Moving Object Detection Algorithm in Video Sequences

提出一种基于前景目标分类的视频序列中运动目标检测方法。该方法在RGB空间内建立了一种新颖的背景模型;为了解决背景更新“死锁”问题,提出了像素点的跳跃度函数和稳定性函数,将前景分类为静止目标、运动目标及虚假目标;最后提出了一种基于HSV颜色信息和一阶梯度信息的混合阴影剪除算法。实验结果表明,该方法能有效分割视频场景中的运动目标并鲁棒地分离目标及其阴影区域。An efficient algorithm of detecting moving objects in video sequences was proposed.An adaptive background model was built and subsequently the background model is updated by a foreground object classification based background update algorithm which can resolve the "deadlock" problem efficiently.An improved shadow suppression algorithm which combines HSV color information with first-order image gradient information was exploited to segment shadows.Extensive experiments results on indoor and outdoor image sequences demonstrate that the proposed system can effectively detects moving objects and suppresses their shadows.国家自然科学基金资助项目(60175008);; 国家创新研究群体科学基金项目(60024301

Moving Object Tracking Based on Location and Confidence of Pixels

运动目标跟踪是视频信息处理的重要研究课题之一·首先将时间域上的中值背景建模与空间域上最小交叉熵法相结合,用于检测运动目标所在跟踪区域·在此基础上,提出了跟踪区域内基于像素的可信度与空间位置的权重函数,利用HSV色彩分布模型计算出目标模型与预测模型间的相似性,选出最优相似模型作为当前目标模型,从而实现了多目标的跟踪·实验显示,该算法计算简单,对相似目标能实现准确的跟踪,对非刚性目标的尺度变化、多目标的交叉及部分遮挡具有鲁棒性·Moving object tracking is a critical issue of image sequence processing. In this paper, a moving object tracking algorithm based on location and confidence of pixels is proposed. Firstly, the moving objects are detected by combining the median background model in temporal domain with the minimum cross-entropy in spa tial domain. Then the rectangle area of the objects are obtained, and at the sa me time an HSV color distribution model is used to measure the similarity betwe en t arget rectangles and hypothetical rectangles. In this process, a weighting func tion based on location and confidence of pixels is presented to weigh the pixel values in the rectangle area of the tracking. The experimental results show tha t the algorithm is computationally efficient and robust to scale invariant, part ial occlusion and interactions of non-rigid objects, especially similar objects .国家创新研究群体基金项目(60024301);; 国家自然科学基金项目(60175008

AlGaN/GaN HEMT电流崩塌效应研究进展

简要回顾了AlGaN/GaN HEMT器件电流崩塌效应研究的进展,着重阐述了虚栅模型、应力模型等几种解释电流崩塌效应形成机理的模型和器件钝化、生长盖帽层等减小电流崩塌效应的措施

A Noise Filtering Method of Adaptive Fuzzy Threshold for Wavelet

针对小波变换多分辨分析(MRA)的特点,本文提出一种多尺度分级的自适应模糊权重中值滤波的去噪方法.首先,利用开关控制策略的模糊理论建立隶属函数,用高斯自适应模型对噪声点进行预检测,然后在每一级小波变换过程中应用自适应模糊中值滤波(AFWMF)算法进行噪声滤波.实验表明,常规的小波去噪方法只能去除图像中的高斯噪声,该方法既能去除高斯噪声也能去除非高斯噪声.与中值滤波方法相比,该方法在去噪的同时能保留大量的原图像边缘、细节等重要信息,具有更好的去噪效果.According to the feature of the MRA,this paper presents a noise filtering method of adaptive fuzzy threshold for wavelet classification base on multi resolution.First,subject function is brought forward according to switch controlling of fuzzy strategy.Adaptive gauss model is established Ior pre-detecting.Finally,the adaptive fuzzy weight media filter(AFWMF) is applied in each layer of wavelet transform.This algorithm needn’t know the uncontaminated signal as well as the problem of wavelet coefficient.The experimental results show that usual wavelet algorithm only eliminates gauss noise.However,our algorithm removes both gauss noise and non-gauss noise.Compared with media noise filtering,it preserves original image ingormation of edge and details while removes noise.国家自然科学基金(60175008)资

HSV Color Space and First-order Gradient Based Shadow Suppression Algorithm

提出了一种新的基于HSV颜色信息和一阶梯度信息的阴影剪除算法,该算法运用双重准则检测阴影:准则一利用了HSV颜色空间与人观察彩色的方式十分接近的特性;准则二利用了空间上的一阶梯度信息。引入一种阴影剪除算法的评估方法,并用之对论文算法的效率进行了量化分析。实验结果表明该算法能有效剪除视频场景中运动目标的阴影。This paper proposes an efficient shadow suppression algorithm which combines HSV color information with first-order image gradient information.The algorithm uses double rules to detect shadows:Rule 1 makes use of the character of HSV color space which corresponds closely to the human perception of color;Rule 2 utilizes first-order gradient information.A quantitative method is imported to evaluate shadow suppression algorithm and further analyse the efficiency of the proposed algorithm.Extensive experiments results on indoor and outdoor image sequences demonstrate that the proposed algorithm effectively suppresses their shadows in the scene.国家自然科学基金项目(编号:60175008);; 国家创新研究群体项目(编号:60024301);; 福建省自然科学基金项

认知行为干预对孕产妇负性情绪的影响

目的探讨认知行为干预对孕产妇负性情绪的临床作用。方法选择产前检查及分娩的孕产妇40例作为干预组,选择同时期分娩的产妇37例作为对照组,对照组产妇接受常规的治疗,干预组产妇给予认知行为干预治疗,于孕37周及产后3天分别进行焦虑和抑郁量表的问卷填写,比较两组焦虑和抑郁情况的差异。结果孕37周及产后3天两组患者焦虑状况差异无显著性（P〉0.05）;孕37周两组患者抑郁状况差异无显著性（P〉0.05）,产后3天抑郁状况差异有显著性（P〈0.05）结论认知行为干预对孕产妇的抑郁情绪有一定的正向影响。</p