Image edge detection based on nonsubsampled contourlet transform and mathematical morphology

A novel algorithm for image edge detection is presented. This algorithm combines the nonsubsampled contourlet transform and the mathematical morphology. First, the source image is decomposed by the nonsubsampled contourlet transform into multi-scale and multi-directional subbands. Then the edges in the high-frequency and low-frequency sub-bands are respectively extracted by the dualthreshold modulus maxima method and the mathematical morphology operator. Finally, the edges from the high- frequency and low-frequency sub-bands are integrated to the edges of the source image, which are refined, and isolated points are excluded to achieve the edges of the source image. The simulation results show that the proposed algorithm can effectively suppress noise, eliminate pseudo-edges and overcome the adverse effects caused by uneven illumination to a certain extent. Compared with the traditional methods such as LoG, Sobel, and Carmy operators and the modulus maxima algorithm, the proposed method can maintain sufficient positioning accuracy and edge details, and it can also make an improvement in the completeness, smoothness and clearness of the outline.

EDGE DETECTION METHOD OF REMOTE SENSING IMAGES BASED ON MATHEMATICAL MORPHOLOGY OF MULTI-STRUCTURE ELEMENTS

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise & remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.

An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

A novel mathematical morphology based algorithm for shoreline extraction from satellite images

Shoreline extraction is fundamental and inevitable for several studies.Ascertaining the precise spatial location of the shoreline is crucial.Recently,the need for using remote sensing data to accomplish the complex task of automatic extraction of features,such as shoreline,has considerably increased.Automated feature extraction can drastically minimize the time and cost of data acquisition and database updating.Effective and fast approaches are essential to monitor coastline retreat and update shoreline maps.Here,we present a flexible mathematical morphology-driven approach for shoreline extraction algorithm from satellite imageries.The salient features of this work are the preservation of actual size and shape of the shorelines,run-time structuring element definition,semi-automation,faster processing,and single band adaptability.The proposed approach is tested with various sensor-driven images with low to high resolutions.Accuracy of the developed methodology has been assessed with manually prepared ground truths of the study area and compared with an existing shoreline classification approach.The proposed approach is found successful in shoreline extraction from the wide variety of satellite images based on the results drawn from visual and quantitative assessments.

Edge detection on terahertz pulse imaging of dehydrated cutaneous malignant melanoma embedded in paraffin

Terahertz pulse imaging of cutaneous malignant melanoma dehydrated by ethanol and embedded in paraffin was carried out across a frequency range of 0.2- 1.4 THz. First, the tissue images based on the time-domain electric-field amplitude information were acquired. Then the areas of normal and cancerous tissues were determined using multi-scale, multi-azimuth and multi-structural element mathematical morphology. The physical meaning of the image was analyzed by calculation of the refractive index and absorption coefficient of cutaneous malignant melanoma in different areas. The refractive index of both normal and cancerous tissues showed anomalous dispersion. The refractive index of cancerous tissues tended to vary between 0.2 and 0.7 THz, while that of normal and fat tissues remain almost unchanged. The absorption of cancerous tissues was higher, with a maximum at 0.37 THz. We concluded that both the refractive index and absorption coefficient differ considerably between normal and cancerous tissues, and the areas of normal and abnormal tissues can be identified using THz pulse imaging combined with mathematical morphology. The method for edge detection of terahertz pulse imaging of cutaneous malignant melanoma provides a reference for the safe surgical removal of malignant tumors.

二值形态学边缘检测算法的量子实现及仿真

为进一步完善量子计算机图像边缘检测算法,结合IBM Q平台上的量子计算操作,提出了量子图像二值形态学边缘检测算法。在NEQR量子二值图像表示和量子二值腐蚀和膨胀操作的基础上,设计并实现了相应的二值形态学去噪处理量子电路和边缘提取量子电路。通过Qiskit扩展在虚拟本地量子模拟器上实现了8×8和128×128大小的量子图像二值形态学边缘检测,同时在IBM Q平台的真实云端量子模拟器上实现了8×8大小的量子图像对比仿真实验,实验结果证明了量子图像二值形态学边缘检测算法的可行性与有效性。

数学形态学在数字图像处理中的应用

阐述数学形态学的特点,数字图像的表达方式,数学形态学在数字图像处理中的应用,包括二值数学形态学、数字图像处理中的消除噪声、边缘检测、提取形态骨架、图像分割。

基于形态学和改进管道滤波的红外小目标检测

红外小目标的正确检测是图像处理领域中的一个重要课题,现有方法没有充分挖掘小目标的特征导致目标容易淹没在复杂的背景中.此外,目标的运动轨迹信息没有完全利用也使得目标和孤立噪声不易区分.为了解决该问题,设计了一种基于多属性形态学和改进管道滤波相结合的算法.对红外图像构建最大树,并通过面积、高度和对角线属性综合提取小目标的多种特征达到目标增强背景抑制的目的,融合多属性形态学得到的结果确定候选目标,通过改进的管道滤波利用目标运动轨迹的规律性进一步去除类目标噪声.将所提出的方法在4个数据集与4种方法进行比较,实验结果证明所提出的方法能在最大程度上抑制背景杂波的同时增强目标,对多种复杂场景下的不同类型、尺寸不一和亮度差异大的目标检测效果鲁棒.

基于分段图像识别的风电场异常运行数据清洗方法

风电场异常运行数据清洗对风电场功率预测、理论功率计算以及发电性能评估至关重要。针对现有数据清洗方法中基于统计特征的清洗算法无法有效清洗风功率曲线中部的堆积型异常数据,基于图像识别的直接清洗算法会忽略不同风速区间的像素点密度分布差异等问题,文章提出了基于分段图像识别的风电场异常运行数据清洗方法。首先,以切入风速和额定风速为数据集分段点,生成分段二值图像集,构建基于Canny边缘检测的分散型像素点辨识模型;然后,基于边缘内图像集,构建基于数学形态学的堆积型异常像素点辨识模型;最后,以中国西北地区某两个风电场的实际运行数据为算例,与四分位法、基于密度的空间聚类(DBSCAN)法、基于图像的直接清洗算法进行对比分析,验证了所提算法的有效性和适用性。所提方法采用数学形态学分割图像的主要部分和突起部分,从而有效辨识风功率曲线中部的堆积型异常数据;通过设置不同分段图像的清洗参数以改进图像直接清洗算法对局部细节的识别性能,清洗后的正常数据散点分布更为平滑。

基于图像处理的轨旁组合信号灯识别方法

对列车轨旁信号灯进行准确辨识是保障列车安全运行的重要措施。然而,对于轨道系统而言,除了通常的单色信号灯外,还有组合(两种颜色)信号灯的指示方式。针对轨旁组合信号灯的识别问题,文中提出了一种基于图像处理技术对位于列车前方150 m内的轨旁组合信号灯的识别方法。根据组合信号灯的颜色和形状特征,采用颜色分割、形态学处理、霍夫变换等手段提取组合信号灯的候选区域。同时利用组合信号灯位于当前轨道右侧的位置特征以及组合信号灯之间圆心距的距离特征,定位其位置并识别组合灯颜色。实验结果表明,该方法能准确定位和识别轨旁组合信号灯,对绿与黄、两绿和两黄这3种组合灯的识别率分别为94.14%、96.21%、86.67%。

基于机器视觉的圆斑状钢轨擦伤检测算法

针对现有基于灰度阈值的钢轨擦伤检测算法受光照等外部环境的影响较大的问题,本文提出了一种基于机器视觉的圆斑状钢轨擦伤检测算法。首先通过分析采集图像在垂直方向的灰度均值曲线,提取出钢轨顶面区域;然后运用边缘检测的方法得到擦伤区域边缘的候选像素点;最后运用形态学处理删除不属于擦伤区域的虚假边缘,确定钢轨擦伤区域的位置。用测试数据集对本文算法进行检测性能评测,并与基于灰度阈值的算法进行对比。结果表明:本文算法对圆斑状钢轨擦伤样本的检测准确率为96.4%,而基于灰度阈值的算法的检测准确率为86.8%,本文算法的检测准确率大幅提升,能够对钢轨擦伤进行有效检测。

图像边缘检测算法的Simulink实现及改进

图像边缘检测是数字图像处理中一个重要的课题。图像边缘检测的目的是找到图像变化强烈的像素点的集合、提取出图像中的重要信息。图像边缘检测在人工智能、图像识别、机器视觉与图像检测等方面有着广泛的应用。论文在Simulink中实现了Roberts、Prewitt、Sobel、Laplace边缘检测算法和高斯滤波算法。给出了使用Simulink进行算法设计与实现的框架。并且在Simulink中对图像边缘检测算法做出调整,提高图像边缘检测的效果。该算法可以利用Simulink与System Generator联合生成HDL IP核,后期在FPGA中直接调用IP核即可。这样可以简化FPGA开发流程,使工作重点放在算法的设计与实现上。

数学形态学在图像处理中的应用

介绍了数学形态学的基本运算及数学形态学在图像处理中的主要应用,并讨论了结构元素的选取方法。

一种新的红外热像仪图像边缘检测方法

针对红外热像仪采集的红外影像边缘信息模糊、影像存在噪声、边缘信息难提取的特点,提出了一种基于数学形态学对LOG算子改进和Roberts算子数据相结合的边缘检测新方法。该方法首先引进形态学中的开闭运算对具有随机噪声的红外影像进行滤波,接着运用拉普拉斯算法边缘检测,然后再采用Roberts算子提取边缘信息,建立相应的融合规则及阈值条件,将两种方法检测出的影像边缘信息融合,得到最终的融合影像。最后,对增加椒盐噪声的影像用MATLAB进行仿真实验,结果表明,该方法结合了两种检测算子的优点,定位精度高,有很强的抗噪性,获得了比较理想的检测效果。

基于数学形态学的图像边缘检测算法的研究

为了提高图像边缘检测的效率、降低噪声对图像边缘检测的影响,提出了一种基于数学形态学的图像边缘检测算法。该算法引入多元结构元素的概念,提出了一种改进的形态学边缘检测算子,能够有效地检测出带有噪声的图像边缘,并保持边缘的平滑性。实验结果表明,与传统边缘检测算子相比较,该算法抗噪声性能良好,实时性较好,具有一定的实用性和可行性。

基于数学形态学的车牌定位算法

车牌定位是汽车牌照识别系统的重要环节,直接影响车牌识别的准确率。利用Sobel算子对汽车图像进行边缘检测,并运用数学形态学的膨胀、区域填充以及腐蚀对二值图像进行处理,可以得到几个车牌候选区,然后利用面积、长宽比以及垂直投影特征值进行综合的分析方式,能准确定位车牌区域。实验结果表明,车牌定位准确率在95%以上,定位时间为0.15 s,并适用于不同环境。

数学形态学的细化算法

对目标图象进行细化处理，提取其骨架，在目标检测、图象编码以及光学字符识别等计算机视觉、图象处理与模式识别领域都有着广泛的应用．在定义了数学形态学图象轮廓和骨架的基础上，提出了基于两组结构元素模板的数学形态学细化新算法，这两组结构元素模板分别用来去掉目标的西北、东北、东南、西南方向四个角上的点和北、东、南、西四个方向上的点．用该算法对实际的字符进行细化，获得了令人满意的结果．

基于小波变换的红枣裂沟的多尺度边缘检测

红枣裂沟的检测是红枣外观品质实现自动评判的难点。为有效检测红枣裂沟,采用了基于小波变换的多尺度边缘检测和数学形态学相结合的方法,该方法具有良好的检测红枣图像局部突变的能力,还可以结合多尺度信息进行红枣裂沟的检测。该方法首先利用多尺度小波函数,对红枣图像进行处理得到灰度梯度局部极大值点,然后利用概率密度法或局部自适应法确定出低高阈值;并分别用低高阈值对局部极大值点进行分割,得到相应边缘点;最后通过数学形态学的连通方法和腐蚀运算得到检测结果。试验结果表明,采用基于小波变换的多尺度边缘检测和数学形态学相结合的方法检测红枣裂沟,可以得到更加连续、光滑(完整)、单像素宽的边缘链图像,提高了检测的有效性。

基于多结构元的噪声污染灰度图像边缘检测研究

提出了具有多结构元的灰度形态学边缘检测梯度算子 ,根据图像的几何特征 ,采用多结构元对图像细节进行匹配 ,既能提取精细的边缘 ,又能很好地抑制噪声 .