Large margin classifiers such as support vector machines (SVM) have been applied successfully in various classification tasks.However,their performance may be significantly degraded in the presence of outliers.In this...Large margin classifiers such as support vector machines (SVM) have been applied successfully in various classification tasks.However,their performance may be significantly degraded in the presence of outliers.In this paper,we propose a robust SVM formulation which is shown to be less sensitive to outliers.The key idea is to employ an adaptively weighted hinge loss that explicitly incorporates outlier filtering in the SVM training,thus performing outlier filtering and classification simultaneously.The resulting robust SVM formulation is non-convex.We first relax it into a semi-definite programming which admits a global solution.To improve the efficiency,an iterative approach is developed.We have performed experiments using both synthetic and real-world data.Results show that the performance of the standard SVM degrades rapidly when more outliers are included,while the proposed robust SVM training is more stable in the presence of outliers.展开更多
Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier ...Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.展开更多
Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limita...Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.展开更多
The outlier detection and accommodation of integration navigation of strapdown inertial navigation systems and global position system（SINS/GPS） were studied.Based on analyzing the innovation orthogonal property in K...The outlier detection and accommodation of integration navigation of strapdown inertial navigation systems and global position system（SINS/GPS） were studied.Based on analyzing the innovation orthogonal property in Kalman filter,an outlier adaptive detection approach was first presented,which included the determination of evaluation function and threshold and the logic decision of outlier occurrence.To effectively attenuate the influence on estimation accuracy,a modified Kalman filter algorithm was proposed by accommodation of the dynamic data with outlier.Results of data processing from vehicle-test SINS/GPS integration navigation show the effectiveness of the proposed method.展开更多
基于特征的点云配准技术利用点云局部特征建立对应点,但受噪声和重复结构影响,对应点结果不可避免地包含了大量错误匹配,导致配准精度降低。为充分利用离散点间几何信息优化同平台点云配准精度,本文提出了基于空间一致性的同平台点云配...基于特征的点云配准技术利用点云局部特征建立对应点,但受噪声和重复结构影响,对应点结果不可避免地包含了大量错误匹配,导致配准精度降低。为充分利用离散点间几何信息优化同平台点云配准精度,本文提出了基于空间一致性的同平台点云配准方法,该方法通过增加候选匹配点构建同平台点云图模型,为获取图模型的最优匹配结果,提出了重加权随机游走匹配(reweight random walks matching,RRWM)的优化方法。基于假设验证方法构建了点云配准模型,并结合试验验证了本文方法的有效性。展开更多
文摘Large margin classifiers such as support vector machines (SVM) have been applied successfully in various classification tasks.However,their performance may be significantly degraded in the presence of outliers.In this paper,we propose a robust SVM formulation which is shown to be less sensitive to outliers.The key idea is to employ an adaptively weighted hinge loss that explicitly incorporates outlier filtering in the SVM training,thus performing outlier filtering and classification simultaneously.The resulting robust SVM formulation is non-convex.We first relax it into a semi-definite programming which admits a global solution.To improve the efficiency,an iterative approach is developed.We have performed experiments using both synthetic and real-world data.Results show that the performance of the standard SVM degrades rapidly when more outliers are included,while the proposed robust SVM training is more stable in the presence of outliers.
基金supported by the National Natural Science Foundation of China (62276192)。
文摘Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.
基金supported by the National Natural Science Foundation (Grant Nos.91644216 and 41575128)the CAS Information Technology Program (Grant No.XXH13506-302)Guangdong Provincial Science and Technology Development Special Fund (No.2017B020216007)
文摘Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.
基金Sponsored by the National Natural Science Foundation of China(60774071)the National High Technology Research and Development Program of China("863"Program)(2008AA121302)the National Basic Research Program of China("973"Program)(2009CB724000)
文摘The outlier detection and accommodation of integration navigation of strapdown inertial navigation systems and global position system（SINS/GPS） were studied.Based on analyzing the innovation orthogonal property in Kalman filter,an outlier adaptive detection approach was first presented,which included the determination of evaluation function and threshold and the logic decision of outlier occurrence.To effectively attenuate the influence on estimation accuracy,a modified Kalman filter algorithm was proposed by accommodation of the dynamic data with outlier.Results of data processing from vehicle-test SINS/GPS integration navigation show the effectiveness of the proposed method.
文摘基于特征的点云配准技术利用点云局部特征建立对应点,但受噪声和重复结构影响,对应点结果不可避免地包含了大量错误匹配,导致配准精度降低。为充分利用离散点间几何信息优化同平台点云配准精度,本文提出了基于空间一致性的同平台点云配准方法,该方法通过增加候选匹配点构建同平台点云图模型,为获取图模型的最优匹配结果,提出了重加权随机游走匹配(reweight random walks matching,RRWM)的优化方法。基于假设验证方法构建了点云配准模型,并结合试验验证了本文方法的有效性。
