一种结合粒子滤波和张量子空间的目标跟踪算法

由于传统的子空间方法易于丢失图像目标的二维特性,为此本文提出了一种新颖的自适应目标跟踪算法,通过张量的方式建立目标的外观模型——张量子空间,利用在线学习的方法更新其外观模型,同时,利用目标仿射运动的先验信息,通过粒子滤波自适应地跟踪运动目标,并将获得的最优目标观测作为新数据反馈回子空间更新.此外,为了保证子空间更新能获得精确且紧致的目标子空间表达,引入动态部分函数滤除样本野点.实验结果表明,本文提出的自适应目标跟踪方法具有较强的鲁棒性,对于存在姿态变化、短时遮挡和光照变化等情况下均可有效地跟踪目标.     

基于曲面分割的三维点云物体识别

提出了一种基于局部描述符的三维点云物体识别算法.算法首先得到点云的邻域、法线矢量等相关信息,通过邻域进一步得到形状索引值.特征点的提取以形状索引值为依据,以每个特征点为基点对曲面根据欧式距离和矢量夹角分割.分割的曲面片进行等间距划分为多个欧氏距离同心圆,以特征点切平面为基平面投影,并进行等角度抽样,通过抽样点相对特征点的法线矢量及测地距离变化曲线,建立曲面片的二维描述,从而把三维识别转化为二维.根据算法建立模型数据库,给定一个物体,通过和模型数据库中的曲面描述进行比对,得到潜在的识别结果,再通过迭代最近点算法,得到最终的识别结果.最后,通过大量具体实验验证了算法的有效性,并给出了算法的计算复杂度及耗时对比分析,说明了算法的高效性.     

Human detection based on motion object extraction and head–shoulder feature

Aimed at the shortcomings of the traditional video monitoring system, human detection method in intelligent video monitoring system was researched. This paper proposed a human detection method based on motion object extraction and head–shoulder feature to complete human detection and statistics in video image sequences. Firstly, background subtraction based on adaptive threshold was used to extract foreground moving object information, then image erosion and image dilation were used to bypass the object shade and remove false object in order to optimize the results of motion object extraction. And finally, for realizing human moving object detection, we proposed the object discrimination algorithm based on human head–shoulder feature to complete human detection and statistics. Experimental results show that the method can successfully realize human detection and statistics. The method is highly accurate and has good real-time and extensive applications. The identification rate is 86% through human video sequences to test. This method can detect human automatically and provide the theoretical and technological base for object detection in the intelligent surveillance system.     

Robust object detection based on deformable part model and improved scale invariant feature transform

We propose an approach to improve the detection results of a generic offline trained detector on frames from a specific video. For two consecutive frames of a video with the object, deformable part model (DPM) detection is performed to get the original detections. Then the image patches corresponding to the detected root box and part boxes were respectively obtained. Thirdly, improved scale invariant feature transform features (SIFT) from those image patches were extracted and matched with the SIFT features by KD-Tree. K-means clustering the angle and scale of matched keypoints to filter out the uncorrected matches and further remove false matches by RANSAC algorithm. Finally, the SIFT_DPM detection result from the matches between image patches of continuous frames was obtained. We focus on methods with high precision detection results since it is necessitated in real application. Extensive experiments with state-of-the-art detector demonstrate the efficacy of our approach.     

Neural correlates of a clinical continuous performance test

Functional magnetic resonance imaging (fMRI) was performed in 30 healthy adults to identify the location, magnitude, and extent of activation in brain regions that are engaged during the performance of Conners' Continuous Performance Test (CPT). Performance on the task during fMRI was highly correlated with performance on the standard Conners' CPT in the behavioral testing laboratory. An extensive neural network was activated during the task that included the frontal, cingulate, parietal, temporal, and occipital cortices; the cerebellum and the basal ganglia. There was also a network of brain regions which were more active during fixation than task. The magnitude of activation in several regions was correlated with reaction time. Among regions that were more active during task, the overall volume of supratentorial activation and cerebellar activation was greater in the left hemisphere. Frontal activation was greater in dorsal than in ventral regions, and dorsal frontal activation was bilateral. Ventral frontal region and parietal lobe activation were greater in the right hemisphere. The volume of clusters of activation in the extrastriate ventral visual pathway was greater in the left hemisphere. This network is consistent with existing models of motor control, visual object processing and attentional control and may serve as a basis for hypothesis-driven fMRI studies in clinical populations with deficits in Conners' CPT performance.     

关联成像目标重构的伪逆迭代降噪方法

结合伪逆关联成像和迭代去噪关联成像,提出了关联成像目标重构的伪逆迭代方法.该方法以伪逆关联成像重构结果为初始值,选取合适的与噪声干扰相关的阈值,通过迭代运算逼近实际的噪声干扰,最终抑制噪声并提高重构图像的峰值信噪比.以峰值信噪比和相关系数为衡量标准,将伪逆迭代关联成像的重构结果与差分关联成像、伪逆关联成像进行对比分析.仿真实验结果表明,伪逆迭代方法的峰值信噪比较伪逆关联成像方法、差分关联成像方法分别高出约1.0dB、3.1dB,同时其相关系数、视觉效果也有所改善,验证了该方法的有效性.     

具有稳定系数的多目标多维模糊决策算法

对多目标多维模糊决策模型的模糊交叉算法做了进一步研究,分析了多目标多维模糊决策模型中主观监督因子在三维以上模糊决策时对目标权重调节不灵敏的原因,提出一种新的模糊环境下带有目标权重主观监督因子和稳定系数的目标函数,给出了具有稳定系数和主观监督因子的目标权重计算公式,并给出了多目标多维模糊决策的算法,实例计算说明了算法的有效性。     

数据库管理系统在地震滑坡研究中的应用——地震滑坡数据库管理系统

地震滑坡资料是深入研究地震滑坡问题的宝贵基础资料,但是目前对这些资料缺乏有效的管理和利用。本文介绍的地震滑坡数据库管理系统可以科学合理地对这些资料进行高效管理,节省科研前期的投入,提高工作效率。本系统界面友好、操作方便,具有良好的可维护性、可扩充性和升级换代能力,可以加速地震滑坡问题的研究步伐,是地震滑坡问题研究的一个有力工具。     

INFORMA基于TMN 的OXC’s 信息建模

本文介绍了对多波长光传送网实施基于TMN管理中涉及的信息建模的方法问题,在建议了一组用于描述光传送网通用功能的基本传送对象类的基础上,讨论了几种典型OXC′s信息建模的方法,包括可能用到的对象类,这些方法和对象类为建立实际的、完整的OXC′s信息模型提供了的基础.     

基于光谱空间变换的遥感图像目标探测方法研究

针对遥感图像在频率域中的表征,提出了一种基于光谱空间变换的遥感图像目标探测方法。该方法首先利用傅里叶变换,将遥感图像从空域转变到频率域;然后利用频谱能量楔状采样和谐波叠置等手段,将不同频谱能量所表征的目标特征信息分解到不同的高、低频段中,由此获取对应目标特征在频率域中的探测标志;最后结合在频谱能量上具有方向和频带选择性的匹配Gabor滤波器,实现了居民楼地物目标的有效探测。试验结果表明,文章所提出的方法能够较好地探测遥感图像的目标信息,并且具有特定方向上目标检测的能力。