Wavelet inverse scale space for image restoration

This paper proposes a model for image restoration by combining the wavelet shrinkage and inverse scale space （ISS） method. The ISS is applied to the wavelet representation to modify the retained wavelet coefficients, and the coefficients smaller than the threshold are set to zero. The curvature term of the ISS can remove the edge artifacts and preserve sharp edges. For the multiscale interpretation of the ISS and the multiscale property of the wavelet representation, small details are preserved. This paper illustrates that the wavelet ISS model can be deduced from the wavelet based on a total variation minimization problem. A stopping criterion is obtained from this minimization in the sense of the Bregman distance in the wavelet domain. Numerical examples show the improvement for the image denoising with the proposed method in the sense of the signal to noise ratio and with fewer details remained in the residue.

Multivariate Image Analysis in Gaussian Multi-Scale Space for Defect Detection

Inspired by the coarse-to-fine visual perception process of human vision system,a new approach based on Gaussian multi-scale space for defect detection of industrial products was proposed.By selecting different scale parameters of the Gaussian kernel,the multi-scale representation of the original image data could be obtained and used to constitute the multi- variate image,in which each channel could represent a perceptual observation of the original image from different scales.The Multivariate Image Analysis (MIA) techniques were used to extract defect features information.The MIA combined Principal Component Analysis (PCA) to obtain the principal component scores of the multivariate test image.The Q-statistic image, derived from the residuals after the extraction of the first principal component score and noise,could be used to efficiently reveal the surface defects with an appropriate threshold value decided by training images.Experimental results show that the proposed method performs better than the gray histogram-based method.It has less sensitivity to the inhomogeneous of illumination,and has more robustness and reliability of defect detection with lower pseudo reject rate.

Upsilon Constants and Their Usefulness in Planck Scale Quantum Cosmology

This paper introduces the two Upsilon constants to the reader. Their usefulness is described with respect to acting as coupling constants between the CMB temperature and the Hubble constant. In addition, this paper summarizes the current state of quantum cosmology with respect to the Flat Space Cosmology (FSC) model. Although the FSC quantum cosmology formulae were published in 2018, they are only rearrangements and substitutions of the other assumptions into the original FSC Hubble temperature formula. In a real sense, this temperature formula was the first quantum cosmology formula developed since Hawking’s black hole temperature formula. A recent development in the last month proves that the FSC Hubble temperature formula can be derived from the Stephan-Boltzmann law. Thus, this Hubble temperature formula effectively unites some quantum developments with the general relativity model inherent in FSC. More progress towards unification in the near-future is expected.

Grid Services Trusted Authentication Algorithm Based on Image Trusted Root in Scale Space

A new authentication algorithm for grid identity trusted computing unlimited by hardware is presented;the trusted root is made as an image data.The grid entity is trusted in the soft platform when its feature of image root is entirely matched with that from the other entities＇ feature database in a scale space process.To recognize and detect the stable image root feature,the non-homogeneous linear expandable scale space is proposed.Focusing on relations between the scale parameter of the inhomogeneous Gaussian function terms and the space evolution of thermal diffusion homogeneous equations,three space evolution operators are constructed to exact and mark the feature from image root.Analysis and verification are carried on the new scale space,operators and the core of making decisions for grid entities certifications.

PIECEWISE SPARSE RECOVERY VIA PIECEWISE INVERSE SCALE SPACE ALGORITHM WITH DELETION RULE

In some applications,there are signals with piecewise structure to be recovered.In this paper,we propose a piecewise_ISS(P_ISS)method which aims to preserve the piecewise sparse structure(or the small-scaled entries)of piecewise signals.In order to avoid selecting redundant false small-scaled elements,we also implement the piecewise_ISS algorithm in parallel and distributed manners equipped with a deletion rule.Numerical experiments indicate that compared with alSS,the P_ISS algorithm is more effective and robust for piecewise sparse recovery.

Extracting hand articulations from monocular depth images using curvature scale space descriptors

We propose a framework of hand articulation detection from a monocular depth image using curvature scale space(CSS) descriptors. We extract the hand contour from an input depth image, and obtain the fingertips and finger-valleys of the contour using the local extrema of a modified CSS map of the contour. Then we recover the undetected fingertips according to the local change of depths of points in the interior of the contour. Compared with traditional appearance-based approaches using either angle detectors or convex hull detectors, the modified CSS descriptor extracts the fingertips and finger-valleys more precisely since it is more robust to noisy or corrupted data;moreover, the local extrema of depths recover the fingertips of bending fingers well while traditional appearance-based approaches hardly work without matching models of hands. Experimental results show that our method captures the hand articulations more precisely compared with three state-of-the-art appearance-based approaches.

Multi-Scale Object Perception with Embedding Textural Space

This paper mainly focuses on the issues about generic multi-scale object perception for detection or recognition. A novel computational model in visually-feature space is presented for scene & object representation to purse the underlying textural manifold statistically in nonparametric manner. The associative method approximately makes perceptual hierarchy in human-vision biologically coherency in specific quad-tree-pyramid structure, and the appropriate scale-value of different objects can automatically be selected by evaluating from well-defined scale function without any priori knowledge. The sufficient experiments truly demonstrate the effectiveness of scale determination in textural manifold with object localization rapidly.

A Closure for Isotropic Turbulence Based on Extended Scale Similarity Theory in Physical Space

The closure of a turbulence field is a longstanding fundamental problem, while most closure models are introduced in spectral space. Inspired by Chou＇s quasi-normal closure method in spectral space, we propose an analytical closure model for isotropic turbulence based on the extended scale similarity theory of the velocity structure function in physical space. The assumptions and certain approximations are justified with direct numerical simulation. The asymptotic scaling properties are reproduced by this new closure method, in comparison to the classical Batchelor model.

Scale Invariant Theory of Gravitation in Einstein-Rosen Space-Time

In this paper, we have studied the perfect fluid distribution in the scale invariant theory of gravitation, when the space-time described by Einstein-Rosen metric with a time dependent gauge function. The cosmological equations for this space-time with gauge function are solved and some physical properties of the model are studied.

基于改进无参尺度空间经验小波变换的变压器高频电流局放噪声抑制研究

变压器是电力系统中的关键设备,局部放电(PD)是致使变压器绝缘劣化的主要原因,也是绝缘劣化的重要表现形式。针对高频PD信号中复杂的噪声信息,提出一种基于互信息和峭度优化的改进无参经验小波变换的降噪方法。该方法以经验小波变换(EWT)分解为基础,构造高斯核函数平滑频谱生成尺度空间,利用k-means聚类对边界点进行筛选得到频带分界点,然后提取无参尺度空间EWT分解的模式分量的互信息值和峭度值进行合并优化,排除冗余的频谱划分点并对频谱重新分割,利用新的频谱边界进行划分得到有价值的分量,在有价值的分量中去除噪声分量并将剩余分量进行通用阈值降噪然后重构信号达到PD信号降噪效果。文中方法不需要人工参与且解决了模态混叠问题,仿真和实验室、现场测试表明,与现有小波变换降噪和基于经验模态分解降噪的方法进行对比,文中的方法能够更加有效抑制PD信号中的噪声信息。

基于非线性各向异性滤波的图像特征匹配算法

图像特征匹配是增强现实系统中的关键技术,匹配精度是提升特征匹配性能的关键。提出了一种多尺度特征匹配加强算法(I-AKAZE),通过对非线性各向异性滤波过程中传导函数的改进,减缓图像梯度值大的区域非线性扩散速度,极大程度地保留了匹配图像的边缘特征;同时,结合改进的非线性量化加速稳健特征描述符(NLG-SURF),提高了描述符的识别率。实验结果表明I-AKAZE算法在Mikolajczyk数据集上的可重复性得分相比目前先进的AKAZE算法有着大幅度提升,对应的特征描述符的平均识别率提升8.4%,并且运行速度比经典的SIFT算法快约19%,算法整体在检测和描述阶段上的性能都有提升。

基于改进经验小波变换的海洋平台结构模态参数自动识别方法

针对经验小波变换(empirical wavelet transform,EWT)方法在处理低信噪比信号中频谱分割边界容易产生误判的问题,提出了一种改进经验小波变换(improved empirical wavelet transform,IEWT)的结构模态参数自动识别方法。首先计算信号的互功率谱矩阵,采用奇异值分解(SVD)及尺度空间(SSPP)方法确定频谱的分割边界,将信号分解为若干固有模态函数(IMF)分量,再结合随机减量技术(RDT)和希尔伯特变换(HT)实现模态参数的自动识别。使用IEWT方法对自由振动响应信号及ASCE Benchmark模型信号进行模态参数识别,并分别与EWT方法、基于自回归功率谱的经验小波变换(AR-EWT)方法及小波变换(WT)方法进行对比,结果表明IEWT方法能够自适应确定频谱分割边界,对结构的频率及阻尼比等模态参数具有较高的识别精度;进一步将该方法应用到实验室海洋平台模型的模态参数识别中,证明该方法可用于复杂噪声环境下的低频结构的模态参数识别。

住宅小区理想规模的模型探究——基于共有空间社会密度的视角

住宅小区内共有空间的社会密度会对居民间的邻里交往产生显著影响。为保证住宅小区共有空间资源的数量和品质,在第Ⅲ建筑气候区条件下建立小区模型,对不同类型小区的共有空间进行分析。结果显示,随着小区内建筑平均层数的增加,户均共有空间资源的数量逐渐减少,共有空间的品质在空间尺度、围合度、私密性等维度上显著降低。基于模型研究结果和城市对土地利用的要求,进而提出住宅小区的理想规模为3~4 hm2,住宅平均层数宜为5~8层,而当确有需要建设高层住宅时,住宅平均层数也不宜超过14层。

基于多尺度Harris角点检测的图像配准算法

针对现有多尺度Harris算子算法较复杂、运算量大、精确性一般的问题,提出一种高效简便算法。首先建立多尺度空间,令Harris算子在尺度空间提取特征点,用简化的32维SIFT特征向量描述特征。利用最近邻法匹配特征点;然后采用改进的相似三角形法筛选匹配点,再使用改进的K-means算法对特征点分组,使组内特征点聚集,组间特征点远离;最后应用改进的RANSAC算法在不同组中选取特征点求变换矩阵,避免了选取的特征点距离过近,算法陷入局部最优。实验验证了所提算法的性能。

多尺度空间特征思考下的工业遗产差异化更新——以广州市两处食品加工类工业遗产为例

随着城市更新逐步进入高质量有机发展阶段,工业遗产如何通过差异化的更新手段实现未来可持续发展成为一个重要研究课题。通过引入空间句法的理论技术,结合POI数据、建筑功能及空间布局的分析等,从城市、片区、遗产本身的尺度对两处改造背景相似的工业遗产更新项目的空间特征进行对比研究,并根据分析结果总结该类工业遗产差异化更新的一般路径。研究发现,遵循空间发展规律、制定适应空间特征更新策略的珠江琶醍,其遗产更新成效显著。因此,建议在进行工业遗产更新改造之前,应从多尺度探析所处区域的空间特征,研判区域可达性、区位价值等影响因素,宏观上确认园区发展方向,中观上判断准入业态类型及配置,微观上提升功能与空间的适应性,提高工业遗产更新的效率以及实施结果的可持续性。

一种信标辅助的北斗伪卫星CPD定位方法

针对北斗伪卫星信号引入室内并在小尺度空间下提供高精度定位的难题,如何在顾及网络建设成本的基础上提升小尺度空间的定位时效性和定位精度是未来室内定位技术中需要解决的重要一环。此处充分结合室内环境下小尺度空间的空间特征,提出一种室内节点信标辅助的北斗伪卫星载波相位差定位方法。该方法首先将传统的大范围指纹构建问题转化为指纹节点的方式,在指纹图谱的基础上提出室内节点信标的概念,通过信标节点实现小尺度空间与周围空间的连接,并对以载噪比和载波相位为基础的信标特征谱构建与处理进行了分析;然后以室内节点信标为基础给出了基于载波相位差的位置估计过程,最后结合粒子群寻优算法提出一种顾及行人特征的位置与速度空间约束下的位置搜索算法。在真实环境下的试验结果表明,通过室内节点信标的辅助在室内环境可以实现动态定位精度优于30 cm、暂停态下的定位精度优于25 cm的定位效果。正常步速运动下算法与惯性导航定位精度相当,同时相比于惯性导航方式具有更宽松的运动姿态条件。在小尺度空间下算法具有更优的适用性。

智能垃圾分类拾捡机器人抓取目标定位方法

为准确定位抓取目标所处位置,提出基于深度学习的智能垃圾分类拾捡机器人抓取目标定位方法。定义深度学习算法的目标函数,并在此基础上,完成对抓取目标的检测。建立抓取目标尺度空间,并标定具体的机器人抓取对象,再根据抓取目标定位条件估算值,实现抓取目标定位。实验结果表明,所提方法应用可以有效控制抓取目标节点与定位节点之间的坐标差水平,能够准确定位抓取目标,具有突出的作用价值。

基于微设计理论的城市小微公共空间设计——以南京秦淮“梧桐语”城市客厅为例

随着城镇化的发展,城市建设取得丰富成果的背后,是众多小微公共空间的低效使用与文化缺失。与此同时,在城市更新所面临的土地资源稀缺与提升环境质量的矛盾中,城市腹地中具有良好品质的小微公共空间正在成为城市更新的主要抓手。文章通过对“微设计”理论的研究,结合南京秦淮梧桐语城市客厅设计实践,为我国城市中小微公共空间的设计提供有效的策略借鉴与参考,推动城市小微公共空间的触媒作用。

城市弱势区位的崛起——基于大众点评数据的北京休闲消费空间研究

信息通信技术的发展改变了休闲活动的方式及空间偏好,为城市弱势区位的崛起提供了机遇。文章以北京市为例,利用大众点评2015年和2019年店铺及其评论数据,从宏观(城市)及微观(建筑)尺度分析休闲消费空间分布变化趋势。结果表明:1)店铺及其数字口碑在各尺度都呈现向低可达性区域转移的趋势,揭示了城市弱势区位的崛起;2)数字口碑在宏观尺度向优势区位集聚,而在微观尺度向弱势区位转移,反映了数字口碑的两极化;3)店铺及数字口碑在宏观尺度的变化均弱于微观尺度,表明此期间北京市弱势区位的崛起主要在微观尺度;4)不同类型店铺的比较验证了结果的稳健性。该研究可促进对数字时代城市休闲空间的认识,推动精细尺度的城市更新及管理。

多重空间尺度的神经表征

空间环境具有多重尺度特性,具体分成可操作的近尺度空间、可导航的单视点空间和环境空间,以及大尺度的地理空间,有效表征不同尺度的空间环境对于人和动物的日常生活至关重要。空间尺度的表征与其对应的功能需求有关,近尺度空间表征由顶叶负责;可导航空间表征在海马和皮层都表现出从后往前的“由粗到细”的梯度性;抽象社会空间的尺度表征则表现出二分性。未来研究应关注空间尺度表征的时间动态性及其对认知地图存在形式的影响。