基于形态学梯度的图像边缘检测算法

边缘检测是数字图像处理的一个重要内容,讨论了经典的边缘检测算子算法,该算法更多地采用Prewitt算子、LOG算子、Canny算子等在空域中进行。数学形态学在图像处理中有广泛的应用,其基本原理是基于利用结构元素去探测图像;在讨论常见数学形态学梯度的基础上,提出了一种基于形态学梯度的图像边缘检测算法,应用定义的形态学梯度结构检测出较理想的图像边缘信息。仿真结果表明,该算法在含噪图像中能得到较为理想的图像边缘信息,其抗噪声性能明显地优于经典的算子检测算法,在检测精度方面较经典的单一算子检测方法亦有一定的改善。     

基于形态学梯度的图像边缘检测算法

边缘检测是数字图像处理的一个重要内容，讨论了经典的边缘检测算子算法，该算法更多地采用Prewitt算子、LOG算子、Canny算子等在空域中进行。数学形态学在图像处理中有广泛的应用，其基本原理是基于利用结构元素去探测图像；在讨论常见数学形态学梯度的基础上，提出了一种基于形态学梯度的图像边缘检测算法，应用定义的形态学梯度结构检测出较理想的图像边缘信息。仿真结果表明，该算法在含噪图像中能得到较为理想的图像边缘信息，其抗噪声性能明显地优于经典的算子检测算法，在检测精度方面较经典的单一算子检测方法亦有一定的改善。     

高分辨率遥感影像的形态学边缘检测算法

结合高空间分辨率遥感影像的特点,利用数学形态学方法,选取具有不同尺度和包含全部方向的结构元素,设计一种多尺度全方位形态学边缘检测算法,并采用峰值信噪比方法代替均值以自适应地确定尺度权重.实验结果表明:与传统Canny算子和结构元素尺度形状单一的形态学边缘检测算法相比,本算法解决了噪声抑制和精细边缘提取的矛盾,检测出的图像边缘清晰,而且抗噪性能强.边缘检测结果可用于遥感影像地物几何特征和纹理特征提取.     

基于无人机航拍图像的车辆间距检测算法

针对车队行驶过程中车队管理者对前后车距信息了解不充分的问题,提出了一种基于无人机航拍图像的车辆间距检测算法。该算法首先对原始图像进行灰度处理,并进行高斯滤波去除一部分噪声;其次使用Canny算子边缘检测获取目标的边缘映射并寻找合适的轮廓;然后参考车辆目标和实际长度的大小来计算像素比,最后计算出车辆之间的距离。仿真实验结...     

基于柔性形态学的梯度边缘检测算法

在深入研究柔性数学形态学边缘检测算法的基础上,提出比传统柔性形态学膨胀和腐蚀算子具有更强鲁棒性的柔性形态学膨胀和腐蚀算子,在此基础上提出柔性形态学梯度边缘检测算法,实验证明了该算法对噪声特别是脉冲噪声有很强的抑制作用,并能很好地检测出图像的边缘信息。     

一种改进的形态学梯度边缘检测算法

数学形态学边缘检测器能克服线性边缘检测器的一些缺点,但是存在宽边缘,导致边缘分辨率较低。文章依据宽边缘形成机理,提出了一种从相互垂直方向分割形态学梯度图像,提取截面曲线局部极大值的边缘检测算法;给出了算法实现的详细步骤。实验表明该算法能提高形态学梯度边缘检测器的边缘分辨能力,并且能增强形态学边缘检测器的抗噪性能。     

彩色图像边缘检测算法

本文分析了常用的梯度算法,提出了针对灰度图像和彩色图像的改进思路,并编程实现这些算法。在RGB空间上实现的彩色图像边缘增强算法,可直接用于二值图像和灰度图像。改进后的算法简单易行,能有效地增强图像的边缘,具有较好的实用价值。     

一种基于数学形态学的遥感图象边缘检测算法

为有效提取边缘密度大的遥感图象中的边缘信息,该文提出了一种利用灰度形态变换原理进行检测的有效算法,首先利用开-闭形态滤波器降低输入图象的噪声,然后将二值图象的边缘提取算法推广到灰度图象中加以应用。实验结果证明,此方法优于灰度形态梯度法,同时也优于Canny算子。     

一种改进的数学形态学边缘检测算法

针对传统数学形态学边缘检测算法存在的边缘分辨率较低、低强度边缘保护能力较差等问题,提出一种改进的数学形态学边缘检测算法。该算法在保持传统形态学方法优点的基础上引入边缘方向信息,采用非极大值抑制方法对形态学梯度图像进行细化处理,利用新的基于方向的检测和连接方法从中提取边缘,既提高了检测出的边缘的分辨率,又实现了低强度边缘的保护,同时还获得了更强的抗噪声能力。与应用较为广泛的传统形态学方法及Canny边缘检测方法的比较研究也证明了该算法的有效性。     

基于自适应柔性形态学边缘检测算法研究

在深入研究柔性形态学边缘检测算法的基础上,提出自适应柔性形态学边缘检测算法,该算法是柔性形态学和自适应技术的结合.通过多方向结构元素的使用,取得其梯度的最大值,从而达到边缘检测较好的效果.通过比较移动结构元素窗口的信噪比和整个图像的信噪比,来实现该算法中自适应的特点.实验结果表明,该算法在边缘检测和抑制噪声方面,要比传统边缘检测算法有较好的效果,同时有较好的鲁棒性.     

基于强化底层特征的无人机航拍图像小目标检测算法

针对无人机航拍图像小目标检测整体精度低、漏检误检的问题,提出了一种新的基于强化底层特征的多尺度小目标检测方法。该方法以Faster R-CNN-ResNet-50-FPN为基础模型,首先,设计提出了新的DetNet-59特征提取网络;其次,设计了扁平的Flat-FPN特征融合网络来提高强化底层特征;最后通过引入soft-NMS解决小目标重叠问题。所提出的算法在VOC2007和VisDrone2019数据集上进行仿真实验测试,在时间消耗提升不大于2%的情况下,mAP较基础模型提高了约11%,并且检测精度也优于现阶段的常用算法。实验结果表明,该算法在保证实时性的同时可以有效提高小目标检测精度。     

Comparison of point matching algorithms for the UAV aerial refueling problem

This paper describes the results of a study focused on the evaluation of the performance of specific algorithms for the “point matching” task within the general problem of applying machine vision-based control laws for the problem of aerial refueling (AR) for UAVs. Two different point matching algorithms for the identification of the corner-points of the tanker are proposed. A detailed study of the algorithms is performed with special emphasis on the correct matching and required computational effort. The results show the importance of a correct point-matching scheme for obtaining accurate pose estimation; furthermore, the analysis highlights the tradeoffs involved in the selection of the appropriate algorithms.     

彩色图像对自适应匹配算法

提出了两种方法来提高图像匹配的精度。一种方法是改变自适应匹配窗口的选择方法,与原来的矩形窗口相比较,提出的窗口选取方法能在低纹理区域得到包含有效信息量更多、更接近实际边界的窗口。此外,通过降低边界点相似度在计算过程中的权重来降低边界点影响力,使得视差不连续区域的匹配精度得到提高。算法采用了Middlebury网站上提供的四幅立体图像对Tsukuba、Venus、Teddy和Cones分别进行实验验证。实验表明文中提出的两种方法对四幅图像的匹配精度均有所提高。     

Edge detection using ant algorithms

In this paper a new algorithm for edge detection using ant colony search is proposed. The problem is represented by a directed graph in which nodes are the pixels of an image. To adapt the problem, some modifications on original ant colony search algorithm (ACSA) are applied. A large number of experiments are employed to determine suitable algorithm parameters. We drive an experimental relationship between the size of the image to be analyzed and algorithm parameters. Several experiments are made and the results suggest the effectiveness of the proposed algorithm.     

基于HSL空间彩色图像多结构元形态边缘检测

构建了一类在HSL颜色空间基于多结构元彩色形态边缘梯度检测算法实现彩色图像边缘检测新算法,多结构元形态边缘检测有着比单一结构元素形态边缘检测更优越的性能。该方法是把RGB空间的彩色图像转换到HSL空间,并且定义了在HSL空间的彩色形态学基本算子,提出了改进的多结构元彩色形态边缘检测算法。经过大量实验证明,该算法在有噪声的干扰下,比传统的方法能够更好地抑制噪声并提取有用的图像边缘信息,能满足不同的应用需要。     

Edge detection in multispectral images

This paper proposes an extension of edge detectors based on second-order differential operators to the case of multiple band (color) images. To this end, we define a local directional measure of multispectral contrast. A definition of edge point is then given, as the location of a directional maximum of the contrast function. Extremal contrast edges are defined, as loci of zero crossing points of the first directional derivative of the contrast function. The qualitative properties of the so defined edges are briefly discussed. An algorithm for edge location with subpixel resolution is proposed, with an example of application to actual RGB imagery.     

基于均衡化及柔性形态学的显微图像边缘检测

对显微图像进行噪声过滤和增强是对其进行的分类、识别、检测处理的基础,在分析、综合传统的图像增强和图像分割的算法的基础上,将直方图变换和柔性数学形态学组合,提出了基于均衡化及柔性数学形态学的显微图像边缘检测方法,并通过实现表明该方法能够有效的抑制微生物显微图像的噪声,提高检测精度,保护边缘细节,并且易于编程实现.     

Edge detection and edge-preserved compression for error-diffused images

In this paper, a new approach to edge detection and image compression of bilevel error-diffused images is proposed. The proposed approach is directly applied to the error-diffused images without any inverse halftoning technique. The main idea behind the proposed edge detection method is to compute the consistency value of each pixel of the error-diffused image, and the computed values are then clustered into two classes to obtain the desired edges. Here, the consistency value is a function of the mass center and geometric center of the window; more precisely, it represents the possibility that the area covered by the window can be uniform. As for compression, each error-diffused input image is compressed by dividing the image into non-overlapping blocks with size 8 × 8, then each highly-consistent block is encoded by its average illumination, and each lowly-consistent block is transmitted directly using the original bitmap. The threshold to distinguish these two kinds of blocks is automatically calculated based on the compression rate required by the users. The complexity of our compression technique is low, and this fast method can be used in real-time application.     

Face detection in color images

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Based on a novel lighting compensation technique and a nonlinear color transformation, our method detects skin regions over the entire image and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful face detection over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from several photo collections (both indoors and outdoors)