Novel Doppler Frequency Extraction Method Based on Time-Frequency Analysis and Morphological Operation

A novel method of Doppler frequency extraction is proposed for Doppler radar scoring systems. The idea is that the time-frequency map can show how the Doppler frequency varies along the time-line, so the Doppler frequency extraction becomes curve detection in the image-view. A set of morphological operations are used to implement curve detection. And a map fusion scheme is presented to eliminate the influence of strong direct current （DC） component of echo signal during curve detection. The radar real-life data are used to illustrate the performance of the new approach. Experimental results show that the proposed method can overcome the shortcomings of piecewise-processing-based FFT method and can improve the measuring precision of miss distance.

Automatic Extraction of Urban Road Centerlines from High-Resolution Satellite Imagery Using Automatic Thresholding and Morphological Operation Method

The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.

A new approach for classification of human brain CT images based on morphological operations

Automatic diagnosis may help to decrease human based diagnosis error and assist physicians to focus on the correct disease and its treatment and to avoid wasting time on diagnosis. In this paper computer aided diagnosis is applied to the brain CT image processing. We compared performance of morphological operations in extracting three types of features, i.e. gray scale, symmetry and texture. Some classifiers were applied to classify normal and abnormal brain CT images. It showed that morphological operations can improve the result of accuracy. Moreover SVM classifier showed better result than other classifiers.

A Novel Adaptive Approach to Process Binary Fingerprint Images Using Directional Morphological Operations

An adaptive algorithm for removing false ridges, bridges and filling gaps in binary fingerprint images based on morphological operations is presented. A novel procedure for structuring elements design based on the specific fingerprint characteristic is described. Using the images from FVC2000 database, we have compared our method proposed here with the approach proposed by other ones. The Experimental results have demonstrated the efficiency of our method.

AVariablebitrateVideoCodingSchemeBasedonMorphologicalOperationsforATMNetworks

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Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction.Precise segmentation of brain hemorrhage is crucial,so an enhanced segmentation is carried out in this research work.The brain image of various patients has taken using an MRI scanner by the utilization of T1,T2,and FLAIR sequence.This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet(multires UNet)through morphological operations.It is hard to precisely segment the brain lesions to extract the existing region of stroke.This crucial step is accomplished by this proposed MMU-Net methodology by precise segmentation of stroke lesions.The proposed method efficiently determines the hemorrhagic stroke with improved accuracy of 95%compared with the existing segmentation techniques such as U-net++,ResNet,Multires UNET and 3D-ResU-Net and also provides improved performance of 2D and 3D U-Net with an enhanced outcome.The performancemeasure of the proposed methodology acquires an improved accuracy,precision ratio,sensitivity,and specificity rate of 0.07%,0.04%,0.04%,and 0.05%in comparison to U-net,ResNet,Multires UNET and 3D-ResU-Net techniques respectively.

COLOR-SCALE MORPHOLOGICAL FILTERS WITH MULTIPLE STRUCTURING ELEMENTS

With the wide use of color in many areas, the interest on the color perception and processing has been growing rapidly. An important topic in color image processing is the development of efficient tools capable of filtering images without blurring them and without changing their original chromatic contents. In this paper, a new technique reducing noise of color image is developed. A class of color-scale morphological operations is introduced, which extend mathematical morphology to color image processing, representing a color image as a vector function. The correlation between color components is utilized to perform noise removal. Color-scale morphological niters with multiple structuring elements (CSMF-MSEs) are proposed. Their properties are discussed and proved. Experimental results show that CSMF-MSEs are suitable and powerful to eliminate noise and preserve edges in color image because of efficient utilization of inherent correlation between color components, and they perform better than vector

Extracting Moving Objects Based on Morphological Motion Filter

This paper presents a new approach to the extraction of a moving object from video sequence. The method is based on morphological motion filter using connected operator and a proposed new filtering criterion. The morphological motion filter aims to detect motion which is distinct from that of the background, and thereby locates independently moving physical objects in the scenes. Experiments show that the algorithm can extract object from moving backgrounds efficiently.

Some Problems of 2D Morphological and Median Filters

In this paper we mainly discussed some problems of 2D morpnological and median filters .The differences between 1D and 2D morphological and median filters arc also described. It can be seen that many propcrties of ID finers arc invalid for 2D filters. Som cxamples and cxpcriments are gived to show these problems.

A New Hybrid Order Approach to Morphological Color Image Processing Based on Reduced Order with Adaptive Absolute Reference

Mathematical morphology can process the binary and grayscale image successfully. This theory cannot be extended to the color image directly. In color space, a vector represents a pixel, so in order to compare vectors, vectoriel orderings must be defined first. This paper addresses the question of the extension of morphological operator to the case of color images. The proposed method used the order by bit mixing to replace the conditional order. Our order is based on a combination of reduced and bit mixing ordering of the underlying data. Additionally it is a total ordering. Since it not only solves the problems of false color generated by the marginal order but also those of multiple extrema generated by reduced order. The performance of the introduced operators is illustrated by means of different applications: color gradients for segmenting, image smoothing (noise suppression) by median filter operator and Laplacian operators. Examples of natural color images and synthetic color images are given. Experimental results show the improvement brought by this new method.

A Novel Approach for the Identification of Morphological Features from Low Quality Images

In this paper, a novel mathematical morphological approach is proposed, which is combined with an active threshold-based method for the identification of morphological features from images with poor qualities. The algorithm is very fast and needs low computing power.First, a mixed smooth filtering is designed to remove background noises. Second, an active threshold-based method is discussed to create a binary image to achieve rough segmentation. Third, some simple morphological operations, such as opening, closing, filling, and so on, are designed and applied to get the final result of segmentation. After morphological analysis, morphological features, such as contours, areas, numbers, locations, and so on, are obtained. Finally, the comparisons with other conventional methods validate the effectiveness, and an additional experimental result proves the repeatability of the proposed method.

Application of Opening and Closing Morphology in Deep Learning-Based Brain Image Registration

In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant for each label,which leads to the gradient variation con-centrating on the boundary.Thus,the dense deformation field(DDF)is gathered on the boundary and there even appears folding phenomenon.In order to fully leverage the label information,the morphological opening and closing information maps are introduced to enlarge the non-zero gradi-ent regions and improve the accuracy of DDF estimation.The opening information maps supervise the registration model to focus on smaller,narrow brain regions.The closing information maps supervise the registration model to pay more attention to the complex boundary region.Then,opening and closing morphology networks(OC_Net)are designed to automatically generate open-ing and closing information maps to realize the end-to-end training process.Finally,a new registra-tion architecture,VM_(seg+oc),is proposed by combining OC_Net and VoxelMorph.Experimental results show that the registration accuracy of VM_(seg+oc) is significantly improved on LPBA40 and OASIS1 datasets.Especially,VM_(seg+oc) can well improve registration accuracy in smaller brain regions and narrow regions.

一种联合空谱特征的沥青路面裂缝检测方法

文章提出了一种基于高光谱图像的沥青路面裂缝检测算法,旨在解决传统方法下裂缝检测精度低、误检较多的问题。本方法利用高光谱成像技术获取道路的空间-光谱信息,并通过裂缝与路面的光谱信息差异提取裂缝,同时利用裂缝的空间特征信息对目标进行阈值分割、形态学运算等以此来提取裂缝主干,进而联合空间-光谱特征检测结果进行数学运算以实现对沥青路面裂缝的精确检测。实验结果表明,与ACI、VIS2算法相比,本文算法的F1-score分别提升了14.11%和29.03%,证明了该方法的有效性,同时针对不同类型和尺寸的裂缝目标表现出了高检测精度、强抗干扰性和显著的鲁棒性。

基于改进Canny算子的管道GMAW熔池边缘检测

针对管道焊接过程中因受弧光、焊丝和电弧遮挡等多种因素的影响,致使熔池轮廓边缘难以准确提取的问题,提出一种改进传统Canny算子的熔池边缘检测算法。首先通过引导滤波代替高斯滤波对熔池图像进行保边去噪;其次在水平和竖直方向的检测基础上增加45°和135°对角线方向的梯度模板计算梯度分量;最后利用局部自适应阈值代替非极大值抑制技术的双阈值的全局固定方法完成熔池的边缘检测,同时结合形态学处理技术,提高边缘提取的抗噪性和自适应性。实验结果表明:通过不同的摆宽实验,将通过改进算法提取的熔池宽度与焊接完成后的焊缝宽度进行校验,提取的熔池宽度准确率大于95.56%,误差不超过0.3 mm,能满足熔化极气体保护焊(GMAW)熔池宽度测量精度的要求。

基于微根管技术的不同耕作方式下玉米根系生长动态监测

为探究东北黑土区从传统耕作转变为保护性耕作后玉米根系的变化特征,评价保护性耕作的适宜性及微根管技术的可靠性,采用微根管技术对免耕(NT)、旋耕(RT)和条耕(ST)等3种耕作方式下玉米根系进行原位监测,并与传统土钻法进行比较。结果表明,耕作方式对根系形态指标的影响随生育期进行逐渐由表层向深层土壤延伸,影响指标也由少到多。不同耕作方式下超过65%的玉米根系集中分布在0~30 cm土层。苗期以后,ST处理总根长、根长密度明显高于其他处理,但仅在成熟期时差异显著(P<0.05)。耕作方式对根系构型与分布的调控主要通过影响土壤剖面含水量和穿透阻力等来实现。整个土壤剖面ST处理平均含水量最高。0~15 cm土层NT处理土壤穿透阻力显著高于ST和RT处理,最高为1558.20 kPa,15~45 cm土层各处理土壤穿透阻力较为接近。微根管法与土钻法测得的根长密度相对误差基本在10.0%以内。本试验条件下,耕作方式转变可以显著影响根表面积、体积、直径、根长密度等根系特征,土壤水分状况和穿透阻力对作物根系生长具有较大影响。利用微根管法获得的根长密度与土钻法所得结果具有较好的相关性,2种方法相结合在今后作物根系生长动态监测中具有重要推广应用价值。

聚焦于碰摩诱导的非规则形貌套管磨损分析

由于钻磨铣作业管柱刚度低且受力情况复杂,发生涡动时不可避免会碰摩套管,诱导套管产生不连续、随机的磨损,降低井筒安全性。为研究碰摩磨损对套管安全性的影响规律,基于转子动力学理论建立了钻磨铣作业钻柱有限元动力学模型,得到钻柱涡动轨迹及钻柱涡动诱发的套管碰摩特征;同时建立了碰摩磨损体积计算模型,考察转速、钻压对碰摩诱导的套管磨损的影响规律;将碰摩磨损缺陷引入套管模型,采用应力集中比值分析碰摩磨损缺陷对套管承载性能的影响规律。研究结果表明:钻磨铣作业期间钻柱对套管的碰摩主要发生在下部钻具组合处,表现为不连续性碰摩和连续性碰摩2种形式;转速、钻压与碰摩磨损程度呈正相关关系;碰摩磨损缺陷的偏磨率、开口大小及数量会对套管承载性能产生不同的影响。研究结果可为由碰摩诱导产生的套管磨损分析及井筒完整性评价提供理论依据。

A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

基于连通性阈值分割的煤岩裂隙识别方法

煤岩裂隙发育形态是影响煤岩渗透性、决定煤岩体力学特征的重要因素。针对煤岩裂隙识别过程中存在的复杂结构处理不当、裂隙边界特征保留不足、噪声干扰等问题,提出了一种基于连通性阈值分割的煤岩裂隙识别方法。首先,采用直方图均衡化增强算法和非局部均值滤波去噪算法对图像进行预处理,其次,利用自适应Otsu阈值分割确定预处理后图像的阈值,识别出可能的裂隙区域,应用形态学运算对这些区域进行精细化处理,进一步突出裂隙的边界特征。然后,通过Canny边缘计算提取种子点,以识别图像中的关键特征。最后,基于这些种子点进行区域生长操作,从而有效抑制噪声,并在平滑图像裂隙的同时更加清晰地突出裂隙信息。实验结果表明:①连通性阈值分割的均方误差较自适应Otsu阈值分割和自适应阈值分割分别平均减少了7.20,7.10 dB,连通性阈值分割的峰值信噪比较自适应Otsu阈值分割和自适应阈值分割分别平均提高了0.60,0.59 dB。②连通性阈值分割不仅有效解决了裂隙提取不明显、末端提取效果差及连接处特征消失的问题,而且显著减少了噪声的干扰,使裂隙特征变得更加突出,从而极大地提高了裂隙识别的准确性和完整性。③连通性阈值分割在自适应Otsu阈值分割的基础上,强化了裂隙特征并有效消除了噪声点,平均准确率较自适应阈值分割算法和自适应Otsu阈值分割分别提高了8%和0.8%,达98.9%。

改进的车牌校正与识别算法

为解决车牌存在图像背景噪声较大和车牌倾斜校正精度的问题,本文提出基于OpenCV的车牌校正与识别算法。基于形态学运算,对输入的图像进行车牌定位,并通过霍夫变换和透视变换进行倾斜校正,然后进行车牌字符分割,采取模板匹配技术对分割后的字符逐一进行比对识别,输出识别得到的车牌信息。实践结果显示了本文算法的系统性和实用性。