Curvature-aware adaptive re-sampling for point-sampled geometry

With the emergence of large-scale point-sampled geometry acquired by high-resolution 3D scanning devices, it has become increasingly important to develop efficient algorithms for processing such models which have abundant geometric details and complex topology in general. As a preprocessing step, surface simplification is important and necessary for the subsequent operations and geometric processing. Owing to adaptive mean-shift clustering scheme, a curvature-aware adaptive re-sampling method is proposed for point-sampled geometry simplification. The generated sampling points are non-uniformly distributed and can account for the local geometric feature in a curvature aware manner, i.e. in the simplified model the sampling points are dense in the high curvature regions, and sparse in the low curvature regions. The proposed method has been implemented and demonstrated by several examples.     

在线特征选择的目标跟踪*

为提高目标与背景对比度低、相似物体干扰等复杂环境下目标跟踪的效果,提出将在线学习选择最优颜色特征嵌入跟踪算法中,以改善跟踪的稳定性。以当前时刻目标的区域为目标区域,利用卡尔曼滤波预测目标的下一时刻位置,在卡尔曼滤波预测的位置为中心取某一区域作为背景区域进行在线特征选择作为下一时刻的跟踪特征,以卡尔曼滤波预测的位置为初始位置利用Mean-shift搜索目标位置,此位置作为量测进行卡尔曼滤波校正。通过实验表明,该方法在目标与背景的对比度低、相似物体干扰等复杂环境下极大地改善了跟踪的稳定性。     

基于改进 Mean－shift算法的铲斗目标跟踪

针对挖掘机器人铲斗目标实时跟踪问题,提出了Kalman＋Mean－shift算法的铲斗目标跟踪算法。与Mean－shift算法以及Camshift 算法相比,该算法通过Kalman滤波器对目标位置的预测,解决了跟踪过程中出现干扰时无法跟踪目标的问题。运用OpenCV进行跟踪程序设计,对挖掘机模型的铲斗目标进行跟踪实验,对算法的可行性进行验证。实验结果显示,结合Kalman滤波器的Mean－shift算法能够准确地实施对铲斗目标的跟踪。     

基于运动轨迹分量的行人徘徊行为检测研究

针对传统的目标徘徊检测方法在实时性和准确性等方面的不足,本文提出了一种基于目标轨迹分量曲线的行人徘徊检测算法。首先采用帧差法的背景模板建模方法来建立初始背景。然后用改进的结合背景差分的三帧差分法检测前景目标,通过Mean-shift算法对前景目标进行跟踪。最后将得到的运动轨迹做正交分解,根据根据轨迹的X,Y轴分量曲线来对徘徊行为进行识别。实验表明,该方法能够对几种典型的徘徊行为进行实时、精确判断,同时可以检测出其他复杂的徘徊行为,有较好的实时性和准确率。     

Diagnostics analysis for log-Birnbaum-Saunders regression models

In this paper, several diagnostics measures are proposed based on case-deletion model for log-Birnbaum-Saunders regression models (LBSRM), which might be a necessary supplement of the recent work presented by Galea et al. [2004. Influence diagnostics in log-Birnbaum-Saunders regression models. J. Appl. Statist. 31, 1049-1064] who studied the influence diagnostics for LBSRM mainly based on the local influence analysis. It is shown that the case-deletion model is equivalent to the mean-shift outlier model in LBSRM and an outlier test is presented based on mean-shift outlier model. Furthermore, we investigate a test of homogeneity for shape parameter in LBSRM, which is a problem mentioned by both Rieck and Nedelman [1991. A log-linear model for the Birnbaum-Saunders distribution. Technometrics 33, 51-60] and Galea et al. [2004. Influence diagnostics in log-Birnbaum-Saunders regression models. J. Appl. Statist. 31, 1049-1064]. We obtain the likelihood ratio and score statistics for such test. Finally, a numerical example is given to illustrate our methodology and the properties of likelihood ratio and score statistics are investigated through Monte Carlo simulations.     

Study on Bhattacharyya Coefficients within Mean-Shift Framework and its Application

The classic mean-shift tracker has no integrated scale adaptation, which limits its performance in tracking variable scale object as wel l as the object with severe motions. Based on the variation analysis of Bhattacharyya coefficient within mean-shift framework, the sufficient conditions for accurate tracking of object with scale changes are presented. We propose that the changes of object scale and position within the region of previous tracking window will not impact the localization accuracy of mean-shift tracker. Based on our findings, a novel backward tracking method is introduced to solve scaling problem, and the solution of dealing with the severe object motions is also discussed by integrating mean-shift tracker into the low-resolution matching scheme.     

Mean-Shift跟踪算法中目标模型的自适应更新

针对Mean—shift跟踪算法中的模型更新问题,提出利用目标历史模型和当前匹配位置处得到的观测模型,对目标核函数直方图进行Kalman滤波,从而对目标模型进行及时更新。在滤波过程中,通过分析滤波残差动态,调整滤波方程中的各种参数。Bhattacharyya系数被用作模型更新的准则。该系统能够有效地处理遮挡、光照变化等干扰,避免了模型的过更新。大量视频序列测试的结果表明,在场景遮挡、光照变化等因素的影响下,算法能够对目标外观以及尺度的变化进行稳健、实时和有效的跟踪。     

传统服饰云肩实物图像主色的智能检测

为解决获取传统服饰实物图像色彩耗时且缺乏准确性问题,以传统服饰的云肩为例,借助均值漂移（Mean-shift）聚类法,提出了一种检测传统服饰实物图像颜色的方法。运用单反数码相机进行实物图像采集;对所得初始图像的R、G、B3个颜色通道进行去噪处理;再将图像RGB颜色空间的特征向量转换至CIE L*a*b*颜色空间中,利用大津法阈值原理（自适应阈值算法）分割被测图像中云肩实物与背景;最后采用Mean-shift聚类算法,将被测图像的颜色像素分割为若干有效的集群,同时从这些集群中提取云肩主要色彩。实验结果表明,该算法可较为准确地从云肩图像中提取主色,且当Mean-shift聚类算法的带宽被设定为0.05时,分类颜色结果更为准确。     

一种巨幅遥感影像中机场ROI检测算法

目标检测一直是遥感领域的一个难题,巨幅影像处理进一步加大了目标检测的难度。为此,提出了一种基于特征空间分析的机场ROI快速检测方法,将影像分块读取,并行处理提取特征,采用Mean-shift方法对所提取特征分析聚类,最终定位出机场ROI。实验结果表明,该ROI检测方法具有很好的稳健性、自动性和并行性,并能够有效地降低巨幅影像处理的难度。