基于单目相机与K均值聚类分割的船舶航行环境地图深度构建

为降低视觉设备感知航行环境时,水面光照反射对船舶位姿估计和环境地图重构的影响,在HSV(hue,saturation,value)颜色空间下,采用K均值聚类算法对近岸航行环境图像进行聚类分割处理。改进快速特征点提取和描述算法(oriented FAST and rotated BRIEF,ORB)来提高即时定位与地图构建(simultaneous localization and mapping,SLAM)效率,缩短特征点匹配时间,改善对外界环境的感知效果并提升船舶自身位姿估计精度。采用2020年南宁海事局执法船进港和靠泊期间由单目相机拍摄的视频数据进行实例验证。结果表明,提出的算法比传统SLAM算法的运行耗时更少,与传统定位设备输出轨迹的偏差较小,可为船舶全面立体感知海上航行环境提供研究基础。

几种分割算法在CT肝脏兴趣区提取方面的比较

目的:在计算机辅助诊断中,对已提取分割好的CT肝脏组织中的兴趣区的几种提取方法做比较。方法:利用Otsu分割算法、梯度分割算法、指数熵分割算法、均值聚类分割算法和二维直方图分割算法,对已分割的CT肝脏组织的兴趣区进行自动分割,并和医生手工分割的图像相对比。结果:通过实验及对实验数据进行分析,找出了具有良好获取兴趣区能力的分割算法。结论:通过手工分割图像分别与Otsu分割算法、梯度分割算法、指数熵分割算法、均值聚类分割算法和二维直方图分割算法做对比,总结几种分割方法的有效性,为计算机辅助诊断起到了重要的作用。

基于视觉注意机制的感兴趣区提取方法

以精确获取图像中对象感兴趣区域为目标,提出一种基于视觉注意机制和K均值聚类相结合的感兴趣区提取方法。图像经过视觉特征提取、高斯金字塔多尺度变换后,依据多特征图合并策略生成显著图。采用K均值聚类方法分割图像的候选区域,并结合显著图提取图像感兴趣区。实验结果表明,运用该方法提取的感兴趣区更接近人类的视觉注意过程,并具有一定的抗噪能力。

基于Legendre多项式的MR偏移场仿真与估计

为估计MR图像偏移场,本文利用Legendre多项式来拟合偏移场。用大津阈值法创建模板,消除背景的影响;采用k均值聚类算法分割获取脑组织,脑白质可反应偏移场的变化趋势,利用Legendre多项式来拟合偏移场,估计磁共振图像的偏移场。本算法可对偏移场进行粗略估计,消除偏移场的影响,应用于临床。

An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale HESSIAN matrix dot filtering

A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.

Motion feature descriptor based moving objects segmentation

A novel moving objects segmentation method is proposed in this paper. A modified three dimensional recursive search （3DRS） algorithm is used in order to obtain motion information accurately. A motion feature descriptor （MFD） is designed to describe motion feature of each block in a picture based on motion intensity, motion in occlusion areas, and motion correlation among neighbouring blocks. Then, a fuzzy C-means clustering algorithm （FCM） is implemented based on those MFDs so as to segment moving objects. Moreover, a new parameter named as gathering degree is used to distinguish foreground moving objects and background motion. Experimental results demonstrate the effectiveness of the proposed method.

Research on Image Segmentation Algorithm based on Fuzzy C-mean Clustering

This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the search clustering center has small amount of calculation according to density, so it can greatly improve the calculation speed of fuzzy C- means algorithm. The experimental results show that, this method can make the fuzzy clustering to obviously improve the speed, so it can achieve fast image segmentation.

热带气旋客观定位的红外亮温方差方法

目的热带气旋(TC)是生成于热带或副热带洋面上的强烈天气系统。在TC的监测分析和预报工作中,准确地确定其中心实时地理位置至关重要。此外,TC的精确位置也是TC强度估计的重要参数。对此,提出一种利用偏差角方差定位TC中心的方法。方法首先,从红外卫星云图中截取热带气旋主体云系区域,并分别利用Bezier直方图和K均值聚类方法分割得到主体云系二值图像和红外亮温变化剧烈位置二值图像。其中,主体云系二值图像可将TC的主体云系从卫星红外云图中分割提取出来,用割提取出来的图像进行定位可以剔除掉外散环流的小云块对定位结果的影响;而红外亮温变化剧烈位置二值图像则可分别将TC中心密闭云区,螺旋云带和外散环流的边缘及梯度较大区域分割出来,这些区域是最后TC中心定位的主要依据。将上述两幅二值图像相与得到气旋主体云系红外亮温变化剧烈位置的二值图像,这一步剔除了TC的外散环流,而得到的二值图像便可分别将TC中心密闭云区和螺旋云带的边缘及梯度较大的区域分割出来。然后,对得到的气旋主体云系红外亮温变化剧烈位置二值图像进行Hough变换检测以减小气旋中心的搜索范围。最后,以检测区域内每个像素点为参考中心计算得到偏差角矩阵,并计算偏差角矩阵的方差填入对应检测区域内作为参考中心像素点的位置得到方差矩阵,将方差矩阵中值最小的位置作为气旋中心。因为TC除了少数特别强的时候大多数可以用圆形描述,而绝大多数时候TC要用螺旋线描述,但是具体是几度螺旋线来描述合适很难确定,本文用偏差角的方差就可以衡量这些云带、边缘的偏离状况是否集中,方差越小就表示偏离状况越集中。结果运用该方法对400幅无眼TC红外图像和197幅有眼TC红外图像进行中心定位,分别与中国气象局(CMA)、日本气象厅(JMA)和美国台风预警中心(JTWC)的主观定位结果进行比较并取平均偏差,本文方法对有眼TC定位平均偏差约为27 km,无眼TC平均偏差约为45 km。具体到分别与CMA、JMA和JTWC的比较,对于有眼TC定位偏差分别为26.82 km,26.05 km和27.84 km,无眼TC定位偏差为45.84 km,44.84 km和47.15 km。结论就结果而言,本文方法定位与CMA、JMA的偏差比较接近,与JTWC的偏差较大。就西北太平洋的TC而言,CMA和JMA的定位精度较高,JTWC精度稍低,这是与认知相符合,并且也证明了本文方法具有较高的可信度。此外,本文方法为TC定位提供了新的参考依据。