Growing Regional Security Strength

At its August summit, the ShanghaiCooperation Organization shows it’s still astandard-bearer for regional

Flotation bubble image segmentation based on seed region boundary growing

Segmenting blurred and conglutinated bubbles in a flotation image is done using a new segmentation method based on Seed Region and Boundary Growing（SRBG）.Bright pixels located on bubble tops were extracted as the seed regions.Seed boundaries are divided into four curves：left-top,right-top,right-bottom, and left-bottom.Bubbles are segmented from the seed boundary by moving these curves to the bubble boundaries along the corresponding directions.The SRBG method can remove noisy areas and it avoids over- and under-segmentation problems.Each bubble is segmented separately rather than segmenting the entire flotation image.The segmentation results from the SRBG method are more accurate than those from the Watershed algorithm.

A fast and efficient mesh segmentation method based on improved region growing

Mesh segmentation is one of the important issues in digital geometry processing. Region growing method has been proven to be a efficient method for 3D mesh segmentation. However, in mesh segmentation, feature line extraction algorithm is computationally costly, and the over-segmentation problem still exists during region merging processing. In order to tackle these problems, a fast and efficient mesh segmentation method based on improved region growing is proposed in this paper. Firstly, the dihedral angle of each non-boundary edge is defined and computed simply, then the sharp edges are detected and feature lines are extracted. After region growing process is finished, an improved region merging method will be performed in two steps by considering some geometric criteria. The experiment results show the feature line extraction algorithm can obtain the same geometric information fast with less computational costs and the improved region merging method can solve over-segmentation well.

Steganography Using Reversible Texture Synthesis Based on Seeded Region Growing and LSB

Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images,and is hard to guarantee security.To solve these problems,steganography using reversible texture synthesis based on seeded region growing and LSB is proposed.Secret information is embedded in the process of synthesizing texture image from the existing natural texture.Firstly,we refine the visual effect.Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture.We use seeded region growing algorithm to ensure texture’s similar local appearance.Secondly,the size and capacity of image can be decreased by introducing the information segmentation,because the capacity of the secret information is proportional to the size of the synthetic texture.Thirdly,enhanced security is also a contribution in this research,because our method does not need to transmit parameters for secret information extraction.LSB is used to embed these parameters in the synthetic texture.

Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing

The quality of ultrasound scanning images is usually damaged by speckle noise.This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm.Unlike single statistical moment-based speckle reduction algorithms,this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability.The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance.Then,according to the similarity value and tissue characteristics,the entire image is divided into several levels of speckle-content regions,and adaptive smoothing is performed based on these classification characteristics and the corresponding window size determined by the proposed region growing technique.Tests conducted from phantoms and in vivo images have shown very promising results after a quantitative and qualitative comparison with existing work.

Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.

A MICRO-IMAGE FUSION ALGORITHM BASED ON REGION GROWING

Due to the limitation of Depth Of Field (DOF) of microscope, the regions which are not within the DOF will be blurring after imaging. Thus for micro-image fusion, the most important step is to identify the blurring regions within each micro-image, so as to remove their undesirable impacts on the fused image. In this paper, a fusion algorithm based on a novel region growing method is proposed for micro-image fusion. The local sharpness of micro-image is judged block by block, then blocks whose sharpness is lower than an adaptive threshold are used as seeds, and the sharpness of neighbors of each seed are evaluated again during the region growing until the blurring regions are identified completely. With the decreasing in block size, the obtained region segmentation becomes more and more accurate. Finally, the micro-images are fused with pixel-wise fusion rules. The experimental results show that the proposed algorithm benefits from the novel region segmentation and it is able to obtain fused micro-image with higher sharpness compared with some popular image fusion method.

An Automatic Segmentation of Kidney in Serial Abdominal CT Scans Using Region Growing Approach

Automatic kidney segmentation from abdominal CT images is a key step in computer-aided diagnosis for kidney CT as well as computeraided surgery. However, kidney segmentation from CT images is generally performed manually or semi-autornatically because of gray levels similarities of adjacent organs/tissues in abdominal CT images. This paper presents an efficient algorithm for segmenting kidney from serials of abdominal CT images. First, we extracted estimated kidney position （EKP） according to the statistical geometric location of kidney within the abdomen. Second, we analyzed the intensity distribution of EKP for several abdominal CT images and exploit an adaptive threshold searching algorithm to eliminate many other organs/tissues in the EKP. Finally, a novel region growing approach based on labeling is used to obtain the fine kidney regions. Experimental results are comparable to those of manual tracing radiologist and shown to be efficient.

Two-stage image segmentation based on edge and region information

A two-stage method for image segmentation based on edge and region information is proposed. Different deformation schemes are used at two stages for segmenting the object correctly in image plane. At the first stage, the contour of the model is divided into several segments hierarchically that deform respectively using affine transformation. After the contour is deformed to the approximate boundary of object, a fine match mechanism using statistical information of local region to redefine the external energy of the model is used to make the contour fit the object's boundary exactly. The algorithm is effective, as the hierarchical segmental deformation makes use of the globe and local information of the image, the affine transformation keeps the consistency of the model, and the reformative approaches of computing the internal energy and external energy are proposed to reduce the algorithm complexity. The adaptive method of defining the search area at the second stage makes the model converge quickly. The experimental results indicate that the proposed model is effective and robust to local minima and able to search for concave objects.

SEED REGION SELECTION AND HOMOGENEITY CRITERION FOR DOORPLATE IMAGE SEGMENTATION IN MOBILE ROBOT NAVIGATION

Focused on the seed region selection and homogeneity criterion in Seeded Region Growing (SRG), an unsupervised seed region selection and a polynomial fitting homogeneity criterion for SRG are proposed in this paper. First of all, making use of Peer Group Filtering (PGF) techniques, an unsupervised seed region selection algorithm is presented to construct a seed region. Then based on the constructed seed region a polynomial fitting homogeneity criterion is applied to solve the concrete problem of doorplate segmentation appearing in the robot navigation along a corridor. At last, experiments are performed and the results demonstrate the effectiveness of the proposed algorithm.

Effects of Different Vegetable Planting Modes on Soil Microbial Flora and Enzyme Activity

To investigate the effects of different vegetable growing regions and planting modes on soil quality,soils in high,medium and low altitude areas of Guizhou were respectively sampled under different vegetable efficient planting modes,and the variations of soil microbial flora and enzyme activities were analyzed. The soil microbial count and total bacteria of the vegetable efficient cultivation mode were significantly higher than that of the control （traditional planting mode） in each planting area,and the microbial diversity index was also improved to varying de- grees.The soil phosphatase,catalase and urease activities of the vegetable efficient planting mode were higher than that of the control.The soil catalase and urease activities were higher than that of the control by 1.37-1.44 and 1.51-2.80 times. Application of vegetable efficient planting mode in different regions will help to im- prove the soil quality in a given period.

Mixed reality head mounted displays for enhanced indoor point cloud segmentation with virtual seeds

Mixed Reality(MR)Head Mounted Displays(HMDs)offer a hitherto underutilized set of advantages compared to conventional 3D scanners.These benefits,inherent to MR-HMDs albeit not originally intended for such appli-cations,encompass the freedom of hand movement,hand tracking capabilities,and real-time mesh visualization.This study leverages these attributes to enhance indoor scanning process.The primary innovation lies in the con-ceptualization of manual-positioned MR virtual seeds for the purpose of indoor point cloud segmentation via a region-growing approach.The proposed methodology is effectively implemented using the HoloLens 2 platform.An application is designed to enable the remote placement of virtual tags based on the user’s visual focus on the MR-HMD display.This non-intrusive interface is further enriched with expedited tag saving and deletion functionalities,as well as augmented tag visualization through overlaying them on real-world objects.To assess the practicality of the proposed method,a comprehensive real-world case study spanning an area of 330 s^(2) is conducted.Remarkably,the survey demonstrates remarkable efficiency,with 20 virtual tags swiftly deployed,each requiring a mere 2 s for precise positioning.Subsequently,these virtual tags are employed as seeds in a region-growing algorithm for point cloud segmentation.The accuracy of virtual tag positioning is found to be exceptional,with an average error of 2.4±1.8 cm.Importantly,the user experience is significantly enhanced,leading to improved seed positioning and,consequently,more accurate final segmentation results.

Study on the Decision Support System for Northing of Winter Wheat Cultivation in Hebei Province

[Objective] This study was to provide methods to improve the scientificity and informatization level of agricultural decision-making system based on the study of Decision Support System for ＂Northing of Winter Wheat＂ in Hebei Province （DSS- NWWH）. [Method] The functions, development process, operation guidance as well as input and output modes of DSSNWWH were introduced, and the simulated results of the system were verified by comparing with the actual situations. [Result] The decision support system established in this study could predict whether a wheat variety could live through the winter in a certain area of northern Hebei Province, as well as the growth conditions based on the previous meteorological data or local weather forecast, and provided corresponding cultivation and management measures, making it possible for the user to determine whether the variety could be planted in the region based on the predictions. [Conclusion] The established DSSNWWH in this study can effectively help decision makers make decisions, providing scientific instructions for the northing of winter wheat.

An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm in PET-CT imaging

To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages： image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography （PET-CT） data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.

Attenuation correction with region growing method used in the positron emission mammography imaging system

The Positron Emission Mammography imaging system （PEMi） provides a novel nuclear diagnosis method dedicated for breast imaging. With a better resolution than whole body PET, PEMi can detect millimeter-sized breast tumors. To address the requirement of semi-quantitative analysis with a radiotracer concentration map of the breast, a new attenuation correction method based on a three-dimensional seeded region growing image segmentation （3DSRG-AC） method has been developed. The method gives a 3D connected region as the segmentation result instead of image slices. The continuity property of the segmentation result makes this new method free of activity variation of breast tissues. The threshold value chosen is the key process for the segmentation method. The first valley ill the grey level histogram of the reconstruction image is set as the lower threshold, which works well in clinical application. Results show that attenuation correction for PEMi improves the image quality and the quantitative accuracy of radioactivity distribution determination. Attenuation correction also improves the probability of detecting small and early breast tumors.

A Fast Filling Algorithm for Image Restoration Based on Contour Parity

Filling techniques are often used in the restoration of images.Yet the existing filling technique approaches either have high computational costs or present problems such as filling holes redundantly.This paper proposes a novel algorithm for filling holes and regions of the images.The proposed algorithm combines the advantages of both the parity-check filling approach and the region-growing inpainting technique.Pairing points of the region’s boundary are used to search and to fill the region.The scanning range of the filling method is within the target regions.The proposed method does not require additional working memory or assistant colors,and it can correctly fill any complex contours.Experimental results show that,compared to other approaches,the proposed algorithm fills regions faster and with lower computational cost.

Spatio-temporal Compensation Based Object Detection for Video Surveillance Systems

Moving object detection in video surveillance is an important step. This paper addresses an automatic object detection algorithm based on spatio-temporal compensation for video surveillance. Temporal difference of the pairs of two frames with a k-frame distance is utilized to obtain coarse object masks. Usually, object regions in these coarse masks have discontinuous boundaries and some holes. Region growing with the distance constraint is proposed to compensate these coarse object regions in spatial domain, followed by filling holes. The added distance constraint can prevent object regions from growing infinitely. The proposed filling holes method is simple and effective. To solve the temporarily stopping problem of moving objects, temporal compensation is proposed to compensate the object mask by utilizing temporal coherence of moving objects in temporal domain. The proposed detection algorithm can extract moving objects as completely as possible. Experimental results have successfully demonstrated the validity of the proposed algorithm.

An adaptive region growing algorithm for breast masses in mammograms

This study attempted to accurately segment the mammographic masses and distinguish malignant from benign tumors.An adaptive region growing algorithm with hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis was developed in this paper.In order to accommodate different situations of masses,the likelihood and the edge gradients of segmented masses were weighted adaptively by the use of information entropy.106 benign and 110 malignant tumors were included in this study.We found that the proposed algorithm obtained segmentation contour more accurately and delineated the tumor body as well as tumor peripheral regions covering typical mass boundaries and some spiculation patterns.Then the segmented results were evaluated by the classification accuracy.42 features including age,intensity,shape and texture were extracted from each segmented mass and support vector machine(SVM)was used as a classifier.The classification accuracy was evaluated using the area(A_(z))under the receiver operating characteristic(ROC)curve.It was found that the maximum likelihood analysis achieved an A_(z)value of 0.835,the maximum gradient analysis got an A_(z)value of 0.932 and the hybrid assessment function performed the best classification result where the value of A_(z)was 0.948.In addition,compared with traditional region growing algorithm,our proposed algorithm is more adaptive and provides a better performance for future works.

3D segmentation and visualization of lung and its structures using CT images of the thorax

Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting various illnesses automatically. What has been highlighted among the existing exams that allow diagnosis aid and the application of computing systems in parallel is Computed Tomography (CT). CT enables the visualization of internal organs, such as the lung and its structures. Computational Vision systems extract information from the CT images by segmenting the regions of interest, and then recognize and identify details in those images. This work focuses on the segmentation phase of CT lung images with singularity-based techniques. Among these methods are the region growing (RG) technique and its 3D RG variations and the thresholding technique with multi-thresholding. The 3D RG method is applied to lung segmentation and from the 3D RG segments of the lung hilum, the multi-thresholding can segment the blood vessels, lung emphysema and the bones. The results of lung segmentation in this work were evaluated by two pulmonologists. The results obtained showed that these methods can integrate aid systems for medical diagnosis in the pulmonology field.

Plane extraction for navigation of humanoid robot

In order to make the humanoid robot walk freely in complicated circumstance, the reliable capabilities for obtaining plane information from its surroundings are demanded. A system for extracting planes from data taken by stereo vision was presented, After the depth image was obtained, the pixels of each line were scanned and split into straight line segments. The neighbouring relation of line segments was kept in link structure. The groups of three line segments were selected as seed regions. A queue was maintained for storing seed regions, and then the plane region was expanded around the seed region. The process of region growing continued until the queue of seed regions was empty. After trimming, the edges of the planes became smooth. In the end, extracted planes were obtained. In the experiment, two models were used： pipe and stairs. Two planes in pipe mode/and six planes in stairs model were extracted exactly. The speed and precision of algorithm can satisfy the demands of humanoid robot＇s navigation.