Edge Direction Research Articles

A Histogram of Oriented Gradients Approach for Detecting Broken Bars in Squirrel-Cage Induction Motors

This article presents a novel approach for detecting broken rotor bars in squirrel-cage induction motors, using a time-domain current analysis. More particularly, this solution proposes a new use of histogram of oriented gradients since this method is usually applied in computer vision and image processing applications. Fully broken rotor bars have been detected when the motor was running at a very low slip since this operational condition is very difficult to identify using the traditional motor-current signature analysis. In addition, only one phase of the stator current of the machine was applied to extract the intensity gradients and edge directions of each current time window, for both healthy and damaged rotors. It is important to highlight that the present method does not require the slip measurement for fault detection, as demand for other techniques and often related to false negative indications. The features extracted from the histograms have been applied as inputs for a neural network classifier. This method has been validated using some experimental data from a 7.5-kW squirrel-cage induction machine running at distinct load levels (slip conditions) and also for oscillatory loads.

A New Edge Patch with Rotation Invariance for Object Detection and Pose Estimation

Local patch-based methods of object detection and pose estimation are promising. However, to the best of the authors’ knowledge, traditional red-green-blue and depth (RGB-D) patches contain scene interference (foreground occlusion and background clutter) and have little rotation invariance. To solve these problems, a new edge patch is proposed and experimented with in this study. The edge patch is a local sampling RGB-D patch centered at the edge pixel of the depth image. According to the normal direction of the depth edge, the edge patch is sampled along a canonical orientation, making it rotation invariant. Through a process of depth detection, scene interference is eliminated from the edge patch, which improves the robustness. The framework of the edge patch-based method is described, and the method was evaluated on three public datasets. Compared with existing methods, the proposed method achieved a higher average F1-score (0.956) on the Tejani dataset and a better average detection rate (62%) on the Occlusion dataset, even in situations of serious scene interference. These results showed that the proposed method has higher detection accuracy and stronger robustness.

Improving the Accuracy for Offline Arabic Digit Recognition Using Sliding Window Approach

Handwritten Digit recognition is a challenging problem these days due to the widely used Arabic language in the world, especially in the Middle East region. In this paper, sliding windows are used to enhance classification accuracies and implemented using random forests (RF) and support vector machine (SVM) classifiers for recognition of Arabic digit images. In order to study their effectiveness with and without using sliding windows, four different feature extraction techniques have been proposed which includes Mean-based, Gray-Level Co-occurrence Matrix (GLCM), Moment-based, and Edge Direction Histogram (EDH). The obtained accuracies show the significance of using sliding windows for classifying digit. The recognition rates acquired using the modified version of AHDBase dataset are 98% when Mean-based and Moment-based are applied with RF classifier, 98.33% and 99.13% when GLCM and EDH are used with linear-kernel SVM, respectively. Moreover, the performance of this study is compared against recent state-of-the-art approaches, namely Geometric-based, two-dimensional discrete cosine transform, Hierarchical features, Hetero-features, Discrete Fourier Transform and geometrical features, Gabor-based, gradient, structural, and concavity and Local Binary Convolutional Neural Networks.

Efficient hybrid multi-level matching with diverse set of features for image retrieval

Content-based image retrieval has become popular in the retrieval of images from large image database using reduced human intervention. Researchers are still in need to develop effective systems for dealing many of the wide-scope scientific and medical applications. Past research works have faced a problem on differentiating different images by means of using the single features alone. In this paper, a multi-level matching scheme is introduced for retrieval of image based on a hybrid feature similarity integrating local and global features. Both global- and local-level features included in multi-level scheme are used for image representation. From an image, the color information is extracted globally using a new color, edge directivity descriptor and color-based features. Further, the interest of points from each image is detected using local descriptors called local binary pattern and speeded-up robust features. Using two image databases, the improved retrieval accuracy obtained with the combination of global and local features is analyzed. Experimental outcomes have revealed the effectiveness of proposed system on achieving 91% and 92% precision rates over two datasets compared to other existing methods.

Noncontact guide system for traveling continuous thin steel plate: fundamental consideration on control performance by positioning control in edge direction

Noncontact guide system for traveling continuous thin steel plate: fundamental consideration on control performance by positioning control in edge direction

Facial Expression Recognition with Neighborhood-Aware Edge Directional Pattern (NEDP)

Currently available local feature descriptors used in facial expression recognition at times suffer from unstable feature descriptions, especially in the presence of weak and distorted edges due to noise, limiting their performances. We propose a novel local descriptor named Neighborhood-aware Edge Directional Pattern (NEDP) to overcome such limitations. Instead of relying solely on the local neighborhood to describe the feature around a pixel, as done by the existing local descriptors, NEDP examines the gradients at the target (center) pixel as well as its neighboring pixels to explore a wider neighborhood for the consistency of the feature in spite of the presence of subtle distortion and noise in local region. We introduce template-orientations for the neighboring pixels, which give importance to the gradients in consistent edge directions, prioritizing the specific neighbors falling in the direction of the local edge to represent the shape of the local textures, unambiguously. Moreover, due to the effective management of the featureless regions, no such region is erroneously encoded as a feature by NEDP. Experiments of the performances for person-independent recognition on benchmark expression datasets also show that NEDP performs better than other existing descriptors, and thereby, improves the overall performance of facial expression recognition.

Intelligent color image restoration system using cognitive fuzzy concept

Deinterlacing is the procedure of reworking interlaced video signal into a non-interlaced format. The interlaced video signal is generally used in both of analog TV and HDTV with 1080i. The deinterlacing method was designed to solve a legacy issue, therefore the interlaced signal which was requested by analog TV must be trans-formed to be displayed on HDTV. This paper introduces fuzzy concept-based deinterlacing method and shows how it can be executed on various test set. The proposed method includes weight calculation stage and deinterlacing implementation stage. In weight calculation stage, I compute correct weight values on each edge direction, which are calculated using fuzzy concepts. In deinterlacing implementation stage, the computed weight values are used to obtain final deinterlacing pixels by multiplying them with candidate existent pixels. The presented method provides outstanding subjective and objective results in various data set.

An improved BM3D algorithm based on anisotropic diffusion equation.

Traditional 3D block matching (BM3D) algorithms are among the best denoising methods at present; however, they exhibit the issue of ringing around image edges, which makes them unable to protect image edges and details. Therefore, this paper proposes an BM3D noise processing algorithm for the diffusion equation to reduce image noise without affecting image details, specifically at the edges. This method first uses anisotropic diffusion (AD) filtering for image preprocessing, and then uses the edge direction instead of horizontal direction to search for similar blocks. The AD model is mainly improved to achieve better edges and detailed processing effects. Firstly, with the improved AD direction, a 5 × 5 edge enhancement operator model is implemented in eight directions, and the corresponding gradient information is obtained. This operator improves the processed image edges to achieve clear contours and good continuity. Next, a new calculation method for the diffusion function, whose coefficient is constructed using a hyperbolic tangent function, is introduced. The proposed method is based on the link between the image gradient and diffusion function, and it is mathematically proven that the diffusion function converges faster than the diffusion function of the model proposed by Perona and Malik. Experimental results indicate that the improved model can effectively retain the image edges and texture details, avoid edge ringing, and provide significant improvements in terms of the subjective visual effects and objective numerical indicators.

Semi-Supervised Gated Spectral Convolution on a Directed Signed Network

A complex network is a powerful tool that enables a complex system in the real world to be represented as a network structure. Due to the heterogeneous edges and nodes implying rich semantic information, network representation has received considerable attention in both research and industrial domain. Over the recent years, the graph convolutional network (GCN) has provided a novel approach for learning network embeddings. However, this primarily supports undirected unsigned networks; that is, it cannot be directly applied to directed signed networks because it is challenging to effectively depict the direction and signs of edges in such models. In this paper, we therefore propose a method for semi-supervised gated spectral convolution in directed signed networks. We first extend the concept of the GCN to directed signed networks, which not only preserves the advantages of the traditional GCN but also properly describes the significance of the directions and signs of the edges. We then innovatively define sign (label) propagation rules in directed signed networks, rendering the networks semi-supervised. Furthermore, we enhance the balance theory to constrain the process of sign propagation to obtain network embedding with better interpretability. To satisfy the needs of large-scale complex networks, we propose a gating mechanism to adaptively forget sign information, which significantly reduces the time-space complexity of the sign propagation process. Finally, we compare the proposed method with state-of-the-art baselines using four real-world data sets for the classical link sign prediction task. Experimental results demonstrate that the proposed method is competitive.

End-Fire Vivaldi Antenna Array With Wide Fan-Beam for 5G Mobile Handsets

In this paper, we present a portable end-fire antenna array design for the fifth-generation (5G) mobile handsets that exploits a Vivaldi antenna and shows a wide fan-beam on the elevation plane. We make the proposed antenna array more efficient by printing on a 10-layer printed circuit board (PCB) lamination in a vertical direction of the ground plane edge. Using the proposed Vivaldi antenna array design, the radiation characteristics of 4 × 1 linear arrays are fabricated. To validate the feasibility, we perform simulations and experiments. Simulation results show that the total efficiencies of the antenna array are higher than about 8.16 - 9.46 dBi for the scanning range between 0° to 60°. Measurement results display that the antenna has S11 reply less than -10 dB in the frequency area of 27.5 to 28.5 GHz and wide beamwidth (130.8° in elevation plan, 21.35° in azimuth plan). There is a high accord between the calculated and measured results and we consider that the results in this study can be well achieved by designers who design the wide beamwidth high-speed antennas of 5G mobile terminals.

Quality consistency analysis for complex assembly process based on Bayesian networks

In recent years, assembly process has been rapidly pushing the envelope in building complex, optimized products, by taking advantage of big data mining and machine intelligence technologies. Along with such developments, it has become increasingly necessary to strictly control quality consistency level of the assembly activities. However, due to the difficulty of modelling, quality consistency improvement of complex assembly process has always been a challenge for academia and industry. This paper attempts to describe the assembly process by using Bayesian networks and to provide an effective data-based scheme for improving quality consistency. First of all, considering the nonlinear and coupling characteristics, we introduce the maximum information coefficient and network deconvolution method to detect the direct associations in the assembly process and get an undirected graph. Secondly, in order to obtain a complete Bayesian network, we derive the directions of edges based on the independence between causal variable distribution and function mechanism. Thus, the key influence factors on quality consistency and the latent causal mechanism can be analyzed. This data-based scheme is applied to power consistency analysis of a real diesel engine production line, where the effectiveness is further demonstrated from real industrial data. As a result, this study provides theoretical support and technical guarantee for the improvement of assembly process, quality control, product quality and enterprise benefit.

Overview of Edge Computing in the Agricultural Internet of Things: Key Technologies, Applications, Challenges

The application of the Internet of Things in agricultural development usually occurs via a monitoring network that consists of a large number of sensor nodes, thus gradually transforming agriculture from a human-oriented and single-machine-centric production model to an information- and software-centric production model. Due to the large area coverage of agriculture and the variety of production objects, if all farmland perception information is gathered into the cloud server, the server will exert greater pressure on the network, which reduces the speed of response to event processing. This problem may be perfectly solved by the recent emergence of Edge computing, which can share the load of the cloud server and reduce the delay. Edge computing has prospects in agricultural applications, such as pest identification, safety traceability of agricultural products, unmanned agricultural machinery, agricultural technology promotion, and intelligent management. The application of the Agricultural Internet of Things integrates artificial intelligence, the Internet of Things, and blockchain and Virtual/Augmented Reality technologies. This paper primarily reviews the application of Edge computing in the Agricultural Internet of Things and investigates the combination of Edge computing and Artificial Intelligence, blockchain and Virtual/Augmented reality technology. The challenges of Edge computing task allocation, data processing, privacy protection and security, and service stability in agriculture are reviewed. The future development direction of Edge computing in the Agricultural Internet of Things is predicted.

Directional Coherence-Based Scrolling-Text Detection for Frame Rate Up-Conversion

This article proposes a new scrolling-text detection method that uses directional coherence for frame rate up-conversion (FRUC). Most previous methods use either gradient information or motion vector (MV) distribution of the frame for scrolling-text detection. Edges can be generated by non-text components and the number of MVs to determine the scrolling-text decreases in each row of the frame. Thus, they incorrectly detect the non-text regions as scrolling-text and cannot accurately detect the start or end of text scrolling at the frame boundary. The proposed method overcomes these problems using coherence values of edge directions for each pixel and scrolling-text-aware refinement processes. The key idea of the proposed method is to use the directional coherence of edge directions and use texture patterns analysis-based refinement to improve the accuracy of the scrolling-text detection. For refinement processes, the proposed method extracts texture patterns as bit codes. Then, it computes the diversity of the texture patterns around the detected text edges. In addition, the proposed method extracts the representative value of the MV for the detected region to correct the regions falsely detected as the scrolling-text. With these refinement processes, the proposed method can also accurately detect the start or end of text scrolling at the frame boundary. In the experimental results, the proposed method increased the average F1 score to 0.504 (a 131.25% improvement) compared with previous methods. The average computation time per pixel of the proposed method also decreased to $18.571~\mu \text{s}$ (an 80.80% reduction) compared with previous methods.

Vision tracking based on adaptive interactive fusion

Under the influence of COVID-19, detection and identification of moving targets are very important for personnel management. A lot of research work has improved the accuracy and robustness of the moving target tracking method, but the recognition accuracy of the traditional target tracking method in complex scenes (lighting changes, background interference, posture changes and other factors) is not satisfactory. In this paper, in view of the limitations of single feature representation of target objects, the method of fusion of HSV color features and edge direction features is used to identify and detect moving targets. In each frame of the tracking process, the weight of each feature is adjusted adaptively according to the proposed fusion strategy, and the position of the target is located by using the method of double template matching. Experiments show that the proposed tracking algorithm based on multi feature fusion can meet the requirements of moving target recognition in complex scenes. The method proposed in this paper has a certain reference value for personnel management under the influence of COVID-19.

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

Existing edge detection algorithms suffer from inefficient edge localization, noise sensitivity, and/or relatively poor automatic detection capability. Contemporary edge detection algorithms can be improved by targeting these problems to help bolster their performance. Grey system theory can be used to resolve the small data and poor information issues in the local information of uncertain systems. An automatic edge detection algorithm was developed in this study based on a grey prediction model to remedy these problems. Noise characteristics in grey images are used to deploy a noise-filtering algorithm based on local features. A mask with twenty-four edge direction information points (345°) was established based on edge line texture features. By compressing the amplitude of the sequence, the randomly oscillated grey prediction sequence can be converted into a smooth, new sequence. The discrete grey model (1,1) (DGM(1,1)) was established based on this new grey prediction sequence to obtain the grey prediction maximum value. A grey prediction image with enhanced edges was obtained by replacing the pixel value in the original image with the maximum grey prediction value. A grey prediction subtraction image with edges separated from non-edge points was also obtained by subtracting the original image from the grey prediction image. The optimal separation threshold in the grey prediction subtraction image can be determined via the global adaptive threshold selection method. The neighborhood search method was then deployed to remove stray points and burrs from the image after the target was separated from the background, creating the final edge image. Experiments were performed on a computer-simulated phantom to find that both the subjective visual effects and objective evaluation criteria are better under the proposed method than several other competitive methods. The proposed edge detection algorithm shows excellent edge detection ability and is highly robust to noise, though the grey prediction model needs further improvement to optimize the run time.

Privacy-Preserving Content-Based Image Retrieval Using Compressible Encrypted Images

In this article, we propose a novel content-based image-retrieval (CBIR) scheme using compressible encrypted images called “encryption-then-compression (EtC) images.” The proposed scheme allows us not only to directly retrieve images from visually protected images but to also apply EtC images that can be compressed by using the JPEG standard for the first time. In addition, the sensitive management of secret keys is not required in our framework. The proposed retrieval scheme is carried out on the basis of weighted searching images with MPEG-7-powered localized descriptors (weighted SIMPLE descriptors) combining scalable color descriptor (SCD) or color and edge directivity descriptor (CEDD). Weighted SIMPLE descriptors are extended, and CEDD is also modified to avoid the influence of image encryption. In an experiment, the proposed scheme is demonstrated to have almost no degradation in retrieval performance compared with conventional content-based retrieval methods with plain images under the use of two datasets. In addition, the proposed scheme is shown to outperform conventional privacy-preserving CBIR schemes including state-of-the-art ones in terms of mean average precision (mAP) scores.

Centrality anomalies in complex networks as a result of model over-simplification

Tremendous advances have been made in our understanding of the properties and evolution of complex networks. These advances were initially driven by information-poor empirical networks and theoretical analysis of unweighted and undirected graphs. Recently, information-rich empirical data complex networks supported the development of more sophisticated models that include edge directionality and weight properties, and multiple layers. Many studies still focus on unweighted undirected description of networks, prompting an essential question: how to identify when a model is simpler than it must be? Here, we argue that the presence of centrality anomalies in complex networks is a result of model over-simplification. Specifically, we investigate the well-known anomaly in betweenness centrality for transportation networks, according to which highly connected nodes are not necessarily the most central. Using a broad class of network models with weights and spatial constraints and four large data sets of transportation networks, we show that the unweighted projection of the structure of these networks can exhibit a significant fraction of anomalous nodes compared to a random null model. However, the weighted projection of these networks, compared with an appropriated null model, significantly reduces the fraction of anomalies observed, suggesting that centrality anomalies are a symptom of model over-simplification. Because lack of information-rich data is a common challenge when dealing with complex networks and can cause anomalies that misestimate the role of nodes in the system, we argue that sufficiently sophisticated models be used when anomalies are detected.

Real-Time Docking Ring Detection Based on the Geometrical Shape for an On-Orbit Spacecraft.

Docking ring is a circular hatch of spacecraft that allows servicing spacecraft to dock in various space missions. The detection of the ring is greatly beneficial to automatic capture, rendezvous and docking. Based on its geometrical shape, we propose a real-time docking ring detection method for on-orbit spacecraft. Firstly, we extract arcs from the edge mask and classify them into four classes according to edge direction and convexity. By developing the arc selection strategy, we select a combination of arcs possibly belonging to the same ellipse, and then estimate its parameters via the least squares fitting technique. Candidate ellipses are validated according to the fitness of the estimation with the actual edge pixels. The experiments show that our method is superior to the state-of-the-art methods, and can be used in real time application. The method can also be extended to other applications.

Speckle Reduction by Directional Coherent Anisotropic Diffusion

To effectively balance speckle smoothing and preservation of edges and radiation, a novel anisotropic diffusion filter was developed that uses a directional coherent coefficient. The proposed filter effectively improves the edge detection operator of a traditional anisotropic diffusion filter. The new edge detection operator calculates 16 direction coherence coefficients to avoid the interference of the edge direction. For the diffusion function, the proposed method directly uses the detected directional coherent edge as the diffusion coefficient, which simplifies the calculation of the diffusion function and avoids the adverse effects of inaccurate estimation of the diffusion function threshold for a traditional anisotropic diffusion filter. The influence of the number of iterations and time steps on the proposed filter was analyzed. A series of experiments was conducted with a simulated image and three real synthetic-aperture radar images from different sensors. The results confirmed that the proposed method not only significantly reduces speckle but also effectively preserves the edge and radiation information of images.

Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk

AbstractThe dynamic behavior of e‐beam irradiated Si atoms in the bulk and at the edges of single‐layer graphene is examined using scanning transmission electron microscopy (STEM). A deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. A Gaussian mixture model is further used to establish the elementary atomic configurations of the Si atoms, defining the bonding geometries and chemical species and accounting for the discrete rotational symmetry of the host lattice. The frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the defect populations and chemical transformation networks from the atomically resolved STEM data. Here, a clear tendency is observed for the formation of a 1D Si crystal along zigzag direction of graphene edges and for the Si impurity coupling to topological defects in bulk graphene.