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 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.

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.

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.

Texture feature based automated seeded region growing in abdominal MRI segmentation

A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- variogram texture features are extracted from the image and a seeded region growing algorithm is run on these feature spaces. With a given Region of Interest (ROI), a seed point is automatically se-lected based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing ‘explosion’. This algorithm is tested on 12 series of 3D ab-dominal MR images.

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.

A Road Extraction Method Based on Region Growing and Mathematical Morphology from Remote Sensing Images

Road traffic is the important driving factor for economic and social development. With the rapid increase of vehicle population, road traffic problems such as traffic jam and traffic accident have become the bottleneck which restricts economic development. In recent years, natural disasters frequently occur in China. Therefore, it is essential to extract road information to compute the degree of road damage for traffic emergency management. A road extraction method based on region growing and mathematical morphology from remote sensing images is proposed in this paper. According to the road features, the remote sensing image is preprocessed to separate road regions from non-road regions preliminarily. After image thresholding, region growing algorithm is used to extract connected regions. Then we sort connected regions by area to exclude the small regions which are probably non-road objects. Finally, the mathematical morphology algorithm is used to fill the holes inside the road regions. The experimental results show that the method proposed can effectively extract roads from remote sensing images. This research also has broad prospects in dealing with traffic emergency management by the government.

Study on Eco-climate Type Regionalization of Wheat Growing Areas in Yunnan Province

In order to provide an objective and scientific theoretical basis for rational distribution of wheat growth in Yunnan Province,according to the relationship between Yunnan weather conditions and wheat growth adaptability,a study on eco-climate type regionalization of wheat growing areas in Yunnan was conducted using principal component analysis and GIS technology. The results show that Yunnan Province could be divided into four types,namely southern warm and humid wheat growing area,central semi-arid wheat growing area,central semi-humid wheat growing area and north-central cold wheat growing area.

Study on Chemical Characteristics of Rainfall in Tobacco-Growing Regions of Chenzhou, Hunan Province

In order to clarify the chemical properties of rainfall in typical tobacco areas in Chenzhou City, Hunan Province, and analyze its potential for soil and flue-cured tobacco planting, rainfall samples in 2020 were collected by rainfall instruments in Fangyuan Town and Aoquan Town of Guiyang County, and the chemical properties that are closely related to the quality of flue-cured tobacco were determined, such as pH, EC, total nitrogen (TN), nitrate nitrogen (), ammonium nitrogen () and ion concentrations (K+, Na+, Ca2+, Mg2+, , Al3+, , Cl−,). The results show that the pH values of rainfall samples at Fangyuan and Aoquan monitoring sites are in the range of 4.92 - 6.17 and 4.93 - 5.69 respectively, with an average of 5.27 and 5.27 respectively. The acid rain characteristic is very obvious, which is mainly dominated by . The variation of rainfall EC has seasonal characteristics. EC is low from January to September, in the range of 6.09 - 56.72 and 11.83 - 30.93 μS/cm respectively, besides, it is high from October to December, in the range of 102.63 - 174.60 and 25.05 - 86.37 μS/cm respectively. The annual deposition of TN was 22.19 and 20.76 kg/hm2/yr respectively, which were higher than that in the western regions with less human disturbance, but lower than or equal to that in the developed agricultural regions in eastern China. The proportion of in the annual deposition of TN was higher than that of at two monitoring sites, with an average of 56.51% and 38.86% respectively. Ammonia volatilization from agricultural activities contributed more to rainfall nitrogen content. The ratios of ammonium nitrogen to nitrate nitrogen deposition at two monitoring points were 1.84, 1.81, 1.86 and 1.34, 1.46, and 1.29 during the whole year, summer and autumn, winter and spring respectively. The ratio is higher in summer and autumn than in winter and spring. The weighted average equivalent concentrations of the main ions at two monitoring sites were 238.88 μeq/L and 211.21 μeq/L respectively, and the orders of the ion concentrations were slightly different. Both the concentrations of and are higher, while Mg2+, and Al3+ are lower. , and are mainly from human activities with a contribution rate between 91.90% and 99.35%. Ca2+ mainly comes from soil and ground dust, besides, Cl− and Mg2+ mainly come from marine sources and K+ mainly comes from terrestrial sources. In general, the acidic rainfall and higher concentration are beneficial to reducing the high pH value of soil in Aoquan tobacco area and improving the quality of flue-cured tobacco. Higher concentrations of and in rainfall are not conducive to the improvement of flue-cured tobacco quality, because of the high content of soil available sulfur in Chenzhou tobacco area and the characteristics of flue-cured tobacco’s preference for ammonium.

Temporal and Spatial Variation Characteristics of Precipitation during Crop Growing Season in Northeast China

[Objective] The aims were to understand variation characteristics of water resources and provide theoretical guidance for the formulation of agricultural irrigation methods.[Method] Taking the precipitation records during crop growing season（from April to September）observed by 177 weather stations from 1971 to 2008 in the three provinces of Northeast China（Heilongjiang,Jilin and Liaoning）as research data,annual change and spatial distribution characteristics of precipitation during crop growing season were analyzed by means of small grid interpolation and climatic trend rate.[Result] The precipitation during crop growing season general exhibited the decreasing trend and the precipitation trend rate was-8.6 mm/10a in Northeast China.In addition,there was lack of rain from 1971 to 1980 and relatively abundant of rain during 1981 and 1990 respectively.Moreover,the precipitation obviously exhibited decreasing trend from 1991 to 2008.But the decreasing trend was inconsistent in spatial distributions,that was,the precipitation slightly increased in relatively rainless areas and obviously decreased in relatively rainy areas.[Conclusion] The areas with obvious decreasing trend of precipitation during crop growing season are the main grain producing zones in Northeast China,so the problem of food production security caused by the precipitation changes should be paid enough attention.

基于机器视觉的芯片字符区域分割和定位算法

芯片表面的字符对分选具有重要意义,对字符的定位是分选工作的关键步骤。为了提高分选工作定位效率和定位精度,提出了一种基于改进的区域生长算法和凸包检测算法分割和定位芯片表面字符区域方法。首先,对采集图像进行预处理操作,利用改进的Canny算法获取无干扰图像边缘,将图像边缘作为区域生长法的种子点并以图像自适应阈值作为生长准则分割图像,使用最大内接矩形算法粗定位字符区域;其次,采用Harris角点检测算法获取字符角点分布位置;最后,筛选角点并提取关键点,利用一种改进的凸包检测算法定位字符区域。经过实验验证,所设计的算法能够完整的分割和定位芯片表面字符区域,定位精度和效率较原有方法提升5.3%和15.4%,满足实际工业生产的要求。

结合超体素与颜色信息的区域生长点云分割方法

为解决传统区域生长点云分割算法存在的欠分割和过分割现象,提出一种结合超体素与颜色信息的区域生长点云分割方法。在分割过程中加入超体素过分割步骤,避免直接从点云中分割数据,有效消除噪声和异常值对分割的影响,利用一种几何和颜色信息的联合准则合并超体素并进行区域生长。与深度学习方法和其它3种传统分割算法相比,分割效率和精度都得到了较大提升,解决了欠分割和过分割的问题。

改进的区域生长算法在三维激光点云识别岩体结构面中的应用

交错分布的结构面构成了岩体中的薄弱部位,准确高效的岩体结构面识别和特征信息提取可为岩体稳定性评价提供重要依据。三维激光扫描技术可以极大地提高结构面勘测效率和精度,但目前主流的点云分析算法存在结构面边缘识别模糊、点云分割准确性不能满足结构面特征信息提取精度等问题。因此,考虑岩体结构面点云位置与其邻域的空间关系,利用KD-tree数据结构进行最邻近搜索的体素下采样,在稳健随机Hough变换的基础上改进了区域生长算法,通过多特征值对区域生长分割参数进行修正,依据点云法向量差值和特征终值进行结构面分割,实现了结构面产状、间距、延展度信息的提取。研究结果表明:与传统的主成分分析法和随机抽样一致法相比,在室内块体模型组成的24个结构面中,该方法在相同区域具有更高的识别率和准确率,既能在复杂变化的平面区域保证数据的完整识别,也能在平面的尖锐位置较好地分割边缘点云。利用该方法可以将24个结构面分为6组,并在识别数据中获取对应的结构面特征信息,与实际测量结果相比,角度信息误差约为1°,距离信息误差1cm以内。利用该方法在长江干流蟒蛇寨斜坡岩体中成功识别出3组结构面同时计算各组结构面间距与延展度信息,并采用赤平投影图分析不同结构面组对斜坡稳定性的影响。所提出的方法在室内模型及现场斜坡验证效果良好,可以为岩体结构面识别分割提供稳定且有效的技术支撑。

Effect of Different Chemical Treatments on Seedling Emergence Growth of Frozen Cassava

The rooting and growth of frozen cassava under different chemical treatments were studied.The result has demonstrated that disinfection effect could increase rooting rate and seedling emergence rate of cassava seed stem and decrease the rate of mildew.The sportak treatment could generate better effect(rooting rate and seedling emergence rate were 63%,the mildew rate of stem was 28%).Among different rooting reagents,Genwang+lime treatment generated better effect on rooting rate and emergence rate of frozen cassava than these of other groups and control group.It was concluded from the effects of different rooting reagents on growth of seed stem that Genwang+lime treatment could promote elongation and growth of cassava significantly(the mean plant height of experimental groups inceased 8.58 cm compared with that of control group) while paclobutrazol+lime generated the best effect on crassation of stem(the stem diameter of experimental group increased 0.4 cm compared with that of control group).

山东烟区烤烟地上部临界氮稀释曲线的构建与营养诊断

为建立山东烟区烤烟地上部临界氮稀释曲线,探讨以氮素营养指数诊断烤烟氮素营养状况的可行性,优化烤烟氮素管理,田间设置6个氮肥用量,分析其对移栽后烤烟地上部干物质积累量和氮浓度的影响,并通过烤烟氮营养指数判断烟株氮素营养的丰缺情况。结果表明,随着施氮量增加,各取样时期烤烟干物质积累量均表现为先增加后稳定的趋势,不同施肥处理烤烟地上部干物质积累量差异显著;随着烤烟地上部干物质积累量的增加,不同施肥处理烤烟地上部氮浓度均逐渐降低;烤烟地上部临界氮浓度和干物质积累量符合幂函数关系,以地上部干物质积累量为驱动变量的临界氮稀释曲线为Nc=2.25×DM-0.13,其决定系数R2为0.72(p<0.05);不同氮肥处理氮营养指数(NNI)均在一定程度上呈现波动性,移栽后随时间增长呈现先上升后下降趋势,根据临界氮稀释曲线计算氮营养指数(NNI)判定烤烟氮营养状况,氮肥施用量90 kg/hm^(2)时氮营养状况较好。山东烟区烤烟地上部临界氮稀释曲线方程为Nc=2.25×DM-0.13,通过烤烟地上部临界氮稀释曲线与氮营养指数能够评价山东烟区烤烟氮营养状况,研究结果表明该试验地点适宜的氮肥施用量在90 kg/hm^(2)左右。

机载LiDAR点云数据的建筑屋顶面提取算法

针对机载LiDAR点云数据的屋顶面提取过程中因受植被影响导致提取精度低的问题,提出了一种基于区域生长的屋顶面点云提取算法。进行滤波处理得到非地面点云,利用屋顶面点云邻域特征信息提取屋顶面种子点,引入植被指数和RGB差值信息作为生长约束条件对屋顶面点云进行生长分割,利用屋顶面的高程与面积值对提取结果进行过滤优化,得到屋顶面点云。选取了农村、城市、工厂三组不同场景的测试数据进行实验,结果表明:Kappa系数分别达到了97.29%、97.82%、97.13%,算法可实现较好的建筑屋顶面提取效果,且针对不同建筑场景具有良好的适应性。

基于三维重建的静爆场破片检测方法

针对靶场静爆试验中破片统计分析工作效率低的问题,提出一种基于多视图三维重建的破片检测方法。引入点云分割中的自适应阈值,有效保留三维点云的细节信息,增强了算法对靶场不同场景应用的泛化能力。针对大数量点云处理效率不佳的问题,采用点云法向量预先验证的方法,再结合颜色的区域生长分割算法,有效降低了检测误差和漏检概率,提升了破片检测的精度。实验结果表明:文中提出的检测方法检测准确率达到95%,破片弹着点误差小于4%,可以有效地分割出破片的着靶参数,为后续破片的统计分析工作提供了实验依据。

一种基于机载激光雷达点云的建筑物识别方法

以三维激光雷达数据为基础进行目标自动检测识别具有重要现实意义。针对传统的以人工判读识别目标的方法已经远不能满足从海量数据中确定目标的要求,文中提出一种基于机载激光雷达点云的建筑物识别方法。通过使用改进后的金字塔方法构建三维激光雷达点云,鉴于三维激光点云具有地物本身的语义信息,可根据点云的多尺度复杂地物特征实现三维激光点云数据的自动分类,对分类后的非地面点进行区域生长分割,通过点云与建筑物平面特征的匹配,确定建筑物的识别结果。整个方案实现过程清晰、简单,而且该方法的实现对计算机硬件配置要求不高,能够从海量三维激光雷达数据中快速、准确地识别类建筑物,正确率高达81.25%,在目标识别领域具有较高的应用价值。