Watershed-based Image Segmentation with Region Merging and Edge Detection

The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A watershed transformation technique is then employes. This includes: gradient of the classified image, dividing the image into markers, checking the Marker Image to see if it has zero points (watershed lines). The watershed lines are then deleted in the Marker Image created by watershed algorithm. A Region Adjacency Graph (RAG) and Region Adjacency Boundary (RAB) are created between two regions from Marker Image. Finally region merging is done according to region average intensity and two edge strengths (T1, T2). The approach of the authors is tested on remote sensing and brain MR medical images. The final segmentation result is one closed boundary per actual region in the image.

IMAGE ANALYSIS BASED ON EDGE DETECTION TECHNIQUES

A method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. An initial segmented result is obtained based on K means clustering technique and the minimum distance. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. The gradient values are calculated and then the watershed technique is used. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The DIS map is obtained. This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. In MRF model, gray level l , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The segmentation results are improved by using watershed algorithm. After all pixels of the segmented regions are processed, a map of primitive region with edges is generated. The edge map is obtained using a merge process based on averaged intensity mean values. A common edge detectors that work on (MRF) segmented image are used and the results are compared. The segmentation and edge detection result is one closed boundary per actual region in the image.

Two-Phase Image Segmentation by the Allen-Cahn Equation and a Nonlocal Edge Detection Operator

Based on a nonlocal Laplacian operator,a novel edge detection method of the grayscale image is proposed in this paper.This operator utilizes the information of neighbor pixels for a given pixel to obtain effective and delicate edge detection.The nonlocal edge detection method is used as an initialization for solving the Allen-Cahn equation to achieve two-phase segmentation of the grayscale image.Efficient exponential time differencing(ETD)solvers are employed in the time integration,and finite difference method is adopted in space discretization.The maximum bound principle and energy stability of the proposed numerical schemes are proved.The capability of our segmentation method has been verified in numerical experiments for different types of grayscale images.

Review of Theory and Methods of Image Segmentation

Image segmentation refers to the technique and process of partitioning a digital image into multiple segments based on image characteristics so as to extract the object of interest from it. It is a key step from image processing to image analysis. In the mid-1950s, people began to study image segmentation. For decades, various methods for image segmentation have been proposed. In this paper, traditional image segmentation methods and some new methods appearing in recent years were reviewed. Thresholding segmentation methods, region-based, edge detection-based and segmentation methods based on specific theoretical tools were introduced in detail.

A new fuzzy edge detection algorithm

Based upon the maximum entropy theorem of information theory, a novel fuzzy approach for edge detection is presented. Firstly, a definition of fuzzy partition entropy is proposed after introducing the concepts of fuzzy probability and fuzzy partition. The relation of the probability partition and the fuzzy c-partition of the image gradient are used in the algorithm. Secondly, based on the conditional probabilities and the fuzzy partition, the optimal thresholding is searched adaptively through the maximum fuzzy entropy principle, and then the edge image is obtained. Lastly, an edge-enhancing procedure is executed on the edge image. The experimental results show that the proposed approach performs well.

A Multiscale Approach to Automatic Medical Image Segmentation Using Self-Organizing Map

In this paper, a new medical image classification scheme is proposed using selforganizing map (SOM) combined with multiscale technique. It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers. First, to solve the difficulty in manual selection of edge pixels, a multiscale edge detection algorithm based on wavelet transform is proposed. Edge pixels detected are then selected into the training set as a new class and a mu1tiscale SoM classifier is trained using this training set. In this new scheme, the SoM classifier can perform both the classification on the entire image and the edge detection simultaneously. On the other hand, the misclassification of the traditional multiscale SoM classifier in regions near edges is greatly reduced and the correct classification is improved at the same time.

IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES

This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.

ON MARKOV RANDOM FIELD MODELS FOR SEGMENTATION OF NOISY IMAGES

Markov random field(MRF) models for segmentation of noisy images are discussed. According to the maximum a posteriori criterion, a configuration of an image field is regarded as an optimal estimate of the original scene when its energy is minimized. However, the minimum energy configuration does not correspond to the scene on edges of a given image, which results in errors of segmentation. Improvements of the model are made and a relaxation algorithm based on the improved model is presented using the edge information obtained by a coarse-to-fine procedure. Some examples are presented to illustrate the applicability of the algorithm to segmentation of noisy images.

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease.This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)model.The proposed GOFED-RBVSC model initially employs contrast enhancement process.Besides,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions.The ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature vectors.Finally,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the work.The performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

基于改进Canny边缘检测的堆叠货箱分割定位方法

为了提高集装箱卸货的自动化水平,针对集装箱内堆叠货箱难分割定位的问题,提出了一种基于改进Canny边缘检测的堆叠货箱分割定位方法。通过阈值分割和形态学处理进行图像预处理,去除背景干扰,提取堆叠货箱区域图像,基于改进Canny算法对堆叠货箱进行边缘检测,根据堆叠货箱边缘特征进行筛选并基于最小二乘法进行直线拟合,解决边缘线条不连续和虚假边缘问题,对边缘进行区域化处理,以此将堆叠货箱分割成独立的货箱区域,提取每个独立货箱的最小外接矩形,得到货箱中心点的位置信息。实验结果表明,该方法对堆叠货箱有很好的分割效果,定位精度小于5 mm,满足定位精度要求。

级联语义分割和边缘检测的GF-2影像耕地提取

针对山地丘陵区的坡耕地和小面积耕地碎片边界模糊不清、分类提取困难的问题,以GF-2影像为数据源,提出了一种级联语义分割和边缘检测模型的遥感影像耕地信息提取方法。首先,针对不同类型耕地的特点选择级联方式;其次,将耕地边缘作为独立的特征类别,结合改进U-Net、DeeplabV3+和DexiNed模型,融合面特征和线特征,使得耕地边缘特征与语义特征能够进行互补,从而提高耕地提取的准确性,实现对复杂地形背景噪声的抑制和不同类型耕地的提取。实验结果表明,对比单一模型DeeplabV3+和U-Net,级联模型的耕地信息提取的总体精度、Kappa系数和F1值均有大幅度提升,针对不同类型耕地级联模型提取的耕地结果更接近真实耕地标注,漏提、误提区域远低于单一模型。

融合Canny边缘检测的多输出损失肺炎CT图像分割算法

针对肺炎的影像学特征如弥漫、多灶、磨玻璃的特点,提出一种肺部感染CT图像分割算法CEDMO。算法主干网络使用ResNet50,在特征提取中,先利用Canny算子对分割目标边缘进行检测,得到的边缘信息再与ResNet50进行融合计算,边缘信息还在解码器部分的多目标输出计算中作为一个引导值。在解码器部分对PSA注意力机制改进并设计了多输出约束,以获得了更多的细节信息和多个尺度的特征。在多输出中,设计了5个输出路径,每个路径的Loss都参与约束计算,使得训练结果加快收敛,从而提高计算效率。最后通过实验对比基线模型的结果,3个指标优于基线模型,其中Dice系数、灵敏度(SE)、增强对准度量(E_?~m)依次提高了4.7、4.5和1.3个百分点。

活塞表面缺陷图像的亚像素边缘检测技术研究

针对活塞图像对比度低、缺陷区域小、缺陷种类多、人工检测效率低等问题,提出一种结合区域生长法和亚像素边缘提取的活塞表面缺陷检测方法。利用图像处理软件采集活塞图像,分析活塞表面图像中缺陷区域与正常区域灰度值的差异。使用区域生长法进行图像分割,结合Canny算子对活塞表面缺陷边缘进行初步定位。通过定位感兴趣区域的位置,进行亚像素级别提取,并平缓感兴趣区域边缘。实验表明,所提算法比传统的边缘提取方法得到的缺陷区域更精确、平滑。

基于区域生长的肝影像分割系统的设计与研究

提出一种基于区域生长算法的肝脏CT影像分割方法,以减少在肝脏医学影像分割中由于对感兴趣区域分割不完整或过度分割导致的临床诊断失误问题。采用区域生长算法的邻接连续阈值法,实现对肝脏CT图像的分割,选取合适的种子点并在各个方向按照提前规定好的生长准则对各个像素点进行筛选分类,分割出感兴趣的区域,使用近百张不同的肝脏CT图像对算法进行训练和测试,完善在此技术之上开发出的系统功能模块。经过测试后,其分割成功率较高,几乎可以准确快速地分割出肝脏CT图像中感兴趣的区域,为临床医学中肝脏处病灶诊断提供可靠依据,避免诊断失误,具有实际应用意义。

含干扰区域的航空铣削件导孔视觉测量方法

在航空铣削零件测量过程中,传统图像处理方法无法有效测量含有切屑和切削液等干扰因素的铣削零件导孔尺寸。针对该问题,提出MPTransUNet模型检测工件图像的干扰区域;采用图像纹理修复方法对检测图像进行修复,并设计了多尺度边缘检测算法提取导孔边缘像素;结合随机抽样一致算法和最小二乘法,对边缘像素点筛选和拟合得到导孔几何尺寸。最后,以航空装夹板件为例验证了该方法的有效性,导孔孔径的测量精度为0.03 mm,可以满足航空铣削零件质检要求。

基于Halcon的水面垃圾清理机器人目标定位与路径规划方法

针对中小型水域垃圾清理作业困难,人工成本较高的问题,提出了基于视觉的水面垃圾清理机器人垃圾检测、定位、路径规划方法。首先,基于Halcon软件对含有水面垃圾的原始图像进行图像分割和边缘检测,实现目标物在原始图像中的二维坐标;接着,通过双目摄像头为垃圾清理机器人提供实际位置的空间坐标信息,再运用蚁群算法进行路径规划,优先选择最短路径,规划水面垃圾清理机器人清理目标垃圾的运行轨迹,以提高清理效率;最后将目标定位和路径规划结果融合,实现水面垃圾清理机器人的自主运行,达到垃圾清理的功能。实验结果表明所提方法能够较好地克服水面复杂环境影响,机器人运行效果较好,有利于提高水环境的卫生水平,具有重要的实际意义和应用价值。

基于图像处理的轨道表面病害检测研究

以钢轨为研究对象,结合图像处理技术,对钢轨表面质量进行检测。首先,对钢轨图像进行预处理,通过线性灰度变换对图像进行增强,并采用自适应滤波进行图像去噪;其次,采用Ostu阈值分割算法对预处理后的钢轨图像进行缺陷分割;最后,以离心率、矩形度和致密度作为分类依据,通过决策树分类法对钢轨表面缺陷进行分类。实验结果表明:基于图像处理的钢轨表面质量检测方法能够有效对钢轨缺陷进行检测,分割准确率为96.7%,分类准确率为90%,为钢轨表面质量检测提供了一种有效的检测方法。

Image Analysis of Fabric Pilling Based on Light Projection

The objective assessment of fabric pilling based on light projection and image analysis has been exploited recently.The device for capturing the cross-sectional images of the pilled fabrics with light projection is elaborated.The detection of the profile line and integration of the sequential cross-sectional pilled image are discussed.The threshold based on Gaussian model is recommended for pill segmentation.The results show that the installed system is capable of eliminating the interference with pill information from the fabric color and pattern.

A Method for Head-shoulder Segmentation and Human Facial Feature Positioning

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