权重自适应模糊层次分析方法及应用

借鉴了误差反向修正理论和增量式的思想,结合层次分析法固有的优势,提出了具有自适应能力的权重设定层次分析法(adaptive weight setting method,AWS)新方法,使盾构法隧道施工风险识别模型能够利用较少的施工数据,完成进行权重调整,满足了实际隧道施工中风险评估的需求.该方法在上海地铁某区间隧道工程中进行了试用,取得了良好的效果,表明了该方法的有效性和实用性.     

基于权值的骨架修剪算法

针对传统骨架提取算法中出现影响骨架识别的毛刺问题,特别是其中对物体形状的描述会产生很大影响的绷带骨架,在骨架的权值的基础上提出了一种新的骨架修剪算法.算法的核心是通过距离变换获取骨架权值并在骨架修剪过程中把表征骨架重要性的权值作为骨架分支是否被去除的依据.实验结果表明,作为目标骨架表示和识别的预处理手段,基于权值的骨架修剪算法能使骨架更能准确的反应目标物体的稳定结构.     

基于证据距离和矛盾因子的加权证据合成法

针对Dempster-shafer (DS)理论直接对高冲突证据合成时会有融合结果不合理的问题,本文提出了一种加权证据合成法.在多个证据融合时,该方法综合利用证据距离和矛盾因子表示证据冲突.并根据各证据与其它证据的冲突程度确定其权重系数,基于此又分别采用两种适用于不同情况的方法对证据基本置信指派函数修正,然后用DS规则合成.算例实验表明了本文方法对高冲突证据融合的有效性,并且其融合结果还具有良好的收敛性.     

双模态语音识别中乘积HMM权重系数与瞬时SNR的关系研究

在有噪声污染等复杂情况下,为了能够得到更高的语音识别率,提出了一种新的乘积隐马尔可夫模型(HMM)用于双模态语音识别,研究并确定了模型中权重系数与瞬时信噪比(SNR)之间的关系.该模型在独立训练音频和视频HMM的基础上,建立二雏训练模型,并使用重估策略保证更高的准确性.同时引入广义几率递减(GPD)算法,调整音视频特征的权重系数.实验结果表明,提出的方法在噪声环境下体现出了良好稳定的识别性能.     

一种基于受限网络的移动对象索引

为了有效地支持城市交通网络中移动对象的过去、现在和将来的轨迹查询,在基于模拟预测的位置表示模型基础上,提出了一种两层R树加上一个表结构的复合索引结构AUC(Adaptive Unit Compounding).根据城市交通网的特征,采用了一种带有环形交叉口的元胞自动机模型模拟移动对象的将来轨迹,并用线性回归和圆弧曲线拟合分别得到对象在规则路段和交叉口的轨迹预测方程;根据移动对象的运动特性,采用了一种新的自适应单元(AU)作为索引结构的基本单位.实验表明,AUC索引的查询和更新性能都要优于TPR树和TB树.     

移动进程的蛰伏性及空间逻辑

界程逻辑(Ambient Logic)定义了一个示范性的空间逻辑来描述移动界程演算中移动进程的空间性质.然而在某些移动计算系统中,界程逻辑对移动进程空间性质的描述粒度是不够的.分析移动进程的蛰伏性质,用蛰伏和活跃来描述移动进程的存在状态,由此给出一种界程逻辑的扩展,称为状态空间逻辑.该逻辑能够描述移动进程的蛰伏性,进而更细粒度地刻画进程空间性质,且其在移动界程演算上的满足性是可判定的.同时还给出了状态空间逻辑公式的形式解释和蛰伏空间公式的逻辑推导规则.     

基于颜色特征的图像检索技术研究

本文针对利用图像的颜色特征丢失空间信息的问题,提出了一种通过提取灰度特征,获取图像的形状特征,综合检索图像的方法。     

基于ROI多特征和相关反馈的图像检索算法

本文提出了一种基于感兴趣区域(ROI)综合多特征和相关反馈的图像检索算法.在对图像进行四叉树分解的基础上,由用户选择感兴趣区域,综合颜色、纹理和形状三种底层特征进行多层感兴趣区域检索;由相关反馈技术体现用户感知的主观性,通过调整感兴趣区域、特征问、特征内的权重来提高查准率.给出了改进的CBIR通用模型和特征权重模型.实现了一个图像检索原型系统,将不同实验结果进行了比较和分析,实验结果表明,该文提出的方法具有良好的检索效果.     

Semantic Plane-Structure based motion detection with a nonstationary camera

This paper presents a novel method to accurately detect moving objects from a video sequence captured using a nonstationary camera. Although common methods provide effective motion detection for static backgrounds or through only planar-perspective transformation, many detection errors occur when the background contains complex dynamic interferences or the camera undergoes unknown motions. To solve this problem, this study proposed a motion detection method that incorporates temporal motion and spatial structure. In the proposed method, first, spatial semantic planes are segmented, and image registration based on stable background planes is applied to overcome the interferences of the foreground and dynamic background. Thus, the estimated dense temporal motion ensures that small moving objects are not missed. Second, motion pixels are mapped on semantic planes, and then, the spatial distribution constraints of motion pixels, regional shapes and plane semantics, which are integrated into a planar structure, are used to minimise false positives. Finally, based on the dense temporal motion and spatial structure, moving objects are accurately detected. The experimental results on CDnet dataset, Pbi dataset, Aeroscapes dataset, and other challenging self-captured videos under difficult conditions, such as fast camera movement, large zoom variation, video jitters, and dynamic background, revealed that the proposed method can remove background movements, dynamic interferences, and marginal noises and can effectively obtain complete moving objects.© 2017 ElsevierInc.Allrightsreserved.     

Progressive spatial-angular feature enhancement network for light field image super-resolution

The intensity and direction of the light field (LF) can be recorded simultaneously by using LF cameras. However, since LF cameras sacrifice spatial resolution for higher angular resolution, the images acquired by LF cameras tend to have low spatial resolution. Therefore, LF image super-resolution (SR) has become an integral part of LF studies. Many existing LF image SR methods fail to fully utilize angular and spatial information due to only using partial sub-aperture images (SAIs). In this paper, we propose a progressive spatial-angular feature enhancement network (PSAFENet) to deal with the problem of missing information in LF image SR. Specifically, we first extract the spatial features of SAIs, the spatial and angular features contained in the macro-pixel images (MacPIs) by three different feature extraction modules. Then, these features are fed into a spatial-angular feature enhancement (SAFE) module to perform enhancement of spatial-angular information on the SAIs. To improve the reconstruction accuracy, we also use the information multi-distillation block (IMDB) to remove the redundant information before upsampling. Our network can well merge the angular and spatial information into each SAI, which facilitates the reconstruction of the LF images. Experimental results on five public datasets show that the proposed PSAFENet method outperforms existing methods in both qualitative and quantitative comparisons.