Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map

A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.

Short Report: A Sponge Phantom Provides a Homogeneous k-Space Pattern at MRI

Background: The spectral coverage of magnetic resonance (MR) sequences can be well assessed in k-space. However, many objects do not provide high signal intensities in the peripheral k-space. Purpose: To experimentally find a phantom that provides a homogeneous spectral pattern also at the high spatial frequencies of the k-space periphery. Material and Methods: Different phantoms were imaged on a 1.5 Tesla magnet, and the resulting MR images were viewed in k-space after fast Fourier transform. Results: Firstly, phantoms with a homogeneous physical structure were studied with a T2-weighted MR sequence, but they provided an inhomogeneous k-space pattern with dominant central low-frequency components. Secondly, phantoms with an inhomogeneous physical structure were studied. In this group, a water-soaked sponge showed a relatively homogeneous k-space pattern also at high spatial frequencies, owing to the fine porous structure. This sponge phantom can also be soaked with Gadolinium chelates for T1-weighted MR imaging. Conclusion: A simple sponge phantom provides a homogeneous k-space pattern, owing to its fine porous structure. This could be utilized in MR sequence development and for viewing the spectral coverage of MR sequences in k-space.

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.

Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.

RESEARCH ON SATELLITE IMAGE PROCESSING AND RECOGNITION WITH PARALLEL ALGORITHM

Using the method of mathematical morphology,this paper fulfills filtration,segmentation and extraction of morphological features of the satellite cloud image.It also gives out the relative algorithms,which is realized by parallel C programming based on Transputer networks.It has been successfully used to process the typhoon and the low tornado cloud image.And it will be used in weather forecast.

Processing of Laser Scanner Data and Extraction of Structure Lines Using Methods of the Image Processing

激光扫描数据提供了一种新的手段用于获取高精度的数字地形表面模型。原始的航空激光扫描数据表达的是一些非规则分布的“点云” ,这些非规则分布的点需要进行有效的事后处理。这种事后处理有 2个目的 :一是将那些分布在地表面上的点 (即地面点 )与分布在非地表面上的点 (譬如树木、房屋或汽车上的点 ,即非地面点 )进行有效的分离 ;二是从分离后的地面点中提取结构线 ,用于建立高精度的数字地面模型。作者发展了一系列的基于数字形态学理论和稳健参数估计理论的方法用于分离和探测地面点。这里所介绍和开发的提取结构线的算法建立在数字图像处理和表面曲率理论的基础上。这些算法同样可以扩展地用于其他领域。所介绍的基于数字图像处理理论处理原始的航空激光扫瞄数据和提取结构线的方法取得了很好的结果。

Object Extraction Based on Evolutionary Morphological Processing

This paper introduces a novel technique for object detection using genetic algorithms and morphological processing. The method employs a kind of object oriented structure element, which is derived by genetic algorithms. The population of morphological filters is iteratively evaluated according to a statistical performance index corresponding to object extraction ability, and evolves into an optimal structuring element using the evolution principles of genetic search. Experimental results of road extraction from high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.

A method of remote sensing image water segmentation based on adaptive morphological elliptical structuring elements

The use of visible and infrared remote sensing images to calculate the water area is an effective means to grasp the basic situation of water resources,and water segmentation is the premise of statistics.Generally,the edge features of the water in the remote sensing images are complex.When the traditional morphology is used for image segmentation,it is easy to change the image edge and affect the accuracy of image segmentation because the fixed structuring elements are used to perform morphological operations on the image.To segment water in the remote sensing image accurately,a remote sensing image water segmentation method based on adaptive morphological elliptical structuring elements is proposed.Firstly,the eigenvalue and eigenvector of the image are estimated by linear structure tensor,and the elliptical structuring elements are constructed by the eigenvalue and eigenvector.Then adaptive morphological operations are defined,combining the close operation to eliminate the influence of dark detail noise on water without overstretching the water edge,so that the water edge can be maintained more accurately.Finally,on this basis,the water area can be segmented by gray slice.The experimental results show that the proposed method has higher segmentation accuracy and the average segmentation error is less than 1.43%.

Weld pool vision sensing and image processing for GMAW

It is difficult to acquire satisfied weld pool image by CCD sensor during gas metal arc welding（ GMAW） , for arc disturbs violently, welding current is great and working frequency is high. By using CMOS vision sensor to GMA W process, the vivid weld pool image is collected at any time, furthermore, whose gray compression ratio is controllable by sensor hardware circuit developed. Acquired weld pool image is firstly pre-processed by using Wiener filter and Ostu threshold segmentation algorithm. Subsequently separation between weld pool image and cathode mist region is conducted by means of mathematical morphological algorithm, and the edge of weld pool image is extracted by using Prewitt algorithm.

基于深度融合的全自动石英坩埚气泡检测方法

针对石英坩埚透明层中气泡检测依赖于人工肉眼检测,存在着效率低、准确性不高的问题,提出了一种基于深度融合气泡检测算法(DFBDA)。首先,利用智能机械臂夹持工业显微镜,自动采集石英坩埚透明层的气泡视频图像;然后,使用融合形态学变换的气泡分割和轮廓提取算法,以确保对气泡的准确分割;最后,通过深度融合方法对多帧相似气泡进行融合,实现了对气泡准确位置的三维重建。针对不同坩埚样品的测试验证,实验结果表明,方法对气泡的平均识别准确率达到了98.7%,速度和精度都满足工业需求。

用扫描仪及Image J软件精确测量叶片形态数量特征的方法

传统的纸样称重法用来测量离体叶片的面积,烦琐、耗时、精度不高。为寻求一种适合的方法,我们对离体叶片采用扫描仪获取叶片的数字图像,利用Image J软件测量叶片的长、宽、周长、面积及叶柄的长,并与传统的纸样称重测定叶面积法进行比较。结果表明,此方法具有低成本、快速、精确等特点,适用于植物形态学、植物生理生态学、林学及农业等对叶片形态特征的测量研究工作。

Application of Morphological Filter in Rosette Scanning Sub-Imaging System

To restore the sub image in a rosette scanning system and provide target recognition system with a low distorted image, the sub image is processed with morphological filters. Morphological filter can process rosette scanning sub images more effectively. It can restore the original area and shape of an object effectively, and keep the energy information of the object. To process sub images got by a rosette scanning system, morphological filter is more effective than traditional low pass filter.

CT图像后处理方法对颅内动脉瘤形态学参数测量准确性的影响

目的:以三维数字减影血管造影(three-dimensional digital subtraction angiography,3D-DSA)为“金标准”,基于CT血管成像(CT angiography,CTA)对比最大密度投影(maximum intensity projection,MIP)联合多平面重组(multiplanar reconstruction,MPR)和减影的容积再现(volume rendering,VR)这2种图像后处理方法之间颅内动脉瘤形态学参数测量结果的差异。方法:回顾性分析重庆医科大学附属第一医院92例(共116个颅内未破裂动脉瘤)患者的头颈CTA和DSA影像资料。使用双盲法分别在3D-DSA的VR、CTA的MIP-MPR和减影VR图像上测量动脉瘤的形态学参数,测量2次取均值,并计算相关的形态学衍生指标。使用组内相关系数(intraclass correlation coefficient,ICC)比较观察者间测量值的一致性,利用重复测量的方差分析或Friedman秩和检验比较不同后处理图像上测量结果的差异,后续组间两两比较采用Bonferroni校正。采用卡方检验对比各组小、微小动脉瘤及宽颈动脉瘤的分布情况。结果:基于MIP-MPR、减影VR和3D-DSA测得的动脉瘤最大径、瘤宽和瘤高在三组间整体比较时差异无统计学意义(χ^(2)值分别为4.484、5.808、4.468,均P>0.05);而得到的颈宽、载瘤动脉的平均直径在三组间整体比较时差异有统计学意义(χ^(2)值分别为48.341、87.948,均P<0.001);进行后续的组间两两比较时结果存在差异,且这种差异在颈内动脉虹吸部与非虹吸部2个亚组内又有一定区别。由MIP-MPR得出的形态学衍生指标均偏大,与DSA相比差异均有统计学意义(P<0.017),除高宽比外,与减影VR相比差异也有统计学意义(P<0.017)。基于减影VR进行动脉瘤的形态学评估,仅载瘤动脉的平均直径和大小比与DSA相比差异有统计学意义(P<0.017),但载瘤动脉的平均直径测量差异在亚组分析时不明显(P>0.017);其余参数与DSA相比差异均无统计学意义(P>0.017)。结论:减影VR上颅内动脉瘤形态学参数测量的准确性优于MIP-MPR,更适用于颈内动脉虹吸部动脉瘤的形态学评估。

基于Halcon的金属软管断丝缺陷检测方法

针对现有的金属软管网套钢丝断丝破坏检测过程中的人工检测时肉眼难以识别、长时间工作注意力下降影响检测效率与准确度等问题,设计一种基于Halcon的金属软管外网套断丝缺陷检测系统。确定图像的光照方式,利用相机拍摄到有利于分析处理的图像效果;在Halcon中首先对图像进行预处理,然后对图像进行阈值分割并提取出表面断丝缺陷,将破损部位的形状转化为规则形状便于测量,最后对破损位置的尺寸以及软管尺寸进行测量并将数值显示在图框中。实验结果表明:该方法能够清晰地检测出金属软管的破损位置并进行尺寸测量,通过对5种直径不同且缺陷位置与大小均不同的金属软管断丝缺陷部分进行测量可知,采用该方法进行测量时所产生的最大误差为0.22 mm,优于断丝检测精度(0.5 mm),符合检测需求。

基于形态学的自动铺丝纤维铺放准确性检测

自动铺丝质量检查既耗时又不充分,缺陷对比度低。对此,根据铺丝过程中的热特性,搭建丝束铺放准确性检测平台,结合形态学算法对缺陷红外图像特征进行纤维铺放准确性检测。首先进行形状相同尺度不同的结构元素的顶帽变换,然后提取每一组尺度下多尺度亮暗区域和相邻组尺度间的多尺度亮暗细节来增强图像;其次使用了多方向多尺度的结构元素和系数自调节的形态学边缘检测算法来获得丝束边缘位置。实验结果表明,该方法在减少图像噪声和增强图像的对比度之上平衡了边缘检测精度与抗噪性能之间的协调问题,有效检测了纤维铺放准确性,最大铺放误差不超过4.53%。

影院票务辅助系统中座位信息的自动提取

在电影院的票务系统中,一般分为线上售票和线下售票两种方式。目前线上售票有成熟的软件工具,用户可以通过手机或电脑在线上自动选择各种类型的座位信息。但在线下售票中,操作人员只能通过人工观察票务系统软件的屏幕界面,人工获取某个场次的不同类型的空余座位信息,然后再手工输入空余的座位信息到出票系统,打印纸质电影票后,然后进行售票。针对线下售票需要人工操作这种不方便不快捷的情况,作者利用C++语言,基于OpenCV图像处理库,首先快速读取票务系统软件的界面图片的彩色图像,将三通道的彩色图像转化为单通道的灰度图像。利用findContours()函数获取图像的轮廓集,处理掉缺失和过小的轮廓后,通过外接矩形来获取轮廓的位置,将轮廓和外接矩形作为属性初始化对应的每个座位的实例化对象。通过读取各个对象的矩形内的像素点信息,可以确定对象的颜色以及位置。实现了对屏幕中各种不同类型的座位信息的自动识别与定位,准确率为100%;然后,自动输入到出票系统,自动打印出纸质电影票,达到了自动线下售票的目的。开发此系统使用的技术方法,对于需要识别第三方软件界面的屏幕信息及其变化的场景,如:监控系统屏幕的变化,有一定的借鉴意义。

MRI Brain Tumor Segmentation with Intuitionist Possibilistic Fuzzy Clustering and Morphological Operations

Digital Image Processing(DIP)is a well-developed field in the biological sciences which involves classification and detection of tumour.In medical science,automatic brain tumor diagnosis is an important phase.Brain tumor detection is performed by Computer-Aided Diagnosis(CAD)systems.The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes.Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research.Brain tumor diagnosis mainly performed for obtaining exact location,orientation and area of abnormal tissues.Cancer and edema regions inference from brain magnetic resonance imaging(MRI)information is considered to be great challenge due to brain tumors complex structure,blurred borders,besides exterior features like noise.The noise compassion is mainly reduced along with segmentation stability by suggesting efficient hybrid clustering method merged with morphological process for brain cancer segmentation.Combined form of Median Modified Wiener filter(CMMWF)is chiefly deployed for denoising,and morphological operations which in turn eliminate nonbrain tissue,efficiently dropping technique’s sensitivity to noise.The proposed system contains the main phases such as preprocessing,brain tumor extraction and post processing.Image segmentation is greatly achieved by presenting Intuitionist Possibilistic Fuzzy Clustering(IPFC)algorithm.The algorithm’s stability is greatly enhanced by this clustering along with clustering parameters sensitivity reduction.Then,the post processing of images are done through morphological operations along with Hybrid Median filtering(HMF)for attaining exact tumors representations.Additionally,suggested algorithm is substantiated by comparing with other existing segmentation algorithms.The outcomes reveal that suggested algorithm achieves improved outcomes pertaining to accuracy,sensitivity,specificity,and recall.

基于Tesseract_OCR的藏文手写乌金体研究

目前,关于藏文手写乌金体识别的研究较为有限,因此研究藏文手写乌金体识别系统有一定意义。文章的主要研究内容包括利用Tesseract_OCR识别引擎来实现对藏文手写乌金体的自动识别以及相关字库的训练。同时,本文使用Matlab编程语言来高效处理数据集,并采用Python编程语言构建了一个藏文手写乌金体识别系统。实验结果表明,通过采用本系统的方法,显著提升了Tesseract_OCR对藏文手写乌金体的识别准确度以及字库的质量。这项研究不仅对于藏文手写体识别技术的发展具有重要意义,还为保护和传承乌金体文化遗产提供了有力的工具。未来的研究将继续探索基于Tesseract_OCR的藏文手写体识别方法,并进一步优化系统以提高识别性能。

SADDLE-POINT BASED SEPARATION OF TOUCHED OBJECTS IN 2-D IMAGE

In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish these features among the objects already separated from each other. The problems will be undoubtedly more complex and of greater challenge if the objects are touched or/and overlapped. This letter presents an algorithm that can be used to separate the touches and overlaps existing in the objects within a 2-D image. The approach is first to convert the gray-scale image to its corresponding binary one and then to the 3-D topographic one using the erosion operations. A template (or mask) is engineered to search the topographic surface for the saddle point, from which the segmenting orientation is determined followed by the desired separating operation. The algorithm is tested on a real image and the running result is adequately satisfying and encouraging.

Fuzzy color morphological sieves

To solve the problems that exist in the color morphological sieves, a new fuzzy color morphological sieve is proposed.The method adopts color fuzzy model to define extrema and selects more rational regional merging way to produce better results.It can deal with the maxima or the minima areas respectively and the approach is simple and agile in design.The color fuzzy model and the steps of the algorithm are discussed.The evaluation of the performance shows the new method can produce the best synthetical performance.