High frequency geometric detail manipulation and editing for point-sampled surfaces

In this paper, based on the new definition of high frequency geometric detail for point-sampled surfaces, a new approach for detail manipulation and a detail-preserving editing framework are proposed. Geometric detail scaling and enhancement can always produce fantastic effects by directly manipulating the geometric details of the underlying geometry. Detail-preserving editing is capable of preserving geometric details during the shape deformation of point-sampled model. For efficient editing, the point set of the model is first clustered by a mean shift scheme, according to its anisotropic geometric features and each cluster is abstracted as a simplification sample point (SSP). Our editing operation is implemented by manipulating the SSP first and then diffusing the deformation to all sample points on the underlying geometry. As a postprocessing step, a new up-sampling and relaxation procedure is proposed to refine the deformed model. The effectiveness of the proposed method is demonstrated by several examples.     

基于RB-K平均带宽设定的Adaptive Mean shift

Meanshift是一个用在图像滤波、图像分割中的迭代过程。对于迭代过程，带宽的设定很重要。固定带宽的Meanshift方法对于输入数据适应能力差，而现有可变带宽估计方法有的对于固定带宽的方法改进并不明显，有的时间复杂度较高，因此对现有带宽估计方法做了改进，提出了一种新的带宽估计方法，即采用K平均聚类算法进行带宽估计，并通过实验证实了该方法的有效性。     

GPU-based rendering of point-sampled water surfaces

Particle-based simulations are widely used to simulate fluids. We present a real-time rendering method for the results of particle-based simulations of water. Traditional approaches to visualize the results of particle-based simulations construct water surfaces that are usually represented by polygons. To construct water surfaces from the results of particle-based simulations, a density function is assigned to each particle and a density field is computed by accumulating the values of the density functions of all particles. However, the computation of the density field is time consuming. To address this problem, we propose an efficient calculation of density field using a graphics processing unit (GPU). We present a rendering method for water surfaces sampled by points. The use of the GPU permits efficient simulation of optical effects, such as refraction, reflection, and caustics.     

A quasi-Monte Carlo method for computing areas of point-sampled surfaces

A novel and efficient quasi-Monte Carlo method for computing the area of a point-sampled surface with associated surface normal for each point is presented. Our method operates directly on the point cloud without any surface reconstruction procedure. Using the Cauchy-Crofton formula, the area of the point-sampled surface is calculated by counting the number of intersection points between the point cloud and a set of uniformly distributed lines generated with low-discrepancy sequences. Based on a clustering technique, we also propose an effective algorithm for computing the intersection points of a line with the point-sampled surface. By testing on a number of point-based models, experiments suggest that our method is more robust and more efficient than those conventional approaches based on surface reconstruction.     

均值漂移算法的研究与应用

对均值漂移算法的理论和应用作一全面的综述．首先根据密度函数的非参数估计推导出均值漂移公式的一般形式,说明了均值漂移迭代算法的步骤及收敛性;然后重点讨论核函数的选择以及带宽矩阵的计算等关键技术;最后归纳了均值漂移算法在模式检测、聚类、图像分割以及物体实时跟踪等方面的应用,并展望了均值漂移算法在理论和应用中的研究方向．     

一种基于运动矢量分析的Mean shift目标跟踪算法

Mean shift算法作为一种非参密度估计算法,目前已被广泛应用于视频运动目标的跟踪。该算法具有运算效率快,对目标变形、旋转不敏感,在部分遮挡的情况下有一定鲁棒性等特点,但该算法在运动目标速度过快的情况下,由于没有考虑利用目标的运动方向和速度信息,因此在跟踪快速运动目标时容易造成跟踪丢失。针对此问题,提出了一种基于运动矢量分析与Mean shift跟踪算法相结合的新方法,即首先对视频编码过程中产生的运动矢量进行概率统计分析,以获取目标运动方向与运动速度的估计值,再以此修正Mean shift运动候选区域的中心位置,使每次搜索开始时,候选中心位置更接近实际目标中心位置。通过与传统的Mean shift算法的跟踪实验比较可见,新算法不仅提高了快速运动目标跟踪的精度,而且减少了算法的搜索迭代次数,从而提高了运算效率。该算法可适用于智能视频监控设备中的视频编码与目标跟踪同时计算的情况,实验结果表明,该算法是有效可行的。     

基于Mean shift算法的煤岩分界识别

提出了一种基于Mean shift算法的煤岩分界识别方案。首先介绍了Mean shift算法原理,通过关联图像的像素位置向量和灰度值构建了一个空间联合域;然后给出了适用于煤岩图像分割的带宽参数选择方法,以去除虚假孤立区域和杂散边界;最后利用煤岩图像的人造边界和真实边界进行仿真,结果表明Mean shift算法较K-means算法能更准确地获得煤岩分界线。     

Mean shift based gradient vector flow for image segmentation

In recent years, gradient vector flow (GVF) based algorithms have been successfully used to segment a variety of 2-D and 3-D imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods may lead to biased segmentation results. In this paper, we propose MSGVF, a mean shift based GVF segmentation algorithm that can successfully locate the correct borders. MSGVF is developed so that when the contour reaches equilibrium, the various forces resulting from the different energy terms are balanced. In addition, the smoothness constraint of image pixels is kept so that over- or under-segmentation can be reduced. Experimental results on publicly accessible datasets of dermoscopic and optic disc images demonstrate that the proposed method effectively detects the borders of the objects of interest.     

基于Mean shift的核窗宽自适应目标跟踪新算法

针对传统均值漂移算法(Mean shift)中核函数直方图对目标特征描述较弱、跟踪过程中核函数带宽的保持不变的缺点,提出了一种新的核函数带宽可变的Mean shift跟踪算法.在特定的色彩空间中,统计落入各区间的像素个数.并对各区间像素的位置建立高斯分布模型,采用二阶空间直方图实现目标建模,强化目标特征描述提高了跟踪的鲁棒性;结合边缘检测与角点检测选取目标特征点估算目标仿射模型确定伸缩尺度.适应目标多自由度变化下的跟踪.实验结果证明,该算法比原有算法跟踪效果更加准确和可靠.     

Defining, contouring, and visualizing scalar functions on point-sampled surfaces

This paper addresses the definition, contouring, and visualization of scalar functions on unorganized point sets, which are sampled from a surface in 3D space; the proposed framework builds on moving least-squares techniques and implicit modeling. Given a scalar function f:P→R, defined on a point set P, the idea behind our approach is to exploit the local connectivity structure of the k-nearest neighbor graph of P and mimic the contouring of scalar functions defined on triangle meshes. Moving least-squares and implicit modeling techniques are used to extend f from P to the surface M underlying P. To this end, we compute an analytical approximation of f that allows us to provide an exact differential analysis of , draw its iso-contours, visualize its behavior on and around M, and approximate its critical points. We also compare moving least-squares and implicit techniques for the definition of the scalar function underlying f and discuss their numerical stability and approximation accuracy. Finally, the proposed framework is a starting point to extend those processing techniques that build on the analysis of scalar functions on 2-manifold surfaces to point sets.     

基于Mean shift和轨迹预测的目标跟踪算法

针对目标跟踪过程中出现的定位偏差问题,提出了Mean shift和轨迹预测相结合的运动目标跟踪算法.该算法首先根据目标已知位置信息采用最小二乘法拟合运动轨迹并得到预测位置,然后利用Mean shift算法得到目标最终位置.通过计算搜索误差判断是否发生严重遮挡情况,并给出相应处理策略.进行了一系列实验,验证了算法的有效性,并将实验结果与其他算法比较,表明该算法有效地提高了快速运动目标跟踪的精度,具有较强的鲁棒性.     

基于均值漂移与粒子滤波的手势跟踪算法研究

基于视觉的手势跟踪技术在虚拟现实、人机交互、视觉监控等领域均有着广阔的应用。本文主要研究用于人机交互的手势跟踪,以颜色特征作为目标的表征方式,并结合MeanShift均值移住提出了一种基于颜色直方图的粒子滤波跟踪方法。仿真实验结果表明,本文研究的方法效果较好,能准确的对序列图中的手势进行跟踪。     

Two-stage image denoising by principal component analysis with local pixel grouping

This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local structures, a pixel and its nearest neighbors are modeled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. Such an LPG procedure guarantees that only the sample blocks with similar contents are used in the local statistics calculation for PCA transform estimation, so that the image local features can be well preserved after coefficient shrinkage in the PCA domain to remove the noise. The LPG-PCA denoising procedure is iterated one more time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage. Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art denoising algorithms.     

基于目标模型自适应更新的mean shift跟踪算法

本文提出一种自动更新mean shift跟踪模型的算法。该算法采用Kalman滤波对跟踪系统下一帧的目标模型进行预测,通过对滤波残余误差样本的假设检验,提出一种更新机制。实验结果表明,跟踪系统可以在目标被遮挡或形状改变的情况下,有效地更新目标模型,实现实时目标跟踪。     

Nonlinear Mean Shift over Riemannian Manifolds

The original mean shift algorithm is widely applied for nonparametric clustering in vector spaces. In this paper we generalize it to data points lying on Riemannian manifolds. This allows us to extend mean shift based clustering and filtering techniques to a large class of frequently occurring non-vector spaces in vision. We present an exact algorithm and prove its convergence properties as opposed to previous work which approximates the mean shift vector. The computational details of our algorithm are presented for frequently occurring classes of manifolds such as matrix Lie groups, Grassmann manifolds, essential matrices and symmetric positive definite matrices. Applications of the mean shift over these manifolds are shown.     

Real-time hand tracking using a mean shift embedded particle filter

Particle filtering and mean shift (MS) are two successful approaches to visual tracking. Both have their respective strengths and weaknesses. In this paper, we propose to integrate advantages of the two approaches for improved tracking. By incorporating the MS optimization into particle filtering to move particles to local peaks in the likelihood, the proposed mean shift embedded particle filter (MSEPF) improves the sampling efficiency considerably. Our work is conducted in the context of developing a hand control interface for a robotic wheelchair. We realize real-time hand tracking in dynamic environments of the wheelchair using MSEPF. Extensive experimental results demonstrate that MSEPF outperforms the MS tracker and the conventional particle filter in hand tracking. Our approach produces reliable tracking while effectively handling rapid motion and distraction with roughly 85% fewer particles. We also present a simple method for dynamic gesture recognition. The hand control interface based on the proposed algorithms works well in dynamic environments of the wheelchair.     

基于改进的均值漂移算法的目标跟踪

提出了一种基于目标颜色特征的改进的均值漂移算法，对符合颜色模板的目标点不论其在直方图中的概率大小，都赋予相同的最大权值，使目标最大限度地成为密度极值区，以克服干扰影响，并提出了一种分块检测遮挡算法，遮挡期间不更新颜色模板，以保证遮挡后恢复准确的跟踪。实验结果表明该算法具有较强的鲁棒性，能有效实现复杂场景下的目标跟踪。     

Sparse non-linear denoising: Generalization performance and pattern reproducibility in functional MRI

We investigate sparse non-linear denoising of functional brain images by kernel principal component analysis (kernel PCA). The main challenge is the mapping of denoised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed. In many applications, including functional magnetic resonance imaging (fMRI) data which is the application used for illustration in the present work, it is of interest to denoise a sparse signal. To meet this objective we investigate sparse pre-image reconstruction by Lasso regularization. We find that sparse estimation provides better brain state decoding accuracy and a more reproducible pre-image. These two important metrics are combined in an evaluation framework which allow us to optimize both the degree of sparsity and the non-linearity of the kernel embedding. The latter result provides evidence of signal manifold non-linearity in the specific fMRI case study.     

A total variation recursive space-variant filter for image denoising

Total Variation (TV) regularization is a widely used convex but non-smooth regularizer in image restoration and reconstruction. Many algorithms involve solving a denoising problem as an intermediate step or in each iteration. Most existing solvers were proposed in the context of a specific application. In this paper, we propose a denoising method which can be used as a proximal mapping (denoising operator) for noises other than additive and Gaussian. We formulate the Maximum A-Posteriori (MAP) estimation in terms of a spatially adaptive and recursive filtering operation on the Maximum Likelihood (ML) estimate. The only dependence on the model is the ML estimate and the second order derivative, which are computed at the beginning and remain fixed throughout the iterative process. The proposed method generalizes the MAP estimation with a quadratic regularizer using an infinite impulse response filter, to the case with TV regularization. Due to the fact that TV is non-smooth and has spatial dependencies, the resulting filter after reweighted least squares formulation of the TV term, is recursive and spatially variant. The proposed method is an instance of the Majorization–Minimization (MM) algorithms, for which convergence conditions are defined and can be shown to be satisfied by the proposed method. The method can also be extended to image inpainting and higher order TV in an intuitively straight-forward manner.     

动态成像条件下基于SURF和Mean shift的运动目标高精度检测

针对动态成像条件下运动目标检测的难点问题,提出了一种将SURF特征和Mean shift图像分割相结合的高精度运动目标检测方法.首先利用SURF特征进行图像配准,以补偿背景图像的运动漂移;然后利用差分求积二值化和形态学滤波方法检测出运动目标区域;最后结合Mean shift图像分割方法实现运动目标的精确检测.通过一系列实拍视频的运动目标检测实验验证了此算法的有效性和可行性.实验结果表明,此方法能精确检测出动态成像条件下所形成的动态背景中的运动目标,而且具有良好的鲁棒性和抗噪能力,对于光照条件和亮度变化等不利因素也有较强的适应能力.