A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

ON THE SHARP GROWTH,COVERING THEOREMS FOR NORMALIZED BIHOLOMORPHIC MAPPINGS IN C^n

In this article, a normalized biholomorphic mapping f defined on bounded starlike circular domain in Cn is considered, where z = 0 is a zero of order k ＋ 1 of f（z） - z. The sharp growth, covering theorems for almost starlike mappings of order α and starlike mappings of order α are established. Meanwhile, the construction of the above mappings on bounded starlike circular domain in Cn is also discussed, it provides the extremal mappings for the growth, covering theorems of the above mappings.

On the Normal Criterion of Quasimeromorphic Mappings

In this paper, we generalize an, inequality of meromorphic mappings to quasimeromorphic ones. Applying the results here, we can establish a normal criterion of quasimeromorphic mappings.

Normalized edge detection, and the horizontal extent and depth of geophysical anomalies

Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, and find the horizontal boundaries of geological bodies. Large deviations between model and true edges are common because of the interference of depth and errors in computing the derivatives; thus, edge detection methods cannot provide information about the depth of the source. To simultaneously obtain the horizontal extent and depth of geophysical anomalies, we use normalized edge detection filters, which normalize the edge detection function at different depths, and the maxima that correspond to the location of the source. The errors between model and actual edges are minimized as the depth of the source decreases and the normalized edge detection method recognizes the extent of the source based on the maxima, allowing for reliable model results. We demonstrate the applicability of the normalized edge detection filters in defining the horizontal extent and depth using synthetic and actual aeromagnetic data.

基于Normal Map虚拟技术的赣南客家围屋仿真实现探析

Normal Map技术是当前虚拟仿真实现的一个重要手段,它在模型基础上通过贴图信息来完成虚拟对象的高仿真实现。当前古建筑虚拟仿真的数字化实现更是离不开Normal Map技术的支撑。赣南客家围屋是江西古建筑群里极具代表性建筑,其历史悠久,文化深厚,建筑形制及材料十分丰富,Normal Map虚拟技术的研究将为赣南客家建筑的保护提供技术支持。本文从Normal Map的运算方式及技术特点,结合赣南客家围屋的具体实际进行建筑微观细节的仿真实现探析。

利用面片阴影法简化Bump Mapping

论文介绍了Bump Mapping的生成方法,并针对可分割bump形式的纹理提出了利用OpenGL简化计算和加速显示的方法———面片阴影算法,即利用面片的阴影降低逐点计算的复杂性。

多回波快速自旋回波T_2 mapping定量技术评价心肌T_2值的价值

目的探讨多回波快速自旋回波序列(MEFSE)的T2mapping定量技术评价左室心肌T2值的价值。方法 30例健康志愿者,均行3.0T心脏磁共振(CMR)Cine电影、T2WI序列、MEFSE T2mapping序列扫描,依据描述性研究方法,探索MEFSE T2mapping序列临床应用的可行性和可重复性,测定感兴趣区T2值,应用方差分析方法统计分析心肌17节段和不同供血区下有无差异。应用Bland^Altman分析和Pearson相关分析方法研究此技术在不同观察者间的一致性。结果健康志愿者17节段心肌T2值不全相等(P<0.01)。不同冠状动脉供血区T2值无统计学差异(F=1.656,P=0.192)。左室心肌平均T2值如下:基底部,(71.6±7.1)ms;中间部,(77.3±9.2)ms;心尖部,(83.0±8.5)ms,差异有统计学意义(F=14.245,P<0.01)。根据Bland-Altman一致性分析和Pearson相关分析得到参数测量在观察者间具有良好的一致性,95%一致性界限(-6.1,24.8)ms,相关系数r=0.809。结论 CMR MEFSE T2mapping定量技术在心脏成像中具有较好的可行性和可重复性。中国健康志愿者中心肌T2值存在节段性差异。

LINEAR DILATATION OF QUASIREGULAR MAPPINGS IN SPACE

This paper presents an explicit upper bound for the linear dilatation of K- quasiregular (K-qr) mappings, which improves S. Rickman's [6, P.37] corresponding re- sult for K-qr mappings and generalizes P. Seittenranta's [7, Theorem 1.5] result for K- quasiconformal (K-qc) maps.

Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data

We propose a multi-sensor multi-spectral and bi-temporal dual-polarimetric Synthetic Aperture Radar(SAR) data integration scheme for dry/wet snow mapping using Sentinel-2 and Sentinel-1 data which are freely available to the research community. The integration is carried out by incorporating the information retrieved from ratio images of the conventional method for wet snow mapping and the multispectral data in two different frameworks. Firstly, a simple differencing scheme is employed for dry/wet snow mapping, where the snow cover area is derived using the Normalized Differenced Snow Index(NDSI). In the second framework, the ratio images are stacked with the multispectral bands and this stack is used for supervised and unsupervised classification using support vector machines for dry/wet snow mapping. We also investigate the potential of a state of the art backscatter model for the identification of dry/wet snow using Sentinel-1 data. The results are validated using a reference map derived from RADARSAT-2 full polarimetric SAR data. A good agreement was observed between the results and the reference data with an overall accuracy greater than 0.78 for the different blending techniques examined. For all the proposed frameworks, the wet snow was better identified. The coefficient of determination between the snow wetness derived from the backscatter model and the reference based on RADARSAT-2 data was observed to be 0.58 with a significantly higher root mean square error of 1.03 % by volume.

NORMAL CRITERION FOR FAMILIES OF HOLOMORPHIC MAPS OF SEVERAL COMPLEX VARIABLES INTO P^N(C)WITH MOVING HYPERSURFACES

This article gives a normal criterion for families of holomorphic mappings of several complex variables into P N(C)for moving hypersurfaces in pointwise general position,related to an Eremenko’s theorem.

ON PROBLEM OF NEAREST COMMON FIXED POINT OF NONEXPANSIVE MAPPINGS

The purpose is by using the viscosity approximation method to study the convergence problem of the iterative scheme for an infinite family of nonexpansive mappings and a given contractive mapping in a reflexive Banach space. Under suitable conditions, it was proved that the iterative sequence converges strongly to a common fixed point which was also the unique solution of some variational inequality in a reflexive Banach space. The results presented extend and improve some recent results.

QUASIMEROMORPHIC MAPPINGS OF SEVERAL COMPLEX VARIABLES

The definitions of quasimeromorphic mappings from Cn to P1n, where P1 C U {∞}, P1n= P1×P1× ×P1(n-times) are introduced. From an inequality of the value distribution of quasimeromorphic functions of single variable, it follows that a normal criterion for the family of quasimeromorphic functions of several complex variables. Futhermore, a normal criterion for the family of quasimeromorphic mappings from Cn to P1n has been obtained.

Lithological mapping with multispectral data–setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan

In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan spectral library(pklib) version 0.1, contains the analysis data of sixty rock samples taken in the Balakot region in Northern Pakistan.The spectral library is implemented as SQLite database. Structure and naming are inspired by the convention system of the ASTER Spectral Library. Usability, application and benefit of the pklib were evaluated and depicted taking two approaches, the multivariate and the spectral based. The spectral information were used to create indices. The indices were applied to Landsat and ASTER data tosupportthespatial delineation of outcropping rock sequences instratigraphic formations. The application of the indices introduced in this paper helps to identify spots where specific lithological characteristics occur. Especially in areas with sparse or missing detailed geological mapping, the spectral discrimination via remote sensing data can speed up the survey. The library can be used not only to support the improvement of factor maps for landslide susceptibility analysis, but also to provide a geoscientific basisto further analyze the lithological spotin numerous regions in the Hindu Kush.

A Note of Spirallike Mappings in C^n

In this paper,the Growth and covering Theorems for a kind of spirallike mapping are given.

基于NDT配准与轮式里程计的激光雷达运动畸变补偿算法

激光雷达是广泛应用于同时定位与地图构建(SLAM)的测距传感器,普遍基于旋转机制收集周围环境的几何信息。当扫描期间激光雷达发生移动时,生成的点云会产生运动畸变,降低SLAM系统的准确性。在激光雷达SLAM算法设计中,为使雷达运动的估计结果更为精确,文中提出一种基于正态分布变换(NDT)和轮式里程计的激光雷达运动畸变补偿算法。首先,使用轮式里程计以高频测量方式对雷达运动进行估计,可补偿部分运动畸变。其次,设计一种基于NDT配准算法的误差处理方法,通过对点云的精准匹配降低里程计漂移的影响,实现雷达运动精确估计,进而精准补偿运动畸变。文中采用数据集以及真实场景实验对提出算法进行测试。实验结果表明,与传统里程计辅助方法相比,提出的算法能够优化运动畸变补偿效果,降低轨迹累积误差并生成全局一致地图。

PBR方法在非遗贝雕工艺数字模型中的应用

该文通过创建高品质的数字模型以复制贝雕工艺品,并利用数字化策略来保护北海贝雕文化遗产,对非物质文化遗产的保护与传承具有重要的意义。通过分析材料微表面的结构,采用了基于数学模型的生成函数和排列方法来生成微表面基元,以用于合成材料法线贴图,这一方法有效地降低了贝雕工艺品数字化的人力成本。将数字技术与传统的北海贝雕制作工艺相结合,有助于扩大北海贝雕技艺的文化传播,提高北海贝雕工艺品的商业价值。

三维人脸深度图的流形学习-LOGMAP识别方法

人脸识别是生物特征识别技术最友好的身份识别方式,而三维人脸识别由于可解决二维人脸识别中存在的光照、姿态等局限,成为人脸识别的研究热点,但其特征维数过高是该领域的瓶颈,而维数约减是解决这一问题的关键。流形学习是一类非线性维数约减算法,LOGMAP是一种基于黎曼法坐标的流形学习算法,该算法可以在保持度量信息不变的情况下,把高维空间的数据映射到低维空间。构建了基于流形学习的三维人脸识别框架,并结合LOGMAP进行三维人脸深度图的识别。实验结果表明,该方法在三维深度图上可以得到良好的识别效果。

基于绿视率和NDVI的城市街道景观分析与优化研究

街道景观空间对市民健康和城市风貌具有重要影响。既往研究中常以归一化植被指数(NDVI)和绿视率(GVI)来分别代表二维和三维的绿色指标,但对二者的指标相关性研究甚少。采用基于深度学习的图像语义分割方法分析百度街景计算代表性街道的GVI,利用GF-1卫星数据计算NDVI,比较分析城市街道的GVI和NDVI指标特征及相关性。结果表明,1)中山市中心城区各代表街道GVI指标参差不齐,从8.06%到36.00%,其中石岐街道兴中道GVI最高;2)各街道观测点的NDVI均值随着缓冲区尺度的增加也随之呈现出不同变化,NDVI均值具有强烈的尺度敏感性;3)50 m GVI和DNVI均值的皮尔逊相关系数最高,达到0.832。在此基础上分析街道景观存在的不足并给出优化建议,为城市街景评估、空间优化、景观提升提供参考。

局域芯片分布特征辨识及自适应变步长扫描与匹配

为解决LED芯片检测与分选中的漏扫、匹配错误等关键问题,提出自适应变步长扫描方法。该方法对当前局域分布特性进行分析,利用晶圆片局部形变的连续性与保持性对邻近区域芯片分布进行预测,实施步长调整。在检测端,提出基于8邻域位置模型的区域芯片分布特征表征与描述方法,确立自适应步长调整策略,建立物理地址映射视图以及归一化逻辑视图。在分选端,利用已知物理地址映射图和归一化逻辑视图,对扫描区域、步长与路径进行规划,在遍历芯片的同时,实现芯片匹配。实测表明,相比冗余方法,该方法能够以低于4%的冗余率,实现漏扫率低于0.5%,提升扫描效率近23%。由于只针对有意义的芯片进行查找,可实现接近100%的匹配率,显著改善了算法效率和抗干扰能力。

MAP译码器的免归一化处理信息更新算法

在不改变译码性能的条件下,为了加快最大后验概率(Maximum A Posteriori Probability,MAP)译码器状态信息更新的速度和降低算法的复杂度,提出了一种用于Turbo码的MAP译码器的免归一化处理算法。算法采用二进制补码加法器和减法器将MAP译码过程中的状态信息投影到一个归一化圆上,当状态信息更新时所有的状态信息在归一化圆上移动,通过保持归一化圆上状态信息的正确关系来计算似然比。归一化过程中不用搜索或估计状态信息的最大值,通过简化状态信息归一化过程加速了MAP译码器的状态信息更新并降低了复杂度。所提算法在与传统算法译码性能相同的情况下,可以降低36.2%的计算复杂度和17.4%的关键路径延迟,达到MAP译码器实现中的高速、低复杂度目标。