A robust hybrid method for nonrigid image registration

A nonrigid registration method is proposed to automatically align two images by registering two sets of sparse features extracted from the images. Motivated by the paradigm of Robust Point Matching (RPM) algorithms [1] and [2], which were originally proposed for shape registration, we develop Robust Hybrid Image Matching (RHIM) algorithm by alternatively optimizing feature correspondence and spatial transformation for image registration. Our RHIM algorithm is built to be robust to feature extraction errors. A novel dynamic outlier rejection approach is described for removing outliers and a local refinement technique is applied to correct non-exactly matched correspondences arising from image noise and deformations. Experimental results demonstrate the robustness and accuracy of our method.     

Robust track-to-track association in the presence of sensor biases and missed detections

The paper addresses the problem of robust track-to-track association in the presence of sensor biases and missed detections. Under the condition of large range biases with sensors, it is validated that the structural difference between two sets of local tracks from different sensors can be described by a non-rigid transformation. After that, we turn the robust track-to-track association problem into the non-rigid point matching problem in the framework of TPS-RPM (Thin Plate Spline-Robust Point Matching). Further, to improve the performance of the track-to-track association, the structural feature is introduced for each local track, and the structural similarity is incorporated by regularizing the energy function of the TPS-RPM algorithm. Simulation results demonstrate the effectiveness of the proposed approaches compared with competing algorithms.     

鲁棒的加权核主成分分析算法

为减弱离群点对数据处理的影响, 提出了一种鲁棒的加权核主成分分析算法。利用核函数将样本投影到核空间, 在核空间构建一个样本加权重建误差最小模型, 最大限度地提取数据中的非线性信息并降低离群点样本的干扰。在Yale人脸库和UCI数据集上的实验表明, 该方法具有很好的识别率, 尤其对离群点样本具有较好的鲁棒性。     

Robust point matching for nonrigid shapes by preserving local neighborhood structures

In previous work on point matching, a set of points is often treated as an instance of a joint distribution to exploit global relationships in the point set. For nonrigid shapes, however, the local relationship among neighboring points is stronger and more stable than the global one. In this paper, we introduce the notion of a neighborhood structure for the general point matching problem. We formulate point matching as an optimization problem to preserve local neighborhood structures during matching. Our approach has a simple graph matching interpretation, where each point is a node in the graph, and two nodes are connected by an edge if they are neighbors. The optimal match between two graphs is the one that maximizes the number of matched edges. Existing techniques are leveraged to search for an optimal solution with the shape context distance used to initialize the graph matching, followed by relaxation labeling updates for refinement. Extensive experiments show the robustness of our approach under deformation, noise in point locations, outliers, occlusion, and rotation. It outperforms the shape context and TPS-RPM algorithms on most scenarios.     

对随机投影算法的离群数据挖掘技术研究

[d]维点集离群数据挖掘技术是目前数据挖掘领域的研究热点之一。当前基于距离或最近邻概念进行离群数据挖掘时,在高维数据情况下的挖掘效果不佳,鉴于此,将基于角度的离群因子应用到高维离群数据挖掘中,提出一种新的基于随机投影算法的离群数据挖掘方案,它只需要用接近线性时间的方法就能预测所有数据点的基于角度的离群因子。该方法可以用于并行环境进行并行加速。对近似质量进行了理论分析,以保证算法的可靠性。合成和真实数据集实验结果表明,对超高维数据集,该方法效率高、可伸缩性强。     

Outlier elimination using granular box regression

A regression method desires to fit the curve on a data set irrespective of outliers. This paper modifies the granular box regression approaches to deal with data sets with outliers. Each approach incorporates a three-stage procedure includes granular box configuration, outlier elimination, and linear regression analysis. The first stage investigates two objective functions each applies different penalty schemes on boxes or instances. The second stage investigates two methods of outlier elimination to, then, perform the linear regression in the third stage. The performance of the proposed granular box regressions are investigated in terms of: volume of boxes, insensitivity of boxes to outliers, elapsed time for box configuration, and error of regression. The proposed approach offers a better linear model, with smaller error, on the given data sets containing varieties of outlier rates. The investigation shows the superiority of applying penalty scheme on instances.     

基于粗糙熵的离群点检测方法及其在无监督入侵检测中的应用

香农的信息熵被广泛用于粗糙集.利用粗糙集中的粗糙熵来检测离群点,提出一种基于粗糙熵的离群点检测方法,并应用于无监督入侵检测.首先,基于粗糙熵提出一种新的离群点定义,并设计出相应的离群点检测算法-–基于粗糙熵的离群点检测(rough entropy-based outlier detection,REOD);其次,通过将入侵行为看作是离群点,将REOD应用于入侵检测中,从而得到一种新的无监督入侵检测方法.通过多个数据集上的实验表明,REOD具有良好的离群点检测性能.另外,相对于现有的入侵检测方法,REOD具有较高的入侵检测率和较低的误报率,特别是其计算开销较小,适合于在海量高维的数据中检测入侵.     

基于深度信念网络的异常点集间的匹配算法

在存在异常值、噪声或缺失点的情况下,损坏的点集中很难区分异常点与正常点,并且点集之间的匹配关系也会受到这些异常点的影响。基于正常点之间存在某种联系以及正常点与异常点之间存在差异的先验知识,提出将点集间匹配关系的估计问题模型化为机器学习的过程。首先,考虑到两个正常点集之间的误差特征,提出了一种基于深度信念网络（DBN）的学习方法来训练具有正常点集的网络;然后,使用训练好的DBN测试损坏的点集,根据设置的误差阈值在网络输出端就可以识别异常值和不匹配的点。对存在噪声和缺失点的2D、3D点集所做的匹配实验中,利用模型预测样本的结果定量评估了点集间的匹配性能,其中匹配的精确率可以达到94%以上。实验结果表明,所提算法可以很好地检测点集中的噪声,即使在数据缺失的情况下,该算法也可以识别几乎所有的匹配点。     

FORA: An OWO based framework for finding outliers in web usage mining

Handling outliers are one of the primary concerns of today’s data mining techniques. The concept of outliers, it’s handling, and diagnosis is context specific and varies according to the field of application. The existence of outliers while mining web data is inevitable by virtue of unique characteristic features exhibited by a typical web user. As the output of a regression algorithm is always different from the actual value, it poses a challenge to the knowledge workers and researchers about the notion of an outlier in such cases. In this paper, we propose to develop the concept of an outlier with respect to regression analysis of any Web-based dataset. A framework to find outliers in the output of a regression algorithm is being formulated with the help of Ordered Weighted operators. The underlying idea is to find an error rectification value, ϵ, that will work, in association with the predicted value from the regression model and then help to distinguish an outlier. This will, in addition, also provide a possible range of deviation from the predicted output. A case study on a web dataset is being done to show the usefulness of the proposed approach.     

A new point matching algorithm for non-rigid registration

Feature-based methods for non-rigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, feature-based non-rigid matching requires us to automatically solve for correspondences between two sets of features. In addition, there could be many features in either set that have no counterparts in the other. This outlier rejection problem further complicates an already difficult correspondence problem. We formulate feature-based non-rigid registration as a non-rigid point matching problem. After a careful review of the problem and an in-depth examination of two types of methods previously designed for rigid robust point matching (RPM), we propose a new general framework for non-rigid point matching. We consider it a general framework because it does not depend on any particular form of spatial mapping. We have also developed an algorithm—the TPS–RPM algorithm—with the thin-plate spline (TPS) as the parameterization of the non-rigid spatial mapping and the softassign for the correspondence. The performance of the TPS–RPM algorithm is demonstrated and validated in a series of carefully designed synthetic experiments. In each of these experiments, an empirical comparison with the popular iterated closest point (ICP) algorithm is also provided. Finally, we apply the algorithm to the problem of non-rigid registration of cortical anatomical structures which is required in brain mapping. While these results are somewhat preliminary, they clearly demonstrate the applicability of our approach to real world tasks involving feature-based non-rigid registration.     

Consolidation of Low‐quality Point Clouds from Outdoor Scenes

The emergence of laser/LiDAR sensors, reliable multi‐view stereo techniques and more recently consumer depth cameras have brought point clouds to the forefront as a data format useful for a number of applications. Unfortunately, the point data from those channels often incur imperfection, frequently contaminated with severe outliers and noise. This paper presents a robust consolidation algorithm for low‐quality point data from outdoor scenes, which essentially consists of two steps: 1) outliers filtering and 2) noise smoothing. We first design a connectivity‐based scheme to evaluate outlierness and thereby detect sparse outliers. Meanwhile, a clustering method is used to further remove small dense outliers. Both outlier removal methods are insensitive to the choice of the neighborhood size and the levels of outliers. Subsequently, we propose a novel approach to estimate normals for noisy points based on robust partial rankings, which is the basis of noise smoothing. Accordingly, a fast approach is exploited to smooth noise, while preserving sharp features. We evaluate the effectiveness of the proposed method on the point clouds from a variety of outdoor scenes.     

基于密度的离群噪声点检测

针对三维扫描仪获取的带噪声和离群点的点云数据,提出了基于局部离群点概念的去噪算法。通过k-近邻(KNN)搜索建立散乱点之间的拓扑关系,进而计算当前测点的局部离群因子以衡量该点的离群程度,从而限制噪声并剔除离群点。重点解决了高密度扫描点云周围分布的低密度离群噪声点的识别问题。实验结果证明,该算法能有效检测出紧挨模型边界的噪声点,并最大限度地保持模型边界。     

一种基于频繁模式的增量式异常检测方法

异常点是数据集中看起来与其他数据有着明显差别的点或者区域。异常点往往并不是错误,并且经常包含比较重要的信息。本文提出一种基于频繁模式的增量式异常检测方法,定义增量式异常检测异常点的性质,使用异常点因子来检测候选集,然后通过改进候选集的来进行迭代确定异常点,最后使用数据对该算法效率进行验证。     

Bounded Influence Support Vector Regression for Robust Single-Model Estimation

Support vector regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with severe outlier contamination of both response and predictor variables commonly encountered in numerous real applications. In this paper, we present a bounded influence SVR, which downweights the influence of outliers in all the regression variables. The proposed approach adopts an adaptive weighting strategy, which is based on both a robust adaptive scale estimator for large regression residuals and the statistic of a “kernelized” hat matrix for leverage point removal. Thus, our algorithm has the ability to accurately extract the dominant subset in corrupted data sets. Simulated linear and nonlinear data sets show the robustness of our algorithm against outliers. Last, chemical and astronomical data sets that exhibit severe outlier contamination are used to demonstrate the performance of the proposed approach in real situations.      

基于图像的重建点云离群点检测算法

基于图像重建出的三维点云模型通常会包含许多离群点,这些离群点可能孤立存在或密集聚集在一起形成点簇,也可能分布在模型周围甚至附着在模型表面。通过一种检测方法很难有效滤除多种分布状态的离群点,因此,提出了综合的离群点监测算法。首先通过空间距离剔除与模型主体较远的离群点,并通过构建空间拓扑关系加快离群点搜索速度;然后利用边界匹配法,将较小点簇分别与最大点簇进行对比,滤除模型周围离群点簇;最后采用改进的K-means算法,根据RGB颜色值特征对点云数据进行聚簇分类,结合已识别的离群点,检测和滤除附着在模型表面的离群点。仿真实验结果表明,此方法能够有效滤除点云模型中多种分布状态的离群点。     

Incremental rare pattern based approach for identifying outliers in medical data

Outlier detection is an imperative field of data mining that has several applications in the field of medical research. Mining outliers based on the notion of rare patterns can be a promising solution for medical diagnosis as it attempts to identify the unconventional and abnormal risk patterns present in medical data. A crucial issue in medical data analysis is the continuous growth of medical databases due to the addition of new records. Existing outlier detection techniques are capable of handling only static data and thus re-execute from scratch to identify the outliers from incremental medical data. This paper introduces an efficient rare pattern based outlier detection (RPOD) method that identifies outliers by mining rare patterns from incremental data. To avoid multiple database scans and expensive candidate generation steps performed by existent rare pattern mining techniques and facilitate incremental mining, a single pass prefix tree-based rare pattern mining technique is proposed. The proposed rare pattern mining technique is a modification of the well-known FP-Growth frequent pattern mining algorithm. Furthermore, to identify the outliers based on the set of generated rare patterns, an outlier detection technique is also presented. The significance of proposed RPOD approach is demonstrated using several well-known medical datasets. Comparative performance evaluation substantiates the predominance of RPOD approach over existing outlier mining methods.     

基于有向模板和颜色加权SVD的图像匹配算法

为快速稳定地匹配视频序列,并考虑SVD算法的高效性,根据视频序列的特点,对SVD匹配算法进行改进,提出了一种适合视频序列的匹配算法。该算法使用Harris角点检测算子检测兴趣点,使用有向模板提取具有旋转不变性的特征,并通过引入颜色加权法改进SVD算法中的相似性度量函数。同时,又提出一种基于运动一致性约束的误配点剔除方法,首先拟合匹配点间的运动模型,然后自适应地调整参数将错误的匹配点剔除。该算法使用有向模板消除图像间旋转变换的影响,使用颜色特征降低兴趣点匹配时的不确定性,通过运动一致性约束降低误配点数量。实验结果表明,该算法在图像间存在旋转变换关系和不同的光照条件时都可以获得很好的匹配结果,特别是在图像间基线距离较大时仍能得到大量的匹配点并具有很高的正确匹配率,能很好满足实际需要。     

基于场强和凸壳的SIFT特征点匹配算法

传统尺度不变特征变换(SIFT)匹配算法的匹配结果易受参数影响。为此,提出一种于场强和凸壳的SIFT特征点匹配算法。在原始SIFT匹配方法基础上,结合特征点群的凸壳,引入引力场强概念刻画特征点群之间的空间特征关系,以进行图像点模式匹配,在匹配中充分利用特征点的几何空间信息。实验结果表明,该算法具有较高的匹配正确率,能找到更多的特征匹配点。