基于离散水平集方法的Chan-Vese模型

采用离散形式的水平集函数代替传统的符号距离函数,提出基于离散水平集方法的Chan-Vese模型。采用变分方法得到水平集函数演化的梯度降方程,对得到的演化方程进行半隐式离散求解和数值算例验证,并与传统的符号距离函数方法进行对比,以验证离散水平集方法在图像分割中的优势。实验结果表明,基于离散水平集方法的Chan-Vese模型分割速度和精度比用符号距离函数实现的Chan-Vese模型有明显提高。     

带噪声的点云数据的隐式曲面重建算法

针对三维扫描获取的带噪声和离群点的点云数据,提出了隐式曲面重建算法.用基于均值漂移的滤波算子,把每个采样点移动到核密度函数的局部最大值的点,以限制噪声并剔除离群点.然后,用自适应的八叉树空间划分方法将降噪后的采样点数据分成小的子域.最后,在每个子域内计算局部形状函数,并用单位分解法将所有的局部形状函数加权求和以逼近模型的全局函数.实验表明,该方法是鲁棒的,能用带噪声和离群点的点云数据实现多分辨三维重建,得到细节丰富的曲面.     

径向基函数在图像修复中的应用

提出了径向基函数的图像修复算法．由用户手工标记出要修复的区域，算法自动计算修复区域的轮廓并沿法向扩张，确定合适的径向基函数重构区域，并将该区域内图像上的每个点的像素值看成高度场中该点的高度值，这样一张图像就张成了以像素值为高度的三维曲面，从而将图像修复问题转化为三维散乱数据点的曲面重建问题．该算法能正确，稳定地处理各种破损区域。     

动摩擦接触问题速率型变分形式及等价性

推导动态接触问题平衡方程与接触约束条件的一种速率型变分形式。通过定义实值赋范可测向量函数空间中的正则虚速度函数，应用系统能量平衡关系将局部状态转变为积分弱形式的变分方程。这种变分方程适用于不同材料的有限变形情况。引入非负松弛函数和切向接触应力函数空间，提出接触约束条件的互补变分关系。证明了积分弱形式的变分方程与逐点满足的平衡方程、接触条件是完全等价的。     

基于球面图像的三角网格模型分割

为了从局部形状的角度实现对三角网格模型的管理和重用,提出了一种基于球面图像的三角网格模型分割方法.通过球面参数化及球面划分,将三角网格模型的表面属性信息映射到球面图像中;利用成熟的区域生长、区域合并图像分割算法对球面图像进行分割;将球面图像的分割结果转换为三角网格模型的分割结果.实验结果表明:该分割方法可以对不均匀的低分辨率三角网格模型进行有效分割,降低了几何属性估算对分割结果的影响,不会发生过分割现象,不需要进行分割的后续处理.     

未标定图像序列的隐式曲面重建方法

为了提高未标定图像序列三维重建得到的几何模型的质量,提出特征点检测算法,以得到更多的匹配点.其主要思想是在首帧图像指定密集的网格,在网格点附近确定最容易跟踪的特征点,利用迭代方法得到子像素精度的特征点坐标,然后用稀疏特征集的金字塔Lucas-Kanade光流跟踪算法跟踪这些特征点,再用自标定算法,重建出相对均匀和稠密的三维点云,最后利用基于径向基函数(RBF)的隐式曲面重建算法,生成目标的表面模型. 多个图像序列的重建结果表明,本方法对纹理丰富的场景能够获得较好的重建结果.     

低照度边缘增强的语义分割模型研究

为提高对低照度图像的语义分割精度,提出了一种基于RPN的边缘增强语义分割模型(EESN)。在该模型中,首先利用深度残差网络提取图像的高阶语义特征,并通过RPN快速生成待分割目标候选区域;然后,利用设计的融合算法对候选区域进行融合,并剔除重复的候选区域;最后,对融合的目标候选区域做低照度边缘搜索,并利用失真代价较小的局部增强算法对低照度边缘进行特征增强。将EESN用于Pascal VOC12和Cityscapes两个数据集的语义分割中,分别获得了81.2%和67.6%的mIoU,该结果证明了EESN对具有低照度边缘的图像具有较好的分割性能。     

基于动态栅格与隐马尔科夫模型前车换道意图识别

针对前车换道意图识别预测问题,提出基于动态栅格地图与混合高斯隐马尔科夫模型结合的识别预测方法,建立以自车车头中心点为零点的动态栅格地图描述前车的位置信息,基于语义分割的方法获取前方车辆的轮廓特征与车道线,并分析前方障碍车辆的相关运动状态信息。将动态栅格地图与混合高斯隐马尔科夫模型相结合,识别预测前车换道行为。利用可观测的前车行驶信息推测出其隐藏的换道行为,并提取NGSIM数据集的相关观测参数,训练验证识别预测模型,结果表明模型达到较好的识别预测效果。     

移动机器人在曲面上的建模与分析

对移动在曲面上的机器人的动态模型进行了研究.在曲面上建立了移动机器人动态模型,通过流形上的李群理论对模型进行分析,指出曲面上移动机器人是可控的,并且在曲面上存在时间最优轨道.利用庞特里亚金最大值原理,得到移动机器人模型的结构方程,并通过结构方程来分析曲面上移动机器人的时间最优轨道,指出时间最优轨道的控制变量完全由结构方程和曲面的高斯曲率决定,并且在高斯曲率小于零的曲面上,机器人的时间最优轨道由3种基本轨道的有限组合而成,给出了这3种基本轨道以单位速度向前(后)移动时角速度变化的具体分布情况.     

基于直接刚度法的三段刚度压杆非线性分析

为分析考虑二阶效应的分段刚度压杆内力及位移,根据位移控制方程,建立了变刚度压杆位移和转角方程;根据杆端位移边界条件和变刚度截面处连续条件,得到了位移系数;根据压杆内力方程,建立了以矩阵形式表达的刚度平衡方程,变换得到了变刚度压杆刚度矩阵模型. 将本模型用于分段刚度压杆分岔失稳临界荷载计算,并与解析解、插值形函数单元模型结果进行对比与分析,验证了模型的精度和效率. 结果表明:采用插值形函数法计算压杆临界荷载时,若只划分一个单元,其计算结果与理论解的相对误差最高可达43.24%,随着划分单元数量增加,相对误差降为0.023%;采用基于直接刚度法得到的变刚度压杆单元刚度矩阵计算压杆临界荷载时,只需划分一个单元,即可保证计算结果与理论解一致,该矩阵可用于压杆的非线性分析中,得到压杆内力及位移的精确解.      

A Bintree Energy Approach for Colour Image Segmentation Using Adaptive Channel Selection

A new hierarchical approach called bintree energy segmentation was presented for color image seg-mentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images,from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the "best" chan-nel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.     

Curvature estimation of point set data based on the moving-least square surface

Curvature estimation is a basic step in many point relative applications such as feature recognition, segmentation, shape analysis and simplification. This paper proposes a moving-least square (MLS) surface based method to evaluate curvatures for unorganized point cloud data. First a variation of the projection based MLS surface is adopted as the underlying representation of the input points. A set of equations for geometric analysis are derived from the implicit definition of the MLS surface. These equations are then used to compute curvatures of the surface. Moreover, an empirical formula for determining the appropriate Gaussian factor is presented to improve the accuracy of curvature estimation. The proposed method is tested on several sets of synthetic and real data. The results demonstrate that the MLS surface based method can faithfully and efficiently estimate curvatures and reflect subtle curvature variations. The comparisons with other curvature computation algorithms also show that the presented method performs well when handling noisy data and dense points with complex shapes.     

FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE

Automaticbraintissuesegmentationfrommag neticresonanceimages(MRI)isofgreatimportance forresearchandclinicalstudyofmuchneurological pathology.Duringthepastdecade,theMRIhashad agreatimpactonthediagnosticimagingofmosthu manorgansystem.Thesegmentationofbrai…     

MEDICAL IMAGE SEGMENTATION BASED ON A MODIFIED LEVEL SET ALGORITHM

Imagesegmentationplaysanimportantrolein imageanalysisaswellasinhigh levelimageinter pretationandunderstandingsuchasrobotvision, objectrecognition,andmedicalimaging.Numerous segmentationmethodshavebeendeveloped.How ever,medicalimagesareoftencorruptedbyno…     

A bintree energy approach for colour image segmentation using adaptive channel selection

A new hierarchical approach called bintree energy segmentation was presented for color image segmentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images, from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the ＂best＂ channel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.     

Image series segmentation and improved MC algorithm

A semiautomatic segmentation method based on active contour is proposed for computed tomography （CT） image series. First, to get initial contour, one image slice was segmented exactly by C-V method based on Mumford-Shah model. Next, the computer will segment the nearby slice automatically using the snake model one by one. During segmenting of image slices, former slice boundary, as next slice initial contour, may cross over next slice real boundary and never return to right position. To avoid contour skipping over, the distance variance between two slices is evaluated by an threshold, which decides whether to initiate again. Moreover, a new improved marching cubes （MC） algorithm based on 2D images series segmentation boundary is given for 3D image reconstruction. Compared with the standard method, the proposed algorithm reduces detecting time and needs less storing memory. The effectiveness and capabilities of the algorithm were illustrated by ,experimental results.     

细胞图像自动分割方法研究

提出一种细胞图像的自动分割与定位方法．首先将直方图分割所得二值图像的距离变换作为边缘提取的指导知识，进行细胞图像的精确分割；然后利用分割边缘点的梯度方向和几何知识确定细胞的中心．该方法既能对非重叠细胞也能对重叠细胞进行细胞的分割与定位．     

Active Contours and Mumford-Shah Segmentation Based on Level Sets

IntroductionThe basic idea in active contour models orsnakes is to evolve a curve,subject to constraintsfrom a given image,in order to detect objects inthat image.For instance,starting with a curvearound the object to be detected,the curve movestoward its interior normal and has to stop on theboundary of the object.LetΩ be a bounded open subset of R2 ,with Ω its boundary. Let u0 ∶Ω→ R be a given image,and C( s)∶ [0 ,1 ]→ R2 be a parameterized curve. Inthe classical snakes and active…     

基于视觉注意机制 PCNN 模型的车牌 图像分割方法

车辆图像中车牌具有所占比例小、位置不固定、大小不一,以及分割易受光照影 响的特点.因此,车牌图像的分割始终是车辆跟踪、车辆识别等领域中的难点问题.针对以 上问题,本文提出了基于视觉注意机制脉冲耦合神经网络模型的车牌图像分割方法.该方 法将视觉注意机制中的数据驱动模式和任务驱动模式相结合. 数据驱动模式中,通过对 PCNN 模型细胞感受野功能的完善,使优化 PCNN 模型具有了尺度性和方向性.任务驱 动模式中,针对不同尺度的分割,利用组合不变矩和局部灰度熵,自适应地确定目标的特 征尺度和最佳尺度,并确定该目标最终的分割结果.经实验验证,该方法对车牌图像具有 较好的分割效果.