size and avoid the distortion that will happen in the image if another method is used to make all bubbles of equal dimensions to prepare them for classification. And then we calculated the (mean square error) of the resulting bubble images to avoid repeating the same bubble in (datasets). The efficiency of the classifier was calculated by using deep learning technology. Where we built a (Convolution Neural Network) consisting of (15) layers and then we trained these layers on the images resulting from the (CHT), which are individual bubbles, each of them classified according to size. The dataset was divided into two parts, 70% of which is for education and the remaining 30% is for (validation), and the test percentage is 100%.

Investigating the Short-Circuit Problem Using the Planarity Index of Complex q-Rung Orthopair Fuzzy Planar Graphs

Planar graphs play an effective role in many practical applications where the crossing of edges becomes problematic. This paper aims to investigate the complex q-rung orthopair fuzzy (CQROF) planar graphs (CQROFPGs). In a CQROFPG, the nodes and edges are based on complex QROF information that represents the uncertain knowledge in the range of unit circles in terms of complex numbers. The motivation in discussing such a topic is the wide flexibility of QROF information in the expression of uncertain knowledge compared to intuitionistic and Pythagorean fuzzy settings. We discussed the complex QROF graphs (CQROFGs), complex QROF multigraphs (CQROFMGs), and related terms followed by examples. Furthermore, the notion of strength and planarity index (PI) of the CQROFPGs is defined and exemplified followed by a study of strong and weak edges. We further defined the notion of complex QROF face (CQROFF) and complex QROF dual graph (CQROFDG) and exemplified these concepts. A study of isomorphism, coweak and weak isomorphism, is set up, and some results relating to the CQROFPG and isomorphisms are explored using examples. Furthermore, the problem of short circuits that results due to crossing is discussed because of the proposed study where an algorithm based on complex QROF (CQROF) information is presented for reducing the crossing in networks. Some advantages of the projected study over the previous study are observed, and some future study is predicted.

Formation of graphene nanostructures using laser induced vaporization of entrapped water

Facile construction of graphene nanostructures are potentially important for fundamental studies and various applications owing to its transparency and mechanical strength. Here we found that focused laser irradiation of a graphene/entrapped water/hydrophobic substrate leads to vaporization of entrapped water and consequent formation of graphene nanostructures. Graphene mechanically exfoliated on a hydrophobicized Si substrate was served as a transformable and impermeable nanocontainer in which water anisotropically and slowly diffuses from the weak edges into the van der Waals (vdW)-coupled interstitial volume between graphene and the substrate. Time-lapsed Raman mappings show that water entrapment promotes progressive lowering of the frequencies of the G and 2D bands of graphene and exhibits slower diffusion owing to vdW decoupling of substrate-induced doping and biaxial strain of graphene with hydrophobic substrate. Moreover, vaporized entrapped water promotes nanostructures by graphene sliding and bulging actions. This methodology represents viable approach to produce nanostructures from two dimensional materials.

Edge and neighborhood guidance network for 2D medical image segmentation

Accurate automatic image segmentation is important in medical image analysis. A perfect segmentation using fully convolutional network (FCN) means an accurate classification of each pixel. However, it is still a great challenge to accurately differentiate edge pixels from neighborhood pixels in weak edge regions. Many previous segmentation methods have focused on edge information to mitigate weak edge problems, but the more important neighborhood information is undervalued. To tackle this problem, in this paper, we propose a novel yet effective Edge and Neighborhood Guidance Network (ENGNet). Specifically, instead of just utilizing the edge information as the shape constraints, the edge and neighborhood guidance (ENG) module is designed to exploit the edge information and fine-grained neighborhood spatial information simultaneously, so as to improve the ability of network to classify edge pixels and neighborhood pixels in weak edge regions. Moreover, the ENG modules are adopted in different scales to learn sufficient feature representations of edge and neighborhood. To extract complementary features more effectively in channel dimension, we also design a multi-scale adaptive selection (MAS) module at channel-wise to extract multi-scale context information and adaptively fuse different-scale features. Two 2D public segmentation datasets including skin lesion dataset and endoscopic polyp dataset are used to evaluate the performance of the proposed ENGNet. Experimental results demonstrated that by exploiting edge information and neighborhood spatial information in different scales simultaneously, the proposed ENGNet can effectively alleviate the misclassification in weak edge regions and achieve better performance than other state-of-the-art methods.

WeBox: locating small objects from weak edges

WeBox: locating small objects from weak edges

Technical Analysis of the Station Equipment IOT Management System

Traditional BAS systems in subway stations have problems such as inflexible deployment, high implementation costs, weak edge computing capabilities, and low integration, which are not conducive to the realization of convenience and intelligence. In response to the problems of the traditional BAS system, Guangzhou Railway Institute designed and applied a brand-new station equipment IOT management system. This article first analyzes the overall technology of the new system. In addition, the article also shows the hardware and software and core applications of the new system. Finally, the article describes the characteristics and main benefits of the new system.

Content-Sensitive Superpixel Generation for SAR Images With Edge Penalty and Contraction–Expansion Search Strategy

In this article, we present a content-sensitive superpixel generation method with edge penalty and the contraction–expansion search strategy (EPCES) for synthetic aperture radar (SAR) images. Specifically, the edge information can be obtained by our previously proposed ratio-based edge detector with recurrent guidance filter, which has been proven to be robust to speckle noise and capable of detecting weak edges in low-contrast areas. The content-sensitive superpixel seeds’ initialization method is proposed with respect to the heterogeneous state of the SAR imagery, benefiting from which EPCES can generate an exact number of superpixels set by the user and the fine details can be preserved well. In EPCES, a new dissimilarity with edge penalty is defined to generate the superpixels with better edge adherence. Rather than adopting the conventional clustering method based on local <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means, we propose the contraction–expansion search strategy (CES), which explicitly utilizes the continuity information contained in neighboring pixels and enforces the connectivity of the superpixel without any postprocessing step. With the aid of the CES, our proposed method can attain superpixels with low computational cost and high edge adherence. Experimental results on both synthetic and real-world SAR images verify that the proposed method consistently performs favorably against several state-of-the-art methods in terms of both quality and efficiency.

Edge-preserving smoothing algorithm based on &amp;lt;italic&amp;gt;l&amp;lt;/italic&amp;gt;&amp;lt;sub&amp;gt;0&amp;lt;/sub&amp;gt; gradient minimization of image-patch

Image smoothing algorithm based on l₀ gradient minimization can smooth details and textures of image while preserving edges. Since the algorithm uses image gradient to determine the smoothed component, the region with smaller image gradients (weak edge) can be smoothed. However, the region with larger image gradients (strong edge) will be preserved. In order to overcome this drawback, we propose an image-patch based l₀ gradient minimization image smoothing algorithm (IP-<italic>l</italic>₀). Instead of globally smoothing the input image, our algorithm smoothed each image patch first and then combined the smoothed patches together to obtain the final smoothed image. The weight parameters in the objective function used to smooth each image patch are dynamically changed according to the local statistics of image patch, so that the patches containing strong textures will be smoothed with greater force, and vice versa. The experimental results show that compared to the original l₀ gradient minimization algorithm and several other state-of-the-art edge-preserving image smoothing algorithms including the local Laplacian filter based algorithm, the relative total variation based algorithm, the tree filter based algorithm, and two kinds of deep-learning based smoothing algorithms, the proposed algorithm can effectively smooth strong textures and well preserve weak edges or structures, and the ability of edge preservation and texture smoothing is better than other algorithms.

Savitzky–Golay Filtering-Based Fusion of Multiple Exposure Images for High Dynamic Range Imaging

The problem of compositing multiple exposure images has attracted lots of researchers, over the past years. It all began with the problem of High Dynamic Range (HDR) imaging, for capturing scenes with vast differences in their dynamic range. Fine details in all the areas in these scenes cannot be captured with one single exposure setting of the camera aperture. This leads to multiple exposure images with each image containing accurate representation of different regions dimly lit, well lit and brightly lit in the scenes. One can make a combined HDR image out of these multiple exposure shots. This combination of multiple exposure shots leads to an image of a higher dynamic range in a different image format which cannot be represented in the traditional Low Dynamic Range (LDR) formats. Moreover HDR images cannot be displayed in traditional display devices suitable for LDR. So these images have to undergo a process called as tone mapping for further converting them to be suitable enough to be represented on usual LDR displays. An approach based on Savitzky–Golay parametric filtering which preserves edges, is proposed which uses filtered multiple exposure images to generate the alpha matte coefficients required for fusing the input multiple exposure set. The coefficients generated in the proposed approach helps in retaining the weak edges and the fine textures which are lost as a result of the under and over exposures. The proposed approach is similar in nature to the bilateral filter-based compositing approach for multiple exposure images in the literature but it is novel, in exploring the possibility of compositing using a parametric filtering approach. The proposed approach performs the fusion in the LDR domain and the fused output can also be displayed using standard LDR image formats on standard LDR displays. A brief comparison of the results generated by the proposed method and various other approaches, including the traditional exposure fusion, tone mapping-based techniques and bilateral filter-based approach is presented where in the proposed method compares well and fares better in majority of the test cases.

Offshore Heavy Oil Polymerflooding Pilot Reveals Alternative Paths

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30277, “Twelve-Year Field Applications of Offshore Heavy Oil Polymerflooding From Continuous Injection to Alternating Injection of Polymer and Water,” by Guangming Pan, Lei Zhang, and Jianting Huang, CNOOC, et al., prepared for the 2020 Offshore Technology Conference Asia, originally scheduled to be held in Kuala Lumpur, 2-6 November. The paper has not been peer reviewed. Copyright 2020 Offshore Technology Conference. Reproduced by permission. Polymerflooding has been considered a suitable method for reservoirs with viscosities up to 150 mPa·s. The authors of the complete paper verify that alternating injection of polymer and water in the Bohai Bay of China proved effective and economical for heavy oil fields, even offshore. This polymerflooding pilot of initially continuous, and then alternating, injection can provide a useful technical reference for similar reservoirs. Introduction Heavy oil reserves are abundant in the Bohai oil field of China. The development of the field has proved that the field with lower viscosity (less than 350 mPa·s) can be developed effectively by water-flooding, while the unconventional heavy oil reservoir with high viscosity has not formed a mature development mode. To better use interwell reserves, a pilot polymerflooding test has been conducted in the NN field since 2008. The cumulative production of nine wells in the surrounding area reached 10.80×104 m3, which confirmed that polymer fluid injection had a good displacement effect on unconventional high-viscosity crude oil. However, with the extension of continuous injection time, the pilot test area faced various problems. In order to explore the applicability of polymerflooding technology used in offshore unconventional heavy oil fields, the polymer-injection mode was studied on the basis of laboratory experimental data and field practice, and the polymer/water alternating injection mode was analyzed. Experimental Continuous Polymerflooding. Experimental Equipment and Materials. The experimental device is composed of a driving system, an experimental model, a pressure-measurement system, a produced-liquid-collection system, and a temperature-control system. According to the distribution of reservoir physical properties in the NN field, a parallel double-tube displacement experiment with a permeability ratio of 5 was designed. The experimental cores are artificial, with a tube length of 30 cm and an inner diameter of 2.54 cm. The low-permeability tube has 1624×10-3 µm2 permeability, and the high-permeability tube has 8488×10-3 µm2 permeability. The experimental temperature is 55°C, which is consistent with the formation temperature of the NN field. The polymer is partially hydrolyzed polyacrylamide. Experimental Procedure. The experimental process includes vacuum pumping, saturating formation water, obtaining core pore volume, saturating simulated oil, calculating oil saturation water drive to a specified water cut, continuously injecting polymer solution, and measuring data. The experimental injection rate is 0.2 mL/min, and the multiple of injected pore volumes (PV) is 0.6 PV. The NN field has weak edge water, and the water cut of the well group was 60 to 90% when polymerflooding was performed. Therefore, the design scheme mainly includes waterflooding and polymerflooding stages. The polymer- injection concentration was 3000 mg/L, and the injection mode is continuous, consistent with the field test.