路面裂缝识别中边缘检测间断性的改进

针对传统边缘检测算子得到图像的间断和不连续的特点,结合基于形态学的图像膨胀算法和图像细化算法对路面裂缝图像的边缘检测进行了改进。先介绍了几种常用的边缘检测算子,并利用各个算子对路面裂缝图像进行了边缘检测,将结果进行了对比,根据对比结果选出了Soble算子为本次实验所用的边缘检测算子,在其基础上改进。最后,根据该类路面裂缝图像的特点,改用"菱形"结构元素代替传统的"方形"结构元素,将间断的路面裂缝图像边缘处理成连续的清晰的边缘,达到了很好的效果。     

Automatic image segmentation by integrating color-edge extractionand seeded region growing

We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.     

Automatic cardiac LV boundary detection and tracking using hybrid fuzzy temporal and fuzzy multiscale edge detection

This paper describes a new fully automatic fuzzy multiresolution-based algorithm for cardiac left ventricular (LV) epicardial and endocardial boundary detection and tracking on a sequence of short axis (SA) echocardiographic images of a complete cardiac cycle. This is a necessary step for automatic quantification of cardiac function using echo images. The proposed method is a "center-based" approach in which epicardial and endocardial boundary edge points are searched for on radial lines emanating from the LV center point. The central point of the LV cavity is estimated using a fuzzy-based technique in which the "uncertain" spatial, morphological, and intensity information of the image are represented as fuzzy sets and then combined by fuzzy operators. Edge-detection stage uses multiscale spatial and temporal information in a fuzzy multiresolution framework to identify a single moving edge point for each one of the epicardial and endocardial boundaries over the M radii in the N frames of a complete cardiac cycle. The raw extracted edge points are then processed in the wavelet domain to reduce the effects of noise from the boundaries and papillary muscles from the endocardial boundary extraction process. Finally, a uniform cubic B-spline approximation method is used to define the closed LV boundaries. Experiments with simulated and real echocardiographic images are presented.     

A shape-based image retrieval method using salient edges

Content-based image retrieval is emerging as an important research area with applications in digital libraries and multimedia databases. In this paper, we present a novel five-stage image retrieval method based on salient edges. In the first stage, the Canny operator is performed to detect edge points. Then, the Water-Filling algorithm is employed to extract edge curves. In the third stage, salient edges are selected and the shape features in terms of the salient edges are yielded. In the fourth stage, a similarity measure, namely the integrated salient edge matching, that integrates properties of all the salient edges, is introduced, and used to compare the similarity of the query image with the images in the database. Finally, the best matches are returned in similarity order. The presented approach is easy to implement and can be efficiently applied to retrieve images with clear edges. Preliminary experimental results on a database containing 6500 images are very promising.     

噪声图像中提取边缘的蚁群搜索算法

该文提出一种边缘引导的蚁群搜索算法,以解决常用的边缘提取方法抑制噪声能力不强,提取边缘不连续的缺点.此算法首先进行边缘检测获取由真实边缘和噪声组成的可能边缘点;然后利用可能边缘信息引导蚁群迭代搜索局部边缘曲线,并根据蚂蚁搜索曲线的长度更新其行走路径上的信息素分布,使搜索逐渐向真实的边缘收敛;最后,依据信息素遗留提取真实的边缘曲线.相对传统的蚁群算法,该文利用边缘信息引导蚁群搜索,增强了搜索的目的性,提高了算法效率.多组噪声图像的实验表明:该算法能够有效地从噪声图像中提取物体的真实边缘,在最大限度地保留细节信息的同时抑制噪声.     

公路病害检测中的图像平滑算法研究

在对公路路面裂缝类病害的检测过程中．通过认真分析路面病害图像特征．研究适合于路面病害识别的图像平滑算法。采用加权的邻域平均噪声滤除算法对病害图像进行平滑处理．裂缝的边缘得到了很好的保护。该算法用于对公路路面裂缝类病害的识别检测过程中．检测精度和检测效果都很好。     

三维点云重建混凝土结构裂缝定位与追踪技术的研究

混凝土结构裂缝是影响既有工程安全的病害之一，如何实现对混凝土结构裂缝的自动定位、扩展跟追，是降低混凝土裂缝病害破坏力、预防安全事故的主要问题。三维点云重建混凝土结构裂缝定位与跟追技术基于大数据技术，其可以通过无人机搭载的高分辨云台照相机获得目标图像集，然后通过优化数据集与三维重建流程，获得建筑结构点云模型并还原相机空间参数，最后通过视点定位算法，求得拍摄图片的坐标，并将图片与坐标相连接，以此将裂缝定位在点云模型中。检验表明，三维点云重建的模型能精准定位混凝土裂缝，并实现对裂缝的实时跟踪与观测。     

一种基于自适应Canny算子的舰船红外图像边缘检测方法

提出了一种用于舰船红外图像边缘检测的自适应Canny算子边缘检测方法。先利用图像的局部最大标准差值为参考值设定高斯滤波尺度参数,然后采用基于梯度幅度直方图的类间方差最大法计算Canny算子的高低闽值,从而实现边缘的自动检测。实验结果表明,采用该方法检测出的舰船边缘清晰连续,噪声较少,优于传统的Canny算子边缘检测方法,并且具有很好的工程适用性。     

An edge fusion scheme for image denoising based on anisotropic diffusion models

In this paper, we propose an enhanced anisotropic diffusion model. The improved model can classify finely image information as smooth regions, edges, corners and isolated noises by characteristic parameters and gradient variance parameter. And for different image information the eigenvalues of diffusion tensor are designed to conduct adaptive diffusion. Moreover, an edge fusion scheme is posed to preserve edges after denoising by combing different denoising and edge detection methods. Firstly, different denoising methods are applied for noisy image to obtain denoised images, and the best method among them is selected as main method. Then edge images of denoised images are obtained by edge detection methods. Finally, by fusing edge images together more integrated edges can be achieved to replace edges of denoised image obtained by main method. The experimental results show the proposed model can denoise meanwhile preserve edges and corners, and the edge fusion scheme is accurate and effective.     

Detection of edges from projections

In a number of applications of computerized tomography, the ultimate goal is to detect and characterize objects within a cross section. Detection of edges of different contrast regions yields the required information. The problem of detecting edges from projection data is addressed. It is shown that the class of linear edge detection operators used on images can be used for detection of edges directly from projection data. This not only reduces the computational burden but also avoids the difficulties of postprocessing a reconstructed image. This is accomplished by a convolution backprojection operation. For example, with the Marr-Hildreth edge detection operator, the filtering function that is to be used on the projection data is the Radon transform of the Laplacian of the 2-D Gaussian function which is combined with the reconstruction filter. Simulation results showing the efficacy of the proposed method and a comparison with edges detected from the reconstructed image are presented.     

基于特征的遥感图像自动配准算法

面向光学图像的多时相、多光谱、多传感器图像的自动配准,本文描述一个基于特征的高精度图像配准算法.它以点映射配准技术为基础,处理具有全局仿射几何失真的图像配准问题.首先,通过边缘检测和相应的后处理提取封闭边界;其次,根据边界链码相关和区域不变矩匹配策略建立边界的对应,并对对应重心即匹配点对进行一致性检测获得基元控制点;最后,估计初始变换参数,并通过显著点片的相关匹配来增加控制点个数,迭代修正变换参数以提高配准精度.多种遥感图像数据的配准实验和对比试验证实了的自动算法具有较高的可靠性和配准精度.     

基于FPGA的低照度图像采集与处理系统研究

针对低照度图像亮度低、噪声高和边缘模糊等问题,基于Xilinx公司的Artix-7系列FPGA芯片,通过驱动微光性能良好的XQE-1310图像传感器,对探测器采集的视频信号进行滤波和边缘检测,完成了低照度图像的采集和处理等一系列操作,最后将处理过的视频信号通过CameraLink视频格式实时显示,设计了一套微光夜视系统。实验结果表明,该系统的最低工作照度可达10－2 lx量级,滤波算法在保持图像边缘信息的同时有效滤除了图像中的椒盐噪声,自适应边缘检测算法可以根据照度水平实时调整阈值,凸显了低照度环境下物体的轮廓信息。系统充分利用了FPGA（Field Programmable Gate Array）速度快、效率高的优势,最后的成像结果清晰稳定,便于人眼观察。     

基于FE-Unet的机场道面裂缝检测

机场道面裂缝具有形态复杂多变、走向不连续、数据噪音多等特征,现有算法模型均未达到令人满意的结果。为了改善裂缝检测效果,本文提出了一种新的深度学习模型,命名为“FE-Unet”。该模型采用改进的残差连接方式,在解决多层网络下梯度的回传问题的同时起到细化特征以及整合通道的信息作用,提升了各阶段特征的区分度;此外,模型中的通道注意力模块(channel attention block, CAB)可以更好地提取判别特征,增强预测的一致性;最后,利用焦点损失(focal loss, FL)使模型专注于难分类的细小裂缝。实验中,以实际7 778张机场道面裂缝图像来训练模型,并在1 701张图像上进行验证。在与经典的全卷积神经网络(fully convolutional network, FCN)、DeepLab v3和Unet对比实验中,FE-Unet对裂缝、灌缝和板缝的检测性能均优于其他模型。其中,FE-Unet对裂缝检测的精度、召回率、F1值分别达到了80.31%、82.72%和81.49%。     

激光扫描混凝土裂缝的超声检测

为了探究激光扫描混凝土裂缝进行超声检测的可行性, 采用激光扫描混凝土表面激发声场、表面波探头接收超声信号的方法, 进行了相应的实验验证。通过对实验信号峰峰值和双极性特征分析, 实现了混凝土表面裂缝的定位。同时采用有限元方法模拟了扫描激光在材料表面激发声场的过程, 对超声信号进行了时域和频域上的分析。结果表明, 当激光与裂缝边沿距离在1mm时, 接收信号具有典型的双极性, 且峰峰值达到极大值; 当激光与裂缝边沿距离继续减小时, 峰峰值迅速降低。通过扫描激光激发超声, 在打点范围上形成由信号峰峰值表征的2维图像, 由峰峰值突变特征和信号的双极性特征, 可以实现混凝土表面缺陷边缘的定位。     

基于LSD和FLD融合的道路裂缝图像预处理方法研究

针对道路中的路标、路沿等直线类干扰物影响道路裂缝识别的问题,提出一种基于LSD(Line Segment Detector)和FLD(Fast Line Detector)融合的道路裂缝图像预处理方法。首先,基于LSD算法和FLD算法对裂缝图像进行直线检测,获取直线类干扰物的线段坐标值;其次,根据直线检测算法返回的线段坐标值进行断线重连,解决了直线检测算法提取线段不连续的问题;最后,根据线段重连后获取的直线类干扰物的掩膜图和裂缝图像原图,运用FMM(Fast Marching Method)图像修复算法达到消除直线类干扰物的目的。经过大量实验分析可得：该方法能够有效地消除裂缝图像中的直线类干扰物,使得裂缝检测的准确率提升了7.1%。     

Image super-resolution by curve fitting in the threshold decomposition domain

A new curve-fitting scheme is proposed in this paper to produce super-resolution images from a single low-resolution source image. The most unique feature of this method is that the threshold decomposition is performed on the given source image to obtain multiple binary images so that the curve-fitting applied on each resulted binary image can be made very efficient and accurate, thus allowing us to focus on tiny objects and thin structures so as to achieve rather nice visual results even when a large up-scaling factor is used. Two novel techniques are further proposed to improve the visual quality: (1) a spreading technique (applied on some significant pixels detected in each threshold decomposed binary image) is used to remove ladder-like false edges that often appear visually in super-resolution images, and (2) an edge correction (guided by the edge information extracted from the original source image) is used to sharpen all inherent edges. Our results are compared with those achieved by using the state-of-arts techniques, showing the ability of our algorithm to achieve a better visual quality in smooth areas as well as for sharp edges and small objects.     

基于圆拟合的非完整圆激光光斑中心检测算法

为了确定非均匀、非完整圆小孔激光光斑的精确中心,提出一种基于圆拟合的非完整圆小孔激光光斑中心检测新方法.该方法通过灰度形态学算法对激光光斑进行阈值分割,从水平和垂直两个方向检测粗略的激光光斑边缘,利用边界生长法消除比较短的圆弧和孤立的边缘点,通过反复迭代拟合获得最佳的圆轮廓并拟合出最终的标准圆.实验结果表明本方法精度明显优于传统的大型激光装置自动准直系统中的重心法,完全满足了该系统中对于非均匀、非完整圆小孔激光光斑中心求取的精度要求.     

应用特征点的彩色图像边缘检测算法

传统的边缘检测技主要通过全局图像扫描,寻找大于设定阈值的点,然后进行伪边缘点的筛除以及断裂边缘的连接。本文提出了一种基于图像特征点检测与边缘生长相结合的边缘检测算法。该方法将边缘看成一种特殊的区域,使用图像分割中区域生长的原理来生成边缘。首先在图像中寻找特征点作为边缘生长的种子点,然后以边缘梯度响应和区域相似度为生长规则,以层序遍历方式得到图像边缘。仿真结果显示,本文提出的算法可以减少参与比较的像素个数,去孤立的边缘,保证边缘的连续性和单一像素宽度。