AN ALGORITHM OF EDGE DETECTION BASED ON ENTROPY OPERATOR

This paper presents an algorithm of edge detection in image processing. A new entropy operator and threshold estimation technique are effectively proposed. The algorithm overcomes some drawbacks of Shiozaki operator. It not only has higher speed but also can extract the edge better. Finally, an example of 2D image is given to demonstrate the usefulness and advantages of the algorithm.

Grey Relevancy Degree and Improved Eight-Direction Sobel Operator Edge Detection

Edge detection is an important aspect to improve image edge quality in image processing. The purpose of edge detection is to identify the points in digital images with great brightness variation. However, the accuracy of traditional edge detection methods in edge extraction is low. For the actual image, the grey edge is sometimes not very clear, the image also contains noise. The detection result of the traditional Sobel operator is relatively accurate, but the detection result is rough and sensitive to noise. To solve the above problems, this paper proposes an improved eight-direction Sobel operator based on grey relevancy degree, which combines 5 × 5 Sobel operator with a grey relational degree and a new eight-direction grey relevancy method. The results show that this method can detect the useful information of edge more accurately and improve the anti-noise performance. However, the drawback is that the algorithm is not automatic.

Edge Detection of Composite Insulators Hydrophobic Image Based on Improved Canny Operator

The detection of hydrophobicity is an important way to evaluate the performance of composite insulators, which is helpful to the safe operation of composite insulators. Image processing technology is used to judge the hydrophobicity of composite insulators, which makes detection results more accurate and overcomes the subjective drawbacks of traditional detection methods.?As the traditional Canny operator requires manual intervention in selecting the variance of the Gaussian filter and the threshold, the paper presents a method of edge detection based on improved Canny operator. First, the adaptive median filter replaces the Gaussian filter, which can eliminate the impact from the variance of Gaussian filter and remove noise according to the characteristics of the image itself. Then the Ostu threshold method is used to select the best threshold automatically, which makes the edge detection be more continuous and reduce the presence of fake edges. The results show that the operator is applicable to all hydrophobic images.

SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism.The secondorder neighborhood shows the connection between the current pixel and the surroundings of this pixel.This connection is for each RGB component color of the input image.Once the image edges are detected for the three primary colors:red,green,and blue,these colors are merged using the combination rule.Then,the final decision is applied to obtain the segmentation.This process allows different data sources to be combined,which is essential to improve the image information quality and have an optimal image segmentation.Finally,the segmentation results of the proposed model are validated.Moreover,the classification accuracy of the tested data is assessed,and a comparison with other current models is conducted.The comparison results show that the proposed model outperforms the existing models in image segmentation.

Edge detection of potential field data based on image processing methods

The conventional methods of edge detection can roughly delineate edge position of geological bodies,but there are still some problems such as low detection accuracy and being susceptible to noise interference.In this paper,three image processing methods,Canny,Lo G and Sobel operators are briefly introduced,and applied to edge detection to determine the edge of geological bodies.Furthermore,model data is built to analyze the edge detection ability of this image processing methods,and compare with conventional methods.Combined with gravity anomaly of Sichuan basin and magnetic anomaly of Zhurihe area,the detection effect of image processing methods is further verified in real data.The results show that image processing methods can be applied to effectively identify the edge of geological bodies.Moreover,when both positive and negative anomalies exist and noise is abundant,fake edge can be avoided and edge division is clearer,and satisfactory results of edge detection are obtained.

Lifting Scheme and Its Application to Edge Detection

Lifting scheme is a useful and very general technique for constructing wavelet decomposition. The paper adapts the lifting into redundant lifting to obtain shift invariant wavelet transform. In prediction and update stages of the lifting morphological operator is adopted for preserving local maxima of a signal over several scales, which is particularly useful in wavelet\|based signal detec tion. The new transform presented in the paper is applied in multiresoluti on edge detection of medical image and experim ent results are given to show better performance and applicable potentiali ty.

Two-Phase Image Segmentation by the Allen-Cahn Equation and a Nonlocal Edge Detection Operator

Based on a nonlocal Laplacian operator,a novel edge detection method of the grayscale image is proposed in this paper.This operator utilizes the information of neighbor pixels for a given pixel to obtain effective and delicate edge detection.The nonlocal edge detection method is used as an initialization for solving the Allen-Cahn equation to achieve two-phase segmentation of the grayscale image.Efficient exponential time differencing(ETD)solvers are employed in the time integration,and finite difference method is adopted in space discretization.The maximum bound principle and energy stability of the proposed numerical schemes are proved.The capability of our segmentation method has been verified in numerical experiments for different types of grayscale images.

A new algorithm of edge detection for color image: Generalized fuzzy operator

The definition of the generalized fuzzy set is presented for the first time, and a generalized fuzzy operator is proposed to transform a generalized fuzzy set into a normal fuzzy set. The algorithm theory of the operator, as the newest method of the edge detection of a 2-D image, is successfully established. Many experiments haw proved that the algorithm is simpler, more rapid and more precise in location than other edge detection methods. And a schedule of the concrete performance has been given additionally about the edge detection of color images.

Fine edge detection in single-pixel imaging

Typical single-pixel imaging techniques for edge detection are mostly based on first-order differential edge detection operators.In this paper,we present a novel edge detection scheme combining Fourier single-pixel imaging with a second-order Laplacian of Gaussian(LoG)operator.This method utilizes the convolution results of an LoG operator and Fourier basis patterns as the modulated patterns to extract the edge detail of an unknown object without imaging it.The simulation and experimental results demonstrate that our scheme can ensure finer edge detail,especially under a noisy environment,and save half the processing time when compared with a traditional first-order Sobel operator.

A review of dielectric optical metasurfaces for spatial differentiation and edge detection

Dielectric metasurfaces-based planar optical spatial differentiator and edge detection have recently been proposed to play an important role in the parallel and fast image processing technology.With the development of dielectric metasurfaces of different geometries and resonance mechanisms,diverse on-chip spatial differentiators have been proposed by tailoring the dispersion characteristics of subwavelength structures.This review focuses on the basic principles and characteristic parameters of dielectric metasurfaces as first-and second-order spatial differentiators realized via the Green's function approach.The spatial bandwidth and polarization dependence are emphasized as key properties by comparing the optical transfer flinctions of metasurfaces for different incident wavevectors and polarizations.To present the operational capabilities of a two-dimensional spatial differentiator in image information acquisition,edge detection is described to illustrate the practicability of the device.As an application example,experimental demonstrations of edge detection for different biological cells and a flower mold are discussed,in which a spatial differentiator and objective lens or camera are integrated in three optical pathway configurations.The realization of spatial differentiators and edge detection with dielectric metasurfaces provides new opportunities for ultrafast information identification in biological imaging and machine vision.

Canny edge detector and its application to analyze the brazed joint interfaces

Image processing is widely used as a method for visnal inspection in industry. Analyzing the microstructure image of a brazed joint is a very important part of the quality control in products related to brazing aUoys. Edge detection techniques are introduced to analyze the bonding strength of the brazing alloys in this paper. Gaussian filter is used for image smoothing. The sharp edge map produced by the Canny edge detector is added to the smoothed noisy image to generate the edge image, which can show the brazing elements clearly. Using the Canny edge detector as a tool to analyze the bonding strength of brazed joint, the experiment results are robust with a very high level. Therefore, the Canny edge detector can be reliably used to analyze the brazed joint interfaces.

Subpixel accuracy for extracting groove center based on corner detection

Subpixel accuracy for V-groove center in robot welding is researched and a software measure to increase the accuracy of seam tracking by laser is presented. LOG （ Laplacian of Gaussian ） operator is adopted to detect image edge. Vgroove center is extracted by corner detection of extremum curvature. Subpixel position is obtained by Lagarange polynomial interpolation algorithm. Experiment results show that the method is brief and applied, and is sufficient for the real time of robot welding by laser sensors.

基于遗传算法优化Canny算子的织绣文物纹样抽取方法研究

织绣文物中的纹样彰显中国传统文化造型艺术的形式美感,是文创设计的重要素材源泉。针对传统Canny算子进行纹样边缘检测需人为确定阈值的问题,本文提出基于遗传算法自适应优化Canny算子阈值的方法,将边缘点、非边缘点类间梯度幅值方差设置为适应度函数,通过格雷码对阈值染色体编解码、锦标赛选择算法优选交叉变异后的阈值,求解最优适应度对应的高低阈值,用于纹样边缘检测;此外,运用形态学操作得到纹样的完整、精确轮廓;最后,添加Alpha通道抽取纹样前景。实验表明,上述方法在织绣纹样抽取精度和像素准确度(PA)上,优于传统Canny算子、混合蛙跳自适应Canny算子、Otsu法优化Canny算子等已有方法,可有效检测织绣文物图像中纹样真实边缘并完成纹样抽取。

无人机影像GrabCut路面裂缝识别

无人机影像裂缝提取是近年来研究的热点问题之一。针对无人机影像强边缘信息干扰问题,本文提出了一种基于GrabCut算子的裂缝识别方法。该方法首先运用GrabCut算子提取保留裂缝的前景路面,然后运用去噪、边缘检测和双阈值轮廓识别方法探测路面裂缝。这种裂缝识别方法较好地排除了大量伪边缘信息和次生噪声干扰,实现了高分辨率无人机影像裂缝自动识别。试验结果表明,基于GrabCut算子的路面提取方法优于颜色特征提取算法和分水岭算法,适用于复杂场景路面提取,具有较高的普适性;同时,该方法可以快速获取裂缝信息,检测尺度可以人为控制,易于实现多尺度裂缝信息识别。研究结果可应用于路面裂缝定位识别、线性路面设施检测及路面灾害性评估等领域。

基于Sobel算子桥接的双编码器路面裂缝检测网络

为提高道路路面裂缝的检测精度,针对路面裂缝的多态性和噪声复杂等问题,提出了一种基于Sobel算子桥接的双编码器路面裂缝检测网络,双编码器由原图编码和梯度编码两部分组成,以解决单编码器容易丢失梯度信息的问题。首先,原图编码结果通过桥接Sobel算子计算8个方向产生梯度编码的编码信息;然后,将原图编码结果与梯度编码结果通过一个多尺度的边缘信息弥补模块,以增强裂缝的边缘信息;最后,引入动态通道图卷积获得通道之间存在的拓扑关系,以突出重要通道的语义特征。研究结果表明:所提出的方法在DeepCrack、CamCrack789和CFD这3个基准数据集上取得较好的结果;综合指标ODS在DeepCrack、CamCrack789和CFD数据集分别为87.75%、85.05%、78.83%。

采煤机截割部低照度图像的边缘检测技术

针对井下低照度环境下采煤机截割部边缘检测任务中存在的边缘缺失、细节模糊等问题,提出一种基于分数阶微分的边缘检测Lif算法。首先采用更大的检测模板尺寸,根据Grünwald-Let-nikov分数阶定义构造最初的分数阶掩膜算子;然后根据Pascal三角形理论确定掩膜算子上各位置的权重系数,并将掩膜算子扩展到4个不同方向;最后将得到的掩膜算子与图像进行卷积,利用图像的局部特征信息对每个方向的微分结果进行后处理。结果表明:(1)在进行多个不同场景的井下低照度图像上的实验时,Lif算法可以更全面地获取图像中不同方向上的边缘信息,在处理低照度图像时具备更强的抗噪性能,并且提取的边缘线条比其余边缘检测算法更加清晰、完整,保留了更多的纹理细节信息。(2)在客观指标评价的对比上,与基于分数阶灰色系统模型的边缘检测算法以及改进的分数阶Sobel边缘检测算法相比,Lif算法在Entropy指标上分别提高了43%、11%,AG指标上分别提高了23%、23%,SSIM指标上分别提高了152%、6%。表明Lif算法在进行采煤机截割部的边缘检测任务时更具优势,研究对井下设备工作运行时的安全性和可靠性提升具有重要意义。

无人机高光谱岩性和矿物识别技术研究

无人机高光谱遥感技术以其高空间分辨率和高光谱分辨率的技术优势,在大比例尺区域地质填图领域可以发挥重要作用。利用无人机搭载全谱段高光谱成像仪(HD_SAVI)获取0.2 m高空间分辨率影像数据,采用经验线性法对所获取数据进行光谱重建,重建后无人机高光谱影像光谱曲线与地面实测光谱曲线谱形基本一致,典型特征吸收峰位置偏差均在1个像元以内。采集岩性样本构建岩性知识库,利用深度学习算法基于无人机高光谱影像数据开展岩性识别;岩性识别结果与大比例尺地质图对比,岩性空间分布基本一致,地质体边界准确且较清晰。参考典型矿物光谱库进行矿物识别。与同一地区CASI/SASI航空高光谱数据地质填图结果进行对比分析,结果表明:两者提取的矿物在空间分布上具有较高的一致性,但无人机低空高光谱影像空间分辨率较高,识别出的矿物种类更加丰富。无人机高光谱遥感技术可以满足无人区、交通不便地区地质勘查,以及重点区或矿区外围等地区大比例尺地质填图的需要。

基于MATLAB的交通标志边缘检测技术

交通标志识别在智能交通系统中意义重大,交通标志边缘检测是交通标志识别中很关键的一步,本文首先介绍几种常用的边缘检测算法,其次对不同类型的交通标志图像进行边缘检测,并对比无噪声标志图像和有噪声标志图像的检测结果,最后对处理得到的结果进行比较分析,便于人们在实际应用的时候选择合适的检测算法。

一种在尺度空间下基于边缘的角点目标检测方法

图像中的目标角点位置是实现很多计算机视觉任务的关键数据。为了克服传统检测算法产生的数据冗余问题,提出了一种在尺度空间下基于边缘的角点目标检测方法。首先,构建一个分组多层的尺度空间,将原图投影到其中后得到多个平滑图像。与此同时,采用定义的边缘算子检出平滑图像中所有边缘而得到多个按序存放的像素点集,当点集数量稳定时停止更大尺度的变换。然后,在当前尺度下,计算点集中各元素反映其角点强度的特征值。根据这些特征值变化规律检出角点的支撑集区间,并在此区间中采用高斯拟合函数确定最终的目标角点。实验表明,该方法能够检出特征显著的目标角点及其角度,其中合成图像精度在像素级,应用案例中的平均误差与图幅比约为1.5/100。