一种基于形态学的多结构元素多尺度图像边缘检测方法

提出了一种基于数学形态学算子的多结构元素多尺度边缘检测方法,首先构造6种具有代表性的结构元素,用改进的形态学边缘检测算子对灰度图像进行边缘检测,然后改变结构元素的尺寸大小得到多尺度下的边缘图像,根据不同尺度抗噪性能不同来确定权值,将不同尺度下的边缘图像合成,得到在噪声存在条件下较为理想的图像边缘,实验结果表明,与传统边缘检测算法相比,该算法在保持图像边缘清晰的同时,有很强的去除噪声能力,具有一定的实用性和可行性.     

基于数学形态学的灰度图像车牌字符提取算法

在深入研究数学形态学的基础上,提取了一种灰度图像车牌字符提取算法。该算法构造4种结构元素,采用数学形态学的膨胀和腐蚀算子依次求出梯度算子对车牌字符进行边缘检测处理和迭代阈值分割,最后结合数学形态学的区域填充方法弥合字符中的空隙。与传统边缘检测算子相比较,实验结果表明该算法具有较强的提取字符能力和良好的抗噪能力,并保护了字符的边缘。其计算量小,在保证处理效果的同时,保证了处理速度,具有一定的可用性和可行性。     

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。     

基于多尺度多方向结构元素的形态学图像边缘检测算法

为进一步改善图像处理中的噪声抑制和边缘检测性能,提出了一种多尺度多方向结构元素形态学图像边缘检测算法.该算法基于数学形态学中结构元素的方向性差异,充分利用了腐蚀、膨胀、开、闭及其变换和组合运算.对图像进行去噪、边缘提取等预处理操作,以提高图像的信噪比和边缘细节;利用递归的多尺度多方向结构元素形态学滤波得到图像的初始轮廓;利用多尺度形态学和多方向结构元素进行图像边缘检测.实验结果表明提出的算法抗噪性强,能有效准确地提取边缘信息.     

基于多尺度多结构形态学火焰图像边缘检测

针对从工业锅炉采集到的火焰图像边缘形态和噪声的不同,以数学形态学梯度边缘检测算子为基础,结合多结构元素和多尺度的特性,提出了一种基于多尺度多结构数学形态学图像边缘检测算法,并将其应用于火焰图像的边缘检测。仿真结果表明,与经典的Sobel算子、Canny算子和传统的算法相比,提出的算法具有边缘定位准确、轮廓清晰、图像细节保留较多、噪声不敏感等显著优点。     

Flame Image Edge Detection Based on Multi- scale and Multi-strncturing Elements Morphology

针对从工业锅炉采集到的火焰图像边缘形态和噪声的不同,以数学形态学梯度边缘检测算子为基础,结合多结构元素和多尺度的特性,提出了一种基于多尺度多结构数学形态学图像边缘检测算法,并将其应用于火焰图像的边缘检测.仿真结果表明,与经典的Sobel算子、Canny算子和传统的算法相比,提出的算法具有边缘定位准确、轮廓清晰、图像细节保留较多、噪声不敏感等显著优点.     

基于融合技术的图像边缘检测方法

提出一种融合柔性形态学方法与Sobel算子的边缘检测方法。利用全方位结构元素对图像进行重复柔性形态学处理，提高了运算速度并抑制了大量的噪声。建立相应的融合原则，对形态学运算获得的边缘和Sobel算子检测的边缘进行融合，得到清晰、连续的边缘图像。该方法对噪声具有很好的鲁棒性，处理含噪声图像能得到准确可靠的边缘检测结果。     

基于改进的形态学灰度图像边缘检测算法

针对传统形态学在边缘检测算法上存在的检测边缘错位、较粗等缺点,在传统形态学的基础上,提出了一种具有方向估计性的梯度算子。该算法通过改进的梯度算子及多结构元素模式来获取梯度方向,并沿边缘梯度方向进行非极大值抑制,以此细化边缘宽度,最终获取图像边缘。实验结果表明,相对传统形态学边缘检测算法,该算法具有更精细的检测结果与较强的抗噪能力。     

量子启发数学形态学的研究

本文在量子信息处理技术的启发下提出一种新的数学形态学结构元素,该结构元素不是传统的结构元素的简单线性组合,而是一种类似于量子叠加态的多态结构元素.利用此结构元素构成的形态学基本算子被定义,其运算意义被解释,并指出在特定的情况下,该算子对数字图像的处理结果能退化到对应的传统形态学算子的处理结果.本文还给出一个利用这种叠加态结构元素进行二值图像边缘检测的实例,并将其检测结果与对应的传统处理结果进行了比较.     

多尺度形态学边缘检测算法

形态学作为一门新兴的非线性图像处理技术,已得到人们的广泛关注,并应用于图像处理的许多方面。文中提出了一种结合形态学滤波的边缘检测算法,该算法采用自适应方法确定锥形结构元素,然后利用双锥形结构元素对图像进行形态学迭代滤波,对滤波后的图像进行多尺度形态学边缘检测,并将各尺度下边缘检测的结果进行合成,最终得到在噪声存在条件下较为理想的图像边缘,并且与其它边缘检测算法进行了对比实验,实验验证了该算法的可行性和有效性。     

脑外科CT图像的综合边缘提取算法

为了实现大脑的三维重建,准确提取二维图像中的边缘成了首要解决的问题。常用的几种经典边缘检测算子对噪声比较敏感,容易造成虚假或不连续的边缘。针对此问题,提出基于Canny算子并结合图像增强和数学形态学的综合边缘提取算法。该算法首先对原始图像进行增强,以便于计算机的分析;然后利用Canny算子对CT图片进行边缘提取,该算子...     

Boundary detection using simulation of particle motion in a vectorimage field

This paper introduces a novel approach in image processing based on a vector image model. A major advantage of the model is that it allows vector operations to be performed on an image. An example of a vector operation is the computation of mechanical moments for detecting inhomogeneities in an object or equivalently edges in an image. A new edge operator derived from a vector image model yields an edge vector field analogous to the Hamiltonian gradient field of the image. The distinct feature of the edge vector field is that edge vectors form current loops encompassing the objects. This feature is exploited to develop a new boundary extraction algorithm based on particle motion in a force field. The edge vector field forces a particle to move along the edges while an orthogonal normalized Laplacian gradient vector field guarantees that the particle will not drift away from the edges. The object boundary can be obtained from the convergent path of the particle trajectory. Using a fine stepping factor, the extracted boundary can achieve subpixel accuracy. The proposed algorithm has major advantages over the conventional edge-detection, edge-thinning, and edge-linking techniques in that it effectively utilizes both direction and magnitude of edges. The algorithm is simple, robust and performs very well even on high curvature objects.     

复杂光照条件下的图像边缘提取算法的改进

边缘是数字图像中的一个重要的局部特征。在复杂光照条件的情况下,图像分辨率较低,传统Sobel算子利用卷积计算出来的灰度梯度较为平缓,边缘不能有效的识别。通过改进Sobel算子的模板,平衡图像的亮边缘与暗边缘,尽可能地避免暗边缘信息的丢失。与此同时,通过分析数据建立相应的数学模型,优化子区域大小,把图像划分为多个子区域,在子区域内利用大津法进行边缘的检测与提取,最后使用Hilditch细化算法将图像的边缘提取出来。通过MATLAB仿真结果表明,与传统So bel算子的边缘提取算法相比,文中算法简单易实现,对于复杂光照条件下的图像,边缘信息提取更完整,自适应能力更强。     

一种飞行器结构特征提取与识别的方法

提出了一种基于SUSAN算法、灰色系统理论和数学形态学结合的飞行器结构特征提取与识别的方法.首先利用SUSAN算法从背景中提取飞行器目标的结构边缘信息, 通过灰度的窗口变换和图像相加法实现目标增强;然后用灰色系统理论检测出飞行器目标的结构特征边缘,同时利用形态学算子对飞行器进行闭运算,利用种子填充算法识别出飞行器目标区域.实验结果表明:该方法对于飞行器目标的跟踪、结构特征提取以及事后判读有重要的意义,同时验证了该方法的可行性.     

Edge-guided boundary delineation in prostate ultrasound images

Accurate detection of prostate boundaries is required in many diagnostic and treatment procedures for prostate disease. In this paper, a new paradigm for guided edge delineation is described, which involves presenting automatically detected prostate edges as a visual guide to the observer, followed by manual editing. This approach enables robust delineation of the prostate boundaries, making it suitable for routine clinical use. The edge-detection algorithm is comprised of three stages. An algorithm called sticks is used to enhance contrast and at the same time reduce speckle in the transrectal ultrasound prostate image. The resulting image is further smoothed using an anisotropic diffusion filter. In the third stage, some basic prior knowledge of the prostate, such as shape and echo pattern, is used to detect the most probable edges describing the prostate. Finally, patient-specific anatomic information is integrated during manual linking of the detected edges. The algorithm was tested on 125 images from 16 patients. The performance of the algorithm was statistically evaluated by employing five expert observers. Based on this study, we found that consistency in prostate delineation increases when automatically detected edges are used as visual guide during outlining, while the accuracy of the detected edges was found to be at least as good as those of the human observers. The use of edge guidance for boundary delineation can also be extended to other applications in medical imaging where poor contrast in the images and the complexity in the anatomy limit the clinical usability of fully automatic edge-detection techniques.     

基于数学形态学和LMS滤波的图像边缘检测算法研究

传统的边缘检测算子对灰度图像进行边缘检测时存在图像细节被丢失,边界不连续等问题。针对上述问题,提出一种基于数学形态学和最小均方差滤波相结合的图像边缘检测方法,该算法先利用小均方差滤波的方法可以有效地滤除图像中的噪声,然后利用形态学中的腐蚀运算对图像进行边缘检测处理。实验结果表明：该方法能够有效地去噪,精确地检测图像中的细节,并且边界的连续性好。     

Edge detection in ultrasound imagery using the instantaneous coefficient of variation

The instantaneous coefficient of variation (ICOV) edge detector, based on normalized gradient and Laplacian operators, has been proposed for edge detection in ultrasound images. In this paper, the edge detection and localization performance of the ICOV-squared (ICOVS) detector are examined. First, a simplified version of the ICOVS detector, the normalized gradient magnitude squared, is scrutinized in order to reveal the statistical performance of edge detection and localization in speckled ultrasound imagery. Both the probability of detection and the probability of false alarm are evaluated for the detector. Edge localization is characterized by the position of the peak and the 3-dB width of the detector response. Then, the speckle-edge response of the ICOVS as applied to a realistic edge model is studied. Through theoretical analysis, we reveal the compensatory effects of the normalized Laplacian operator in the ICOV edge detector for edge-localization error. An ICOV-based edge-detection algorithm is implemented in which the ICOV detector is embedded in a diffusion coefficient in an anisotropic diffusion process. Experiments with real ultrasound images have shown that the proposed algorithm is effective in extracting edges in the presence of speckle. Quantitatively, the ICOVS provides a lower localization error, and qualitatively, a dramatic improvement in edge-detection performance over an existing edge-detection method for speckled imagery.     

基于形态学的人脸图像边缘检测新算法

提出一种利用数学形态学开闭运算--首先滤掉噪声,再使用数学形态学梯度提取边缘的人脸图像边缘检测新算法.仿真结果表明,该方法与传统的边缘检测方法相比,能在滤除噪声的同时很好地检测到人脸图像的有用边缘.     

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.