Weak Edges Research Articles

Influence of Al3+, Ga3+, and In3+ substitution on the structural, microstructural and dielectric characteristics of ZnS nanoparticles

Trivalent metal cations modified polycrystalline ZnS nanoparticles, Zn0.95X0.05S (X = Al3+, Ga3+, & In3+) NPs, were synthesized using co-precipitation technique and their physicochemical investigations were carried out. Structural analysis revealed that all the compositions display cubic crystallinity having Fm3‾m space group as evident from Rietveld refinement technique. The average crystallite size evaluated using Williamson-Hall plot was found to be 4.75 nm, 5.17 nm, and 5.76 nm for Zn0.95Al0.05S NPs, Zn0.95Ga0.05S NPs, and Zn0.95In0.05S NPs respectively. Microstructural studies depicted the nanoscale formation of nanoparticles with uniform grain distribution where the average grain size was observed between 38.2 nm and 45.4 nm. FT-IR spectra analysis confirmed the formation of Zn0.95Al0.05S, Zn0.95Ga0.05S, and Zn0.95In0.05S NPs with high frequency stretching vibrations about 465 cm−1 and 654 cm−1, belonging to the weak and strong edge of elemental regions. The value of the dielectric constant in the lower frequency region was found to be maximum (27.2) for Zn0.95In0.05S NPs and least (17.8) for Zn0.95Al0.05S NPs. It was observed that ac conductivity increases sharply with the increasing frequency of the applied electric field. The measured values of ac conductivity at 1 kHz for Zn0.95Al0.05S NPs, Zn0.95Ga0.05S NPs, and Zn0.95In0.05S NPs were found to be 5.06 × 10−7 Ω−1cm−1, 1.24 × 10−6 Ω−1cm−1, and 1.28 × 10−6 Ω−1cm−1, respectively.

Wiener index of a directed rough fuzzy graph and application to human trafficking

In this paper, we introduced Wiener index ( WI ) and average Wiener index ( AWI ) of directed rough fuzzy graph (DRFG). WI is the most extensively used index in graph theory. This index is based on the geodesic distance between two vertices. If there is no directed path from vertex x to vertex y in DRFG, we assume that the weight of geodesic from vertex x to vertex y is zero. In this paper, we investigate the connection between WI and connectivity index ( CI ), which is one of the most prominent index, by presenting several examples and results. We introduced the concept of complete directed rough fuzzy graph (CDRFG) along with some useful results like CDRFG have no weak edges. We also compute the WI for CDRFG. Moreover, we discussed three types of vertices: Wiener enhancing vertex (WEV), Wiener reducing vertex (WRV), and Wiener neutral vertex (WNV). The proposed study of DRFG is suitable for modeling uncertainties and unclear data information in the real life circumstances. In the end, we proposed an application of the WI in the human trafficking network. We also presented a detailed comparative analysis and comparison table by comparing our result for both CI and WI for the same human trafficking network.

Modality Specific CBAM-VGGNet Model for the Classification of Breast Histopathology Images via Transfer Learning

Histopathology images are very distinctive, and one image may contain thousands of objects. Transferring features from natural images to histopathology images may not provide impressive outcomes. In this study, we have proposed a novel modality-specific CBAM-VGGNet model for classifying H and E stained breast histopathology images. Instead of using pre-trained models on ImageNet, we have trained VGG16 and VGG19 models on the same domain cancerous histopathology datasets which are then used as fixed feature extractors. We have added GAP layer and Convolutional block attention module (CBAM) after the first convolutional layer of convolutional blocks. CBAM is an effective module for neural networks to focus on relevant features. We have implemented the VGG16 and VGG19 in a novel way following the configuration of state-of-the-art models with our own concatenated layers. The addition of the GAP layer in VGGNet has reduced the number of parameters and hence requiring less computational power. Both models are ensembled using the averaging ensemble technique. Features are extracted from the final ensembled model then passed to the feed-forward neural network. A hybrid pre-processing technique is proposed that first use median filter and afterwards contrast limited adaptive histogram equalization (CLAHE). Median filter is utilized to remove the highly significant noise and is directly related to image quality. CLAHE improve the local contrast present in an image and boost the weak boundary edges in each image pixel. The proposed CBAM ensemble model has outperformed state-of-the-art models with an accuracy of 98.96% and 97.95% F1-score on 400X data of BreakHis dataset.

Medical Image Segmentation with active contour and optimization Techniques: Survey

The principal aim is to improve medical diagnosis through segmented images. Thus, medical image segmentation become one of the key technologies in computer-aided diagnosis. Active contours, or snakes, have been widely used for image segmentation purposes. However, high noise sensitivity and poor performance over weak edges are the most acute issues that hinder the segmentation accuracy of these curves, particularly in medical images. To overcome these issues, a novel external force that integrates gradient vector flow (GVF) field forces and the traditional snake function is proposed in this research. In addition, a novel technique is applied to limit the boundary of the initial contour by set four initial points around the medical issue and then connecting them by polynomial curves. Moreover, the positive effect of Particle Swarm Optimization (PSO) on calculating the final active contour area and its percentage to the entire image area is proved in this work.

Gamma-corrected, SNR-enhanced and sharpness-projection-based reconstruction approach for particle holography

ABSTRACT We report a Gamma-corrected, SNR-enhanced and sharpness-projection-based reconstruction approach for digital particle holography. In this approach, we mitigate the artefacts from off-focal planes by capturing three holograms with various illumination angles, and extract particle information by applying image processing to a high-quality maximum – sharpness-projection (MSP) image. This approach reinforces weak edges using a Gamma correction procedure, and segments particles from the MSP image by using a new hybrid threshold, which comprises both local and global features. Experimental results for nonspherical particles show that the reported approach can provide a high-quality MSP image, being less likely than the conventional method to be fooled by noise and more likely to retain particle features. The hybrid threshold helps in retaining fine features, separating neighbouring particles and differentiating particles from the background. For the large particles (>20 pixels), the diameter error is generally less than 10% while using the proposed automatic hybrid thresholding technique.

Implementation of the Canny Edge Detector Using a Spiking Neural Network

Edge detectors are widely used in computer vision applications to locate sharp intensity changes and find object boundaries in an image. The Canny edge detector is the most popular edge detector, and it uses a multi-step process, including the first step of noise reduction using a Gaussian kernel and a final step to remove the weak edges by the hysteresis threshold. In this work, a spike-based computing algorithm is presented as a neuromorphic analogue of the Canny edge detector, where the five steps of the conventional algorithm are processed using spikes. A spiking neural network layer consisting of a simplified version of a conductance-based Hodgkin–Huxley neuron as a building block is used to calculate the gradients. The effectiveness of the spiking neural-network-based algorithm is demonstrated on a variety of images, showing its successful adaptation of the principle of the Canny edge detector. These results demonstrate that the proposed algorithm performs as a complete spike domain implementation of the Canny edge detector.

Adaptive weighted curvature-based active contour for ultrasonic and 3T/5T MR image segmentation

Image segmentation is a complex and core technique for disease diagnosis or image-guided surgery in the medical image domain. However, low-quality images, such as images with weak edges and intensity inhomogeneities, may bring considerable challenges for radiologists. In this paper, we propose an adaptive weighted curvature-based active contour model by coupling heat kernel convolution and adaptively weighted high-order total variation for medical image segmentation to improve diagnosis effectiveness. To reduce the computational complexity, the heat kernel convolution operation is applied to approximate the perimeter of a segmentation curve. In addition, the weighted parameter included in the high-order total variation term can be automatically evaluated based on an adaptive input image to emphasize local structures and increase segmentation accuracy. Since the proposed method is a smoothing optimization model, the alternating direction method of multipliers is introduced to translate the original problems into several easily solvable subproblems. The numerical experimental results on ultrasonic and 3T/5T MRI datasets demonstrate that the proposed model is quite efficient and robust compared with several traditional segmentation methods.

Adaptive Active Contour Model for Brain Tumor Segmentation

For accurately diagnosing the severity of brain tumors in MRI images, Glioma segmentation is a significant step. The Glioma segmentation is due to noise and weak edges of organs in medical images. The geodesic active contour model (GACM) is a standard method for the segmentation of complex organ structures based on edge maps. The GACM performs poorly due to this noise and weak edges. So, the authors propose a method that uses adaptive kernels instead of a constant kernel for creating strong edge maps for GACM. The kernels used in phase congruency are Log Gabor kernels, which resemble similar anisotropic properties like Gabor kernels. They have replaced these with adaptive kernels. This adaptive kernel-based phase congruency provides a robust edge map to be used in GACM. Experimentation shows that when compared with state-of-the-art edge detection techniques, adaptive kernels enhance the weak as well as strong edges and improve the overall performance. Moreover, the proposed methodology substantially requires fewer parameters compared to existing ACM methods.

Sketch2Photo: Synthesizing photo-realistic images from sketches via global contexts

Sketch-to-image synthesis aims to generate realistic images that match the input sketches or edge maps exactly. Most known sketch-to-image synthesis methods use various generative adversarial networks (GANs) that are trained with numerous pairs of sketches and real images. Because of the convolution locality, the low-level layers of the generators in these GANs lack global perception ability, causing feature maps derived from them easily to overlook global cues. Since the global receptive field is crucial for acquiring the non-local structures and features of sketches, the absence of global contexts will impact the generation of high-quality images. Some recent models turn to self-attention to construct global dependencies. However, they are not viable for large feature maps for the quadratic computational complexity concerning the size of feature maps. To address these problems, in this work, we propose Sketch2Photo — a new image synthesis approach that can capture global contexts as well as local features to generate photo-realistic images from weak or partial sketches or edge maps. We employ fast Fourier convolution (FFC) residual blocks to create global receptive fields in the bottom layers of the network and incorporate Swin Transformer block (STB) units to obtain long-range global contexts for large-size feature maps efficiently. We also present an improved spatial attention pooling (ISAP) module to relax the strict alignment requirements between incomplete sketches and generated images. Quantitative and qualitative experiments on multiple public datasets demonstrate the superiority of the proposed approach over many other sketch-to-image synthesis methods. The project code is available at https://github.com/hengliusky/Skecth2Photo.

Trace Concealment Histogram-Shifting-Based Reversible Data Hiding with Improved Skipping Embedding and High-Precision Edge Predictor (ChinaMFS 2022)

Reversible data hiding (RDH) is a special class of steganography, in which the cover image can be perfectly recovered upon the extraction of the secret data. However, most image-based RDH schemes focus on improving capacity–distortion performance. In this paper, we propose a novel RDH scheme which not only effectively conceals the traces left by HS but also improves capacity–distortion performance. First, high-precision edge predictor LS-ET (Least Square predictor with Edge Type) is proposed, and the predictor divides pixels into five types, i.e., weak edge, horizontal edge, vertical edge, positive diagonal edge, and negative diagonal edge. Different types of target pixels utilize different training pixels with stronger local consistency to improve accuracy. Then, a novel prediction-based histogram-shifting (HS) framework is designed to conceal embedding traces in the stego images. Finally, we improve both the data-coding method and the skipping embedding strategy to improve the image quality. Experimental results demonstrate that the capacity–distortion performance of the proposed scheme outperforms the other trace concealment schemes and is comparable to the state-of-the-art schemes utilizing sorting technique, multiple histogram modification, and excellent LS-based predictors. Moreover, it can conceal the embedding traces left by the traditional HS schemes to a certain extent, reducing the risk of being steganalyzed.

Evolution of edge turbulent transport induced by L-mode detachment in the HL-2A tokamak

This paper presents the characteristics of L-mode detachment, together with the behavior of edge turbulent transport and plasma confinement on the HL-2A tokamak. Partially detached and pronounced detached states have been achieved in L-mode plasma. Stored energy was maintained before and after detachment. Edge turbulence and its transport have increased obviously in the partially detached state. In the pronounced detached state, redistribution of the density and temperature profiles due to detachment leads to low amplitude of electron temperature and pressure, as well as very weak edge turbulence and transport. Despite strong plasma radiation in the pronounced detached state, reduced edge turbulent transport contributes to maintaining stored energy in detached L-mode plasma in HL-2A. Different detachment states play an important role in the redistribution of density and temperature profiles, which requires further study.

Adaptive Image Steganography Using Fuzzy Enhancement and Grey Wolf Optimizer

Adaptive imagesteganography embeds secret messages into areas of cover images with complex features, including rich edges and complex textures. In this article, an adaptive image steganography technique based on the edge and complex texture areas of images is proposed, by comprehensively considering three rules in the design of image steganography. First, the embedding area is composed of the edge and complex texture areas of images, according to the complexity-first rule. Edge detection is realized by an improved fuzzy enhancement function, optimized by the grey wolf optimizer to detect both the weak and strong edges. Second, the minimum average classification error rate is used to assess the choice of the complex texture areas. Third, under the spreading rule, two different average filters and one KerBohme filter are used to design the cost function in the embedding areas. Finally, confidential information is adaptively embedded through syndrome-trellis codes. Experimental results show that the proposed algorithm outperforms seven classical adaptive image steganography algorithms on two steganalytic feature sets. The performance improvement is particularly significant when the payload is large.

Weak edge triangle free detour number of a graph

For any two vertices u and v in a connected graph G = ( V , E ), a u − v path P is called a u − v triangle free path if no three vertices of P induce a triangle. The triangle free detour distance D △ f ( u , v ) is the length of a longest u − v triangle free path in G . A u − v path of length D △ f ( u , v ) is called a u − v triangle free detour . A set S ⊆ V is called a weak edge triangle free detour set of G if every edge of G has both ends in S or it lies on a triangle free detour joining a pair of vertices of S . The weak edge triangle free detour number w d n △ f ( G ) of G is the minimum order of its weak edge triangle free detour sets and any weak edge triangle free detour set of order w d n △ f ( G ) is a weak edge triangle free detour basis of G . Certain properties of these concepts are studied. The weak edge triangle free detour numbers of certain classes of graphs are determined. Its relationship with the triangle free detour diameter is discussed and it is proved that for any three positive integers a , b and n of integers with 3 ≤ b ≤ n − a + 1 and a ≥ 4, there exists a connected graph G of order n with triangle free detour diameter D △ f = a and w d n △ f ( G )= b . It is also proved that for any three positive integers a , b and c with 3 ≤ a ≤ b and c ≥ b + 2, there exists a connected graph G such that R △ f = a , D △ f = b and w d n △ f ( G )= c .

Research on surface defect detection of glass wafer based on visual inspection

Glass wafer (GW) is used in a variety of integrated circuit (IC) packaging applications and as substrates to provide better performance and cost-effectiveness. Glass wafer (GW) protects the IC from impact and corrosion while maintaining the contract pins and leads that connect it to the external circuit. In the process of technology or production, this kind of structure is continuously working for a long time. Due to the inherent defects such as bubbles generation, starvation and the structure is often suffered from acid, alkali, moisture, vibration and other factors, which makes its internal structure gradually form corrosion stains. These defects have posed a serious threat to the quality and performance of equipment. Based on these disadvantages, this paper analyzes the defect detection principle of Glass wafer, then designed a method of determine the defect region. The edge signal processing method of visual image defects is studied, the edge detection and defect feature extraction model is established, and the principle of defining strong and weak edges is clarified. A defect feature classifier based on multi-layer perceptron (MLP) is created, and a segmentation algorithm of the classifier is implemented. Finally, a multi-channel image detection experimental platform is built to verify the typical unit structure. The experimental results show that the rate of defective features recognition is high, the detection rate is fast, and it has practical application value in engineering. The research of this recognition method has positive theoretical significance for accurately evaluating the overall reliability of GW structure and ensuring the safe operation of equipment.

Complex networks for community detection of basketball players

In this paper a weighted complex network is used to detect communities of basketball players on the basis of their performances. A sparsification procedure to remove weak edges is also applied. In our proposal, at each removal of an edge the best community structure of the “giant component” is calculated, maximizing the modularity as a measure of compactness within communities and separation among communities. The “sparsification transition” is confirmed by the normalized mutual information. In this way, not only the best distribution of nodes into communities is found, but also the ideal number of communities as well. An application to community detection of basketball players for the NBA regular season 2020–2021 is presented. The proposed methodology allows a data driven decision making process in basketball.

A robust edge detection technique based on Matching Pursuit algorithm for natural and medical images

Edge detection has been used widely in image processing, image recognition, and computer vision. Several edge detection techniques have been suggested over time; however, each has a lot of disadvantages. Our method has some steps. At first, the image should be converted into a grayscale image. A 3*3pattern grid is used for producing a discrete vector for any pixel in the present study. Secondly, the length of the pixel vector is increased to linearize and expand. Thirdly, the Matching Pursuit algorithm is applied to the image, and the residual part of the Matching Pursuit algorithm is extracted. Finally, two Hysteresis thresholds have been used for detecting edge pixels. The edge detection problem has been converted into a signal processing problem, so it will be less time-consuming. Moreover, the proposed algorithm is insensitive to noise, and this method can detect weak edge pixels. Also, the performance of the proposed algorithm is compared with three known edge detection algorithms, SOBEL, Laplacian, Canny, and CASENet and the results show that we have a high value of performance parameters such as PR, PSNR, and FOM compared to other algorithms. Furthermore, it is very an effective and reliable method to diagnose and treat diseases in medical images.

Efficient Echocardiographic Image Segmentation

In this paper, we propose an improved region-based active contour method based on the development of a novel signed pressure force (SPF) function. To obtain the required boundary, the method is applied to the echocardiographic images. Ultrasound image segmentation is particularly challenging due to speckle noise, low contrast, and intensity inhomogeneity. Because of these factors, segmenting echocardiographic images is a difficult task. All of these issues are addressed by the proposed model, which detects the true boundary without any noise. The proposed model is more robust, effective, and accurate when applied to images with weak edges and inhomogeneous intensity.

Local and Context-Attention Adaptive LCA-Net for Thyroid Nodule Segmentation in Ultrasound Images.

The thyroid nodule segmentation of ultrasound images is a critical step for the early diagnosis of thyroid cancers in clinics. Due to the weak edge of ultrasound images and the complexity of thyroid tissue structure, it is still challenging to accurately segment the delicate contour of thyroid nodules. A local and context-attention adaptive network (LCA-Net) for thyroid nodule segmentation is proposed to address these shortcomings, which leverages both local feature information from convolution neural networks and global context information from transformers. Firstly, since most existing thyroid nodule segmentation models are skilled at local detail features and lose some context information, we propose a transformers-based context-attention module to capture more global associative information for the network and perceive the edge information of the nodule contour. Secondly, a backbone module with , convolutions and the activation function Mish is designed, which enlarges the receptive field and extracts more feature details. Furthermore, a nodule adaptive convolution (NAC) module is introduced to adaptively deal with thyroid nodules of different sizes and positions, thereby improving the generalization performance of the model. Simultaneously, an optimized loss function is proposed to solve the pixels class imbalance problem in segmentation. The proposed LCA-Net, validated on the public TN-SCUI2020 and TN3K datasets, achieves Dice scores of 90.26% and 82.08% and PA scores of 98.87% and 96.97%, respectively, which outperforms other state-of-the-art thyroid nodule segmentation models. This paper demonstrates the superiority of the proposed LCA-Net for thyroid nodule segmentation, which possesses strong generalization performance and promising segmentation accuracy. Consequently, the proposed model has wide application prospects for thyroid nodule diagnosis in clinics.

Accurate image segmentation based on adaptive distance regularization level set method

Level set method has been widely applied in the field of image segmentation. However, the level set formulation is inevitably affected by the regularization function, in-homogeneity and weak edge in the process of evolution, which often leads to the instability and inaccuracy of image segmentation results. To solve these problems, a new distance regularization term defined by a double-well potential function is proposed to satisfy more ideal characteristics of signed distance property. In addition, a novel edge indicator function is introduced to segment images with uneven intensity or weak edge. Finally, the adaptive adjustment formulas of distance regularization and area parameters are derived to alleviate the difficulty of parameter adjustment. Experimental results show that the proposed model provides better accuracy and versatility, quantitative experiment on Weizmann segmentation evaluation database achieves mean Dice score (96.87%), IoU (94.38%), Hausdorff distance (3.20[Formula: see text]mm), Recall (97.68%) and Precision (96.32%), respectively.

Collective influences of boundary layer process and synoptic circulation on particulate pollution: A new study in changsha-zhuzhou-xiangtan urban agglomeration of central china

Particulate matter pollution is increasingly serious in Changsha-Zhuzhou-Xiangtan urban agglomeration (CZT) due to rapid industrialization and urbanization. Particulate matter pollution and weather conditions are closely related. In this study, the relationship between the large-scale weather circulation types (CTs), horizontal recirculation wind field, boundary layer structure and particulate matter pollution (PM2.5) in CZT was systematically investigated. Firstly, the Allwine and Whiteman (AW) wind field classification method and Richardson method were used to calculate the horizontal recirculation index and the planetary boundary layer height (PBLH) in CZT. By analyzing the relationship among the recirculation index, the PBLH and the particle concentration, it was found that the recirculation process in CZT could be divided into horizontal recirculation and “pseudo recirculation”. Then, the circulation patterns affecting the CZT were divided into nine types (CT1-CT9) by using T-mode PCA method based on 925 hpa geopotential data. The CZT was prone to recirculation under the control of high pressure rear (CT4), weak high pressure edge (CT5) and southwest vortex (CT7, CT8). It was found that CT4 and CT5 accompanied with lower PBLH and poorer vertical diffusion conditions characterized by high particle concentration. While CT7 and CT8 accompanied with higher PBLH and better vertical diffusion conditions characterized by low particle concentration. Finally, the effect of CTs, recirculation index and boundary layer structure on paticle matter concentration were assessed during two recirculation processes in December 2016 and August 2017, repectively. The analysis was complemented with FLEXPART-WRF model simulations, which confirmed that the recirculation and “pseudo recirculation” can be effectively distinguished by introducing the PBLH, combined with the large-scale weather circulation.