一种新的知觉分割模型*

根据知觉的最新理论研究和实验结果,提出一种新的知觉模型,并用于考查计算机视觉中的分割问题.分割问题实质上是知觉表达问题,现在分割的困难性可能是知觉上局部到整体的还原论假设造成的;分割可能不是一次完成的,而是经过整体到局部的过程才最终完成.新的知觉模型将对建立更好的分割模型提供理论基础.     

基于分水岭和区域合并的图像分割算法*

提出一种应用分水岭与区域合并相结合的图像分割算法.本方法首先利用数学形态学分水岭算法对图像进行分割,然后对分割后的图像进行相似区域合并,以减少分水岭方法产生的过分割现象,从而得到较好的分割效果.     

基于马尔可夫随机场的嘴唇特征提取方法*

讨论了基于马尔可夫随机场(MRF)模型的融合颜色和边缘信息的嘴唇特征提取方法.首先进行嘴唇区域检测,再结合嘴唇形状特点建立了基于MRF的嘴唇图像分割模型,构造相应的能量函数,并采用改进的最高置信度优先(HCF)算法求解能量函数的最优解,得到图像标记场,进而提取出嘴唇轮廓.结合人脸结构信息,提出了融合鼻孔角度信息的嘴唇特征点提取方法.实验结果表明,此算法具有良好的鲁棒性.     

基于阈值分割的松材线虫病树计数与定位

松材病虫病是最紧迫的威胁之一,最近对中国针叶林造成了严重破坏,为了阻止松材病虫病的快速传播,必须在早期准确检测和计数受感染的树木。提出了一种基于密度图的方法,用于从高分辨率航拍图像中估计具有松材病虫病的树木数量,方法引入了阈值分割、形态处理和超像素技术,以最大限度地减少由于建筑物和岩石等相似背景物体引起的误差。实验表明,所提出的方法优于密度学习 算法,平均绝对误差、均方根误差和绝对误差方差分别从21.3、22.2和127.2减少到8.0、11.7和59.6,计数准确率从54.7%提高到81.3%。     

基于多层次语义特征的建筑立面点云提取方法

建筑立面信息是指建筑物与外部空间接触面的空间分布及属性信息,如何从点云数据中提取建筑立面信息是点云数据处理中的热点和难点。为解决传统格网密度算法在建筑立面点云提取时评价标准单一、适应性不强的问题,综合分析建筑区各类典型地物点云的高程分布、投影密度、法向量分布等局部及整体空间特征,构建由点云单点语义、格网语义及区域语义组成的多层次语义特征描述子,在此基础上提出一种建筑立面点云提取方法,针对建筑立面点云在不同层次语义上的特点设置合理阈值,通过逐层筛选实现建筑立面点云的精确提取。试验结果表明：该算法能在低层、高层以及超高层建筑区等不同场景海量点云中快速准确地实现建筑立面点云提取,算法精度、效率、适应性良好。     

基于平流矢量场和弥散流改进的几何活动轮廓模型

几何活动轮廓通过水平集演化捕获目标边界,然而传统方法忽略了前景物体涉及的全局运动及局部形变,从本质上限制了轮廓的动态谐调范围。提出一种由隐式流驱动的几何活动轮廓方法,用于分割形变对象的动态轮廓。根据流体力学,对平流及弥散的隐式流进行建模,从而直接引导水平集的更新。根据图像序列之间有限的运动观测,采用具有平滑和稀疏项的回归算法以拟合平流矢量场。然后,引入受空间梯度约束的非均匀弥散方程,将最终叠加的隐式流用于水平集演化。提出的几何活动轮廓方法构成了一个统一的扩展框架,保留了已有模型对轮廓拓扑变化的适应性。对多个场景下的动态轮廓分割实验表明,提出方法能准确地分割具有全局运动及不规则形变的前景目标,且具有收敛速度快、稳定性强的优点。     

基于水文分割法的流溪河干流典型断面非点源污染负荷估算

针对流溪河非点源污染防治较严峻局面,对流溪河非点源负荷进行了研究。2013—2014年,对流溪河干流温泉、牛心岭、人和、河口断面进行了203 d的水质及水量监测。根据监测结果结合水文分割法和平均浓度法对各断面的非点源污染负荷进行了计算,并分析了非点源污染的特点。结果表明:流溪河干流典型断面污染物以NH3-N、TN和TP为主;越往下游,监测断面各指标平均浓度越大,水质越差;上游断面洪水期各主要指标浓度大于枯水期,下游断面则是枯水期各主要指标浓度大于洪水期;监测断面各指标非点源污染负荷占总负荷的比例为44.22%~96.17%,平均为76.99%;从上游往下游,非点源污染物负荷占总负荷的比例在减小;各断面年污染负荷基本上是以洪水期负荷为主,洪水期污染负荷则以非点源污染负荷为主;河口断面污染物年总负荷中,58.74%的TN、67.22%的TP和65.45%的COD来源于非点源污染。非点源污染在流溪河水体污染中占较大比重,对水质影响不容忽视。     

基于全散度的C-V模型阈值法

为了提高传统C-V模型的收敛速度并降低其对噪声的敏感性,提出基于全散度的C-V模型及其快速阈值分割算法。将Bregman散度与全散度统一获得新的全散度,并将其引入C-V模型的拟合偏差项,提高图像灰度值与分割区域平均灰度偏差值计算的鲁棒性。同时,采用变分水平集理论获得基于直方图的快速阈值计算方法。实验结果表明,该方法分割效果及收敛速度得到提高,且具有较好的鲁棒性和抗噪性。     

Data-driven hair segmentation with isomorphic manifold inference

Hair segmentation is challenging due to the diverse appearance, irregular region boundary and the influence of complex background. To deal with this problem, we propose a novel data-driven method, named Isomorphic Manifold Inference (IMI). The IMI method assumes the coarse probability map and the binary segmentation map as a couple of isomorphic manifolds and tries to learn hair specific priors from manually labeled training images. For an input image, firstly, the method calculates a coarse probability map. Then it exploits regression techniques to obtain the relationship between the coarse probability map of the test image and those of training images. Finally, this relationship, i.e., a coefficient set, is transferred to the binary segmentation maps and a soft segmentation of the test image will be achieved by a linear combination of those binary maps. Further, we employ this soft segmentation as a shape cue and integrate it with color and texture cues into a unified segmentation framework. A better segmentation is achieved by the Graph Cuts optimization. Extensive experiments are conducted to validate effectiveness of the IMI method, compare contributions of different cues and investigate the generalization of IMI method. The results strongly encourage our method.     

Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform

This paper proposes a novel automatic method for the moment segmentation and peak detection analysis of heart sound (HS) pattern, with special attention to the characteristics of the envelopes of HS and considering the properties of the Hilbert transform (HT). The moment segmentation and peak location are accomplished in two steps. First, by applying the Viola integral waveform method in the time domain, the envelope (E_T) of the HS signal is obtained with an emphasis on the first heart sound (S1) and the second heart sound (S2). Then, based on the characteristics of the E_T and the properties of the HT of the convex and concave functions, a novel method, the short-time modified Hilbert transform (STMHT), is proposed to automatically locate the moment segmentation and peak points for the HS by the zero crossing points of the STMHT. A fast algorithm for calculating the STMHT of E_T can be expressed by multiplying the E_T by an equivalent window (W_E). According to the range of heart beats and based on the numerical experiments and the important parameters of the STMHT, a moving window width of N = 1 s is validated for locating the moment segmentation and peak points for HS. The proposed moment segmentation and peak location procedure method is validated by sounds from Michigan HS database and sounds from clinical heart diseases, such as a ventricular septal defect (VSD), an aortic septal defect (ASD), Tetralogy of Fallot (TOF), rheumatic heart disease (RHD), and so on. As a result, for the sounds where S2 can be separated from S1, the average accuracies achieved for the peak of S1 (AP₁), the peak of S2 (AP₂), the moment segmentation points from S1 to S2 (AT₁₂) and the cardiac cycle (ACC) are 98.53%, 98.31% and 98.36% and 97.37%, respectively. For the sounds where S1 cannot be separated from S2, the average accuracies achieved for the peak of S1 and S2 (AP₁₂) and the cardiac cycle ACC are 100% and 96.69%.