基于分形特征的图像边缘检测方法

运用分形理论描述图像纹理特征,通过分析不同纹理图像及图像边缘处的分形参数,得到一种新的边缘检测分形特征,从而提出一种基于分形特征的图像边缘检测方法。自适应阈值的引入,能够实现不同图像的边缘检测。该算法简单迅速,并具有良好的抗噪性能。     

用标记松弛方法检测闪光图像边缘

描述了边缘检测的标记松驰法。在该方法的基础上探索出一条用于检测高能闪光图像边缘的新路子。整个处理过程分为两步：第一,采用离散正交多项式曲面拟合技术探测边缘位置;第二,运用松驰标定网突出有意义的边缘结构和压缩噪声边缘。同时给出了运用此方法探测高能闪光图像边缘的结果。     

基于小波变换的图像边缘检测

本文主要对目前存在的图像边缘检测的方法进行了研究分析,重点研究了小波变换在图像边缘检测方而的优势,提出了基于连续小波变换的边缘检测方法,并编程展示编码的结果,验证其在实际应用中的可行性。     

浅析图像边缘检测方法

介绍图像边缘的基本概念和经典的图像边缘检测方法。并着重近年来发展最快的新的图像边缘检测方法。这对于进一步学习和寻找更好的图像边缘检测方法具有实用意义。     

基于神经网络的图像边缘检测方法

提出了一种基于神经网络的图像边缘检测新方法.该方法首先基于邻域灰度极值提取边界候选图像,然后以边界候选象素及其邻域象素的二值模式作为样本集,输入边缘检测神经网络进行训练.边缘检测神经网络采用BP网络,为加快网络的训练速度,采用了滚动训练和权值随机扰动的方法.实验表明,该方法提高了神经网络的学习效率,获得的边缘图像封闭性好,边缘描述真实.     

边缘特性及边缘检测在图像插值算法中的应用研究

首先论述边缘的基本特性,边缘蕴含丰富的内在信息,如方向、阶跃性质、形状等,对于剧烈跳变的边缘,灰度值差变化大,跳变节奏加快,产生阶跃变化或屋顶状变化。然后分析Canny边缘检测,指出其具有很好的边缘强度估计,为提高插值图像质量和插值算法提供理论根据。     

综合边缘检测和区域生长的红外图像分割方法

针对红外图像的特点,提出了一种综合应用边缘检测和区域生长方法的图像分割方法。其思路为:先对图像进行边缘提取,得到边缘像素点集;然后利用该点集的平均灰度和目标区域的连通性作为生长判决条件,采用区域生长法实现图像分割。仿真结果表明,该方法能快速准确有效地实现红外图像分割,避免了单独使用边缘提取或区域生长法进行图像分割时的典型分割错误。     

红外图像的亚像素边缘检测

针对红外图像目标边缘的模糊性,为了准确提取目标,提出了基于三次样条插值和灰阶边缘细化的方法,该方法通过对边缘过渡区域及其法线方向信息的处理,使图像边缘精确定位在亚像素级。实验结果表明,该方法能够精确定位目标边缘,优于传统的边缘检测方法。     

基于线奇异性分析的图像边缘检测方法

针对基于图像像素点分析的边缘提取方法存在无法同时满足高抑噪性、连续性,定位性等问题,本文提出了方向Beamle变换(DBT)方法,在定义图像线奇异性的理论基础上,利用DBT对图像进行线奇异性分析,依据Beamlet变换具有的线段提取能力,将图像边缘检测问题转化为方向Beamlet变换系数矩阵中奇异点的检测问题,以降低噪声点对边缘检测结果的影响.通过对人工图像以及SAR图像的实验,与经典边缘检测算子相比较,验证了本方法具有较强的抗噪性,特别是针对直线边缘,在抑制噪声影响的同时保证了线状边缘的直线连接性,抗噪性较强.     

图像的模糊边缘检测算法

常用的微分边缘检测算法往往无法设立合适的阈值将影像中梯度较小的模糊边缘检测出来.针对这一点,疚奶岢隽肆街纸饩龅姆椒?将图像方差标准化,拉大模糊边缘处的梯度值,或者通过设置sigmoid函数,将像素点所在区域的信息传递到梯度值中去,对梯度值进行调整,这样就能够设定合适的阈值,有效地将模糊边缘提取出来.本文将这两种算法和常用的一些微分边缘检测算法比如Sobel、LOG算法进行了比较.试验表明,这两种方法都能够有效地提取出模糊边缘.     

一种抗噪快速边缘检测算法

本文提出了一种快速抗噪声的边缘检测算法.首先对原始灰度图像应用快速Kirsch边缘检测算法.这样就得到了边缘的梯度和方向信息.然后基于这些信息进行边界跟踪,达到抗噪的效果,最后是细化和二值化,得到的是一个视觉上有意义的二值图像.用的是一个投票准则.为了检验本算法的有效性,将本算法与一些经典的边缘检测算子,如经典的Kirsch,Canny算子的性能进行了比较.     

An Image Edge Detection Algorithm Based on Multi-Feature Fusion

Edge detection is one of the core steps of image processing and computer vision. Accurate and fine image edge will make further target detection and semantic segmentation more effective. Holistically-Nested edge detection (HED) edge detection network has been proved to be a deep-learning network with better performance for edge detection. However, it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification. There will be detected edge incomplete, not smooth and other problems. To solve these problems, an image edge detection algorithm based on improved HED and feature fusion is proposed. On the one hand, features are extracted using the improved HED network: the HED convolution layer is improved. The residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and shapes. Meanwhile, the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature information. On the other hand, edges are extracted using Otsu algorithm: Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold value. Finally, the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final edge. Experimental results show that on the Berkeley University Data Set (BSDS500) the optimal data set size (ODS) F-measure of the proposed algorithm is 0.793; the average precision (AP) of the algorithm is 0.849; detection speed can reach more than 25 frames per second (FPS), which confirms the effectiveness of the proposed method.     

多尺度形态学图像边缘检测方法

在形态学边缘检测算子的基础上,综合形态膨胀和形态腐蚀,得到修正的边缘检测算子,以减轻图像边缘检测的模糊性;进行形态结构元素尺度调整,并综合各种尺度下的边缘特征,得到噪声存在条件下较为理想的图像边缘。实验验证了该算法的可行性和有效性。     

基于显著性边缘特征的图像匹配

为了克服可见光与红外图像之间的细节边缘纹理的差异对异源图像的匹配造成的不利影响,本文采用一种显著性边缘提取算法,该算法通过抑制图像局部区域内变化微弱的部分,加强了显著性边缘,增加了匹配的精确性和鲁棒性。异源图像匹配仿真实验表明,本算法相比传统的Canny算法在异源图像中的匹配有更好的性能。     

An Adaptive Classifier Based Approach for Crowd Anomaly Detection

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video sections. We use the multi-objective whale optimization algorithm to optimize the entire process and get the best results. The performance parameters such as accuracy, precision, recall, and F-score are considered to evaluate the proposed technique using the Python simulation tool. Our simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.     

基于边缘检测的SAR图像平行线特征提取算法

针对传统平行线定义的局限性,本文提出了一种平行线对模型,并以该模型为核心,设计了一种基于边缘检测的SAR(SyntheticAperture Radar,SAR)图像平行线特征提取算法.在图像经过滤波预处理后,首先采用具有恒虚警特性的ROEWA(Ratio of Exponentially Weighted Averages,ROEWA)算子得到边缘检测图,再利用提出的平行线基元提取算法进行检测,最后基于启发式连接的思想连接断点.实验结果表明,该算法能有效地提取SAR图像中的平行线性结构,可以进一步应用于道路网、机场跑道、河流等大型组合线性目标的自动识别中.     

基于EMD方法的多尺度边缘提取

提出了一个基于EMD方法的多尺度边缘提取方法。利用EMD方法沿水平方向和垂直方向分别处理SAR图像，得到不同尺度的图像。计算不同尺度图像的梯度，得到不同尺度图像的边缘。根据一致性条件，从不同尺度图像的边缘提出SAR图像的边缘。利用这个方法处理了合成孔径雷达图像，成功地提取了图像的边缘信息。     

An Edge Detection Algorithm for Photoelasticity Analysis

Abstract: Photoelasticity has become a modern tool of stress analysis which is capable of competing with other tools employed currently, including finite element analysis. Improved model production and automated fringe analysis allow us to perform investigations of complex models, speeding up the rate of analysis and reducing the action by users, consequently automating the whole process. However, before automated fringe analysis, the mask of the model should be extracted. The authors discuss the development of a new algorithm to detect the mask of the model by analysing isochromatic fringe patterns used in photoelasticity. It is important to know the mask of the model for its analysis and to obtain a stress map. Unlike the available edge algorithms or any other techniques used to detect a model's mask, the proposed algorithm was developed to minimise user action, allowing the process to be automated. There is a major difference between the area of the background and area of the model from the point of view of image processing. Grey level of points inside the background region are distributed along the tilted plane with low total variance, and those points inside the model regions are distributed along the isochromatic fringes having the shape of a wave. The variance of certain areas is measured with respect to the approximated plane created over such area from the grey level of each point. Areas having low variance are then selected and extended to true boundaries based on the fact that edges are characterised by a huge jump in the grey level. The proposed method is validated experimentally for a plate with multiple cutouts in a dark field and a circular disc under diametric compressive load with frozen stress in white field.     

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases. Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure. Although various approaches for retinal vessel segmentation are extensively utilized, however, the responses are lower at vessel's edges. The curvelet transform signifies edges better than wavelets, and hence convenient for multiscale edge enhancement. The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges. Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges. Therefore, in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image. Afterwards C mean thresholding is used for the extraction of vessel. The recommended fusion approach is assessed on DRIVE dataset. Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result. The results demonstrate that the recommended method outperforms the traditional approaches.