Ore Image Segmentation Method Based on U-Net and Watershed

Ore image segmentation is a key step in an ore grain size analysis based on image processing.The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation.In this article,in order to solve the problem,an ore image segmentation method based on U-Net is proposed.We adjust the structure of U-Net to speed up the processing,and we modify the loss function to enhance the generalization of the model.After the collection of the ore image,we design the annotation standard and train the network with the annotated image.Finally,the marked watershed algorithm is used to segment the adhesion area.The experimental results show that the proposed method has the characteristics of fast speed,strong robustness and high precision.It has great practical value to the actual ore grain statistical task.

Image Segmentation Using an Improved Watershed Algorithm

As watershed algorithm suffers from over-segmentation problem, this paper presented an efficient method to resolve this problem. First, pre-process of the image using median filter is made to reduce the effect of noise. Second, watershed algorithm is employed to provide initial regions. Third, regions are merged according to the information between the region and boundary. In the merger processing based on the region information, an adaptive threshold of the difference between the neighboring regions is used as the region merge criteria, which is based on the human visual character. In the merger processing on the boundary information, the gradient is used to judge the true boundary of the image to avoid merging the foreground with the background regions. Finally, post-process to the regions using mathematical morphology open and close filter is done to smooth object boundaries. The experimental results show that this method is very efficient.

Watershed segmentation based on hierarchical multi-scale modification of morphological gradient

Watershed segmentation is sensitive to noises and irregular details within the image,which frequently leads to a serious over-segmentation Linear filtering before watershed segmentation can reduce over-segmentation to some extent,however,it often causes the position offset of object contours.For the purpose of reducing over-segmentation to preserve the location of object contours,the watershed segmentation based on the hierarchical multi-scale modification of morphological gradient is proposed.Firstly,multi-scale morphological filtering was employed to smooth the original image.Then,the gradient image was divided into multi-levels by the volume of three-dimension topographic relief,where the lower gradient layers were further modifiedby morphological closing with larger-sized structuring-elements,and the higher layers with the smaller one.In this way,most local minimums caused by irregular details and noises can be removed,while region contour positions corresponding to the target area were largely preserved.Finally,morphological watershed algorithm was employed to implement segmentation on the modified gradient image.The experimental results show that the proposed method can greatly reduce the over-segmentation of the watershed and avoid the position offset of the object contours.

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 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.

A Tongue-images Segmentation Method Based on Local Restoration and Watershed Algorithm

Objective: To propose a method to segment tongue- images efficiently, and extract tongue- body accurately and quickly. Methods: Firstly, a kind of color- images' pre- processing technique was used to solve tongue- surface reflection problem. Neighbouring and similar region's information was used to restore the region with tongue- surface reflection problem by replacement. Secondly, the restored image was transformed into a gray one, and then processed by mathematical morphological operation- dilation to get a closed- loop edge. The third technique used was watershed algorithm, which is an usual tool in image segmentation. 'Watershed' function of matlab software was used to complete this algorithm. After that, region- combination technique was used. Through measuring neighbourship and similarity of regions, a non- objective and non- background region was merged into one of its neighbouring regions. This step was repeated until only two regions, objective and background regions, were left in the image. At last, corresponding to the merged image, tongue- body image was got from the original image. Results: 316 images were randomly taken from the image library for experiments, and 299 images were correctly segmented, so, the successful ratio is 94.62%. On the other hand, average time of running this method was about 50 s under whole sampling environment. Conclusion: The method presented in this paper can segment a tongue- body image from its original one effectively, and thus laying a good foundation for the following analysis work.

Image Segmentation Algorithm Based on Improved Watershed Algorithm for Young Leaves of Tea

Aiming at the problem that the traditional watershed image segmentation algorithm is sensitive to noise and prone to ＂over-segmentation＂, an image segmentation method based on improved watershed algorithm is proposed. First, the denoising method is used to denoise the tea image by using the differential equation denoising model The interference of the image on the image segmentation, the protection of the tea image of the edge of the tea information; and then use the watershed algorithm to denoise the tea image after the split. The simulation results show that this method can effectively avoid the influence of noise on image segmentation, and get a good image of ,louug leaves of tea image.

基于标记的Watershed图像分割新算法

为了防止Watershed算法过分割问题,文中提出一种新改进的基于标记的Watershed自动图像分割方法.文中设计出一种有效的标记自动提取方法,用来从梯度图像的低频成分中提取与图像中的物体相关的极小值,用这些极小值构成二值标记图像.根据二值标记图像,形态学的极小值标定技术被用来将这些提取的标记强制作为原始梯度图像的极小值,而屏蔽原有梯度图像的所有极小值.最后,watershed算法在修改过的梯度图像上进行图像分割.利用本文提出的图像分割算法可以获得较为理想的图像分割结果.通过对不同类型的图像进行试验,证明本文提出的图像分割算法能够获得符合人类视觉特点,具有实际意义而且均一的分割区域,以及较为准确、连续、一个像素大小的物体边界.与其它的Watershed改进方法相比,本文提出的方法要求的计算复杂度较低,具有简单的参数,同时能够更为有效地降低Watershed算法的过分割问题.

滑动窗口式三维watershed算法实现视频序列分割

视频序列分割是实现视频对象提取、处理和识别的基础,也是基于内容的视频压缩和检索的前提。目前普遍采用的watershed算法是全局涨水算法,用于视频分割时占用的存储和处理空间巨大。针对这一问题本文提出了新的滑动窗口式三维watershed算法(SW3DW),通过时间轴上的局部窗口对视频序列进行观察和分割,而在相邻窗口间采用水源区标号传递和局部流域更新相结合的方法,以较小的存储和处理空间实现了局域和全局处理的一致性。理论分析和实验证明该算法能有效性地实现视频序列的分割,为后续视频对象提取和处理创造了条件。

Elastic Particle Mesh Model and Its Application in Image Segmentation

In this paper, an elastic particle mesh （EPM） model is presented. It can be used like a cover to sketch images. EPM offers two advantages： first, when putting on a sketch image, it helps to repair disconnections on salient features. Second, it hides trivial details in the image, thus has the ability of decreasing over-segmentation when used with watershed transformation.

A method of automatic image segmentation for gas bubbles in water

This paper presents an algorithm of automatic bubble image segmentation using the improved ant colony optimization methodology. The ant colony optimization method is a metaheuristic algorithm, and has been applied in many fields. To reveal the versatility and appropriateness of automatic bubble image segmentation, the fuzzy clustering analysis method is employed in ant colony optimization algorithm. Compared with the well-known image feature extraction operators such as SUSAN and Canny, the proposed method can comparatively suitable to extract the gas bubbles image edge features. The experimental results show that the proposed method is effective and reliable, and can achieve satisfactory image edge extraction effect.

Automated lung segmentation algorithm for CAD system of thoracic CT

Objective: To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic (CAD) system, especially on the segmentation between left and right lungs. Methods: We put forward the base frame of our lung segmentation firstly. Then, using optimal thresholding and mathematical morphologic methods, we acquired the rough image of lung segmentation. Finally, we presented a fast self-fit segmentation refinement algorithm, adapting to the unsuccessful left-right lung segmentation of thredsholding. Then our algorithm was used to CT scan images of 30 patients and the results were compared with those made by experts. Results: Experiments on clinical 2-D pulmonary images showed the results of our algorithm were very close to the expert’s manual outlines, and it was very effective for the separation of left and right lungs with a successful segmentation ratio 94.8%. Conclusion: It is a practicable fast lung segmentation algorithm for CAD system on thoracic CT image.

Fingerprint Database Optimization Using Watershed Transformation Algorithm

Fingerprints are a unique feature for identification and verification of humans. The need to optimise several databases for storing the images of fingerprints is a major concerning issue. Several segmentation algorithms have been used in the time past but there are still several challenges facing some current segmentation algorithms like computational efficiency. Another challenge is that segmentation procedure can be impractically slow, or requires extremely large amounts of memory. This paper addresses the challenges by employing watershed flooding algorithm on the fingerprint images so as to optimize the sizes of the databases. A pre-processing plug-in that implements this segmentation process is developed using Java. We showed its effectiveness by testing it on fingerprint image dataset and the entropy showed that the segmented images sizes were reduced.

AN AUTOMATIC SEGMENTATION METHOD FOR MOVING OBJECTS BASED ON THE SPATIAL-TEMPORAL INFORMATION OF VIDEO

The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find pre~：ise object boundaries of moving objects. To locate moving objects in image sequences, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.

Application of the gradient-based watershed algorithm in M/A constituent identification

This paper reports the quantitative study of martensite/austenite(M/A)constituents in high-grade heavy steel plates using the gradient-based watershed algorithm.Compared with several other image segmentation algorithms,the gradient-based watershed algorithm can effectively remove noises in images with various qualities and identify M/A constituents.The results prove that the gradient-based watershed algorithm is an effective method that can be used to digitize microstructures and integrate the data into Process Intelligent Data Application System(PIDAS).

Feature extraction of jujube fruit wrinkle based on the watershed segmentation

The degree of surface wrinkles on a dried jujube fruit(Ziziphus jujuba Mill.)is an important quality grading criterion.The aim of this research was to propose an image processing method based on the watershed segmentation to extract the wrinkle features of jujube fruits.Original images of jujube fruit taken under cyan light were transformed into grayscale images.The noise in these images was then removed by morphological reconstruction.The H-minima extended transformation was used to label the foreground of jujube fruit images after reconstruction,and the labeled foreground regions were segmented by a distance transform-based watershed algorithm.Then,the grayscale images were filtered with a local range filter.The segmentation function was obtained using the minima imposition method.Finally,a watershed segmentation was used to extract the wrinkle features of jujube fruits.Experiments on 304 images of jujube fruit showed that the accuracy of wrinkle-based grading obtained by the algorithm was 92.11%,which proved that this method could be used to classify jujube wrinkles.

Using Entropy Based Mean Shift Filter and Modified Watershed Transform for Grain Segmentation

Life science research aims to continuously improve the quality and standard of human life. One of the major challenges in this area is to maintain food safety and security. A number of image processing techniques have been used to investigate the quality of food products. In this paper, we propose a new algorithm to effectively segment connected grains so that each of them can be inspected in a later processing stage. One family of the existing segmentation methods is based on the idea of watersheding, and it has shown promising results in practice. However, due to the over-segmentation issue, this technique has experienced poor performance in various applications, such as inhomogeneous background and connected targets. To solve this problem, we present a combination of two classical techniques to handle this issue. In the first step, a mean shift filter is used to eliminate the inhomogeneous background,where entropy is used to be a converging criterion. Secondly, a color gradient algorithm is used in order to detect the most significant edges, and a marked watershed transform is applied to segment cluttered objects out of the previous processing stages. The proposed framework is capable of compromising among execution time, usability, efficiency and segmentation outcome in analyzing ring die pellets. The experimental results demonstrate that the proposed approach is effectiveness and robust.

CT Image Segmentation Method of Composite Material Based on Improved Watershed Algorithm and U-Net Neural Network Model

In the study of the composite materials performance,X-ray computed tomography(XCT)scanning has always been one of the important measures to detect the internal structures.CT image segmentation technology will effectively improve the accuracy of the subsequent material feature extraction process,which is of great significance to the study of material performance.This study focuses on the low accuracy problem of image segmentation caused by fiber cross-section adhesion in composite CT images.In the core layer area,area validity is evaluated by morphological indicator and an iterative segmentation strategy is proposed based on the watershed algorithm.In the transition layer area,a U-net neural network model trained by using artificial labels is applied to the prediction of segmentation result.Furthermore,a CT image segmentation method for fiber composite materials based on the improved watershed algorithm and the U-net model is proposed.It is verified by experiments that the method has good adaptability and effectiveness to the CT image segmentation problem of composite materials,and the accuracy of segmentation is significantly improved in comparison with the original method,which ensures the accuracy and robustness of the subsequent fiber feature extraction process.

融合颜色与形状信息的铁谱磨粒链分割方法研究

为了对铁谱图像中的磨粒链进行有效分割,提高磨粒特征提取的精度,通过分析铁谱磨粒链图像不同区域的特点,提出一种融合铁谱图像颜色、形状特征信息的磨粒链分割方法。该方法首先基于形态学运算对磨粒链分割方法,对异常大磨粒进行提取并断开磨粒链黏连部分,然后利用标记分水岭与灰色聚类的方法对磨粒链进行分割,最后考虑到不同磨粒链颜色信息存在差异,结合自适应阈值法对灰色聚类进行改进,从而实现磨粒链的自适应分割。结果表明,相较于传统分割方法,提出的方法能有效避免过分割与欠分割,分割效果较好,适用性高,操作简单。