Collaborative Distribution Alignment for 2D image-based 3D shape retrieval

Retrieving 3D shapes with 2D images has become a popular research area nowadays, and a great deal of work has been devoted to reducing the discrepancy between 3D shapes and 2D images to improve retrieval performance. However, most approaches ignore the semantic information and decision boundaries of the two domains, and cannot achieve both domain alignment and category alignment in one module. In this paper, a novel Collaborative Distribution Alignment (CDA) model is developed to address the above existing challenges. Specifically, we first adopt a dual-stream CNN, following a similarity guided constraint module, to generate discriminative embeddings for input 2D images and 3D shapes (described as multiple views). Subsequently, we explicitly introduce a joint domain-class alignment module to dynamically learn a class-discriminative and domain-agnostic feature space, which can narrow the distance between 2D image and 3D shape instances of the same underlying category, while pushing apart the instances from different categories. Furthermore, we apply a decision boundary refinement module to avoid generating class-ambiguity embeddings by dynamically adjusting inconsistencies between two discriminators. Extensive experiments and evaluations on two challenging benchmarks, MI3DOR and MI3DOR-2, demonstrate the superiority of the proposed CDA method for 2D image-based 3D shape retrieval task.     

Fast retrieval of hidden data using enhanced hidden Markov model in video steganography

In the digital world, secure data communication has an important role in mass media and Internet technology. With the increase in modern malicious technologies, confidential data are exposed at a greater risk during data communication. For secured communication, recent technologies and the Internet have introduced steganography, a new way to hide data. Steganography is the growing practice of concealing data in multimedia files for secure data transfer. Nowadays, videos are more commonly chosen as cover media than other multimedia files because of the moving sequence of images and audio files. Despite its popularity, video steganography faces a significant challenge, which is a lack of a fast retrieval system of the hidden data. This study proposes a novel video steganography technique in which an enhanced hidden Markov model (EHMM) is employed to improve the speed of retrieving hidden data. EHMM mathematical formulations are used to enhance the speed of embedding and extracting secret data. The data embedding and retrieving operations were performed using the conditional states and the state transition dynamics between the video frames. The proposed EHMM is extensively evaluated using three benchmark functions, and experimental evaluations are conducted to test the speed of data retrieval using differently sized cover-videos. Results indicate that the proposed EHMM yields better results by reducing the data hiding time by 3–50%, improving the data retrieval rate by 22–77% with a minimum computational cost of 20–91%, and improving the security by 4–77% compared with state-of-the-art methods.     

形状的全尺度可视化表示与识别

目的 视觉目标的形状特征表示和识别是图像领域中的重要问题。在实际应用中,视角、形变、遮挡和噪声等干扰因素造成识别精度较低,且大数据场景需要算法具有较高的学习效率。针对这些问题,本文提出一种全尺度可视化形状表示方法。方法 在尺度空间的所有尺度上对形状轮廓提取形状的不变量特征,获得形状的全尺度特征。将获得的全部特征紧凑地表示为单幅彩色图像,得到形状特征的可视化表示。将表示形状特征的彩色图像输入双路卷积网络模型,完成形状分类和检索任务。结果 通过对原始形状加入旋转、遮挡和噪声等不同干扰的定性实验,验证了本文方法具有旋转和缩放不变性,以及对铰接变换、遮挡和噪声等干扰的鲁棒性。在通用数据集上进行形状分类和形状检索的定量实验,所得准确率在不同数据集上均超过对比算法。在MPEG-7数据集上精度达到99.57%,对比算法的最好结果为98.84%。在铰接和射影变换数据集上皆达到100%的识别精度,而对比算法的最好结果分别为89.75%和95%。结论 本文提出的全尺度可视化形状表示方法,通过一幅彩色图像紧凑地表达了全部形状信息。通过卷积模型既学习了轮廓点间的形状特征关系,又学习了不同尺度间的形状特征关系。本文方法在视角变化、局部遮挡、铰接变形和噪声等干扰下能保持较高的识别正确率,可应用于图像采集干扰较多以及红外或深度图像的目标识别,并适用于大数据场景下的识别任务。     

基于Gaussian-Hermite矩的旋转运动模糊不变量

目的 模糊图像的分析与识别是图像分析与识别领域的重要方向。有些图像形成过程中成像系统与物体之间存在相对旋转运动,如因导弹高速自旋转造成的制导图像的旋转运动模糊。大多数对于这类图像的识别都需要先对模糊图像进行“去模糊”的预处理,且该类方法存在计算时间复杂度较高及不适定的问题。对此,提出一种直接提取旋转运动模糊图像中的不变特征,用于旋转运动模糊图像目标检索和识别。方法 本文以旋转运动模糊的退化模型为出发点,提出了旋转运动模糊Gaussian-Hermite (GH)矩,构造了一组由5个对旋转变换和旋转运动模糊保持不变性的GH矩不变量组成的特征向量(rotational motion blur Gaussian-Hermite moment invariants,RMB_GHMI-5),可从旋转变换和旋转运动模糊的图像中直接进行目标检索和识别,无需前置复杂的“去模糊”预处理过程。结果 在USC-SIPI (University of Southern California — Signal and Image Processing Institute)数据集上进行不变性实验,对原图进行不同程度的旋转变换叠加旋转运动模糊处理,证明RMB_GHMI-5对于旋转变换和旋转运动模糊具有良好的稳定性和不变性。在两个数据集上与同类4种方法进行图像检索实验比较,在80%召回率下,本文方法维数更少,相比性能第2的特征向量,在Flavia数据集中,高斯噪声、椒盐噪声、泊松噪声和乘性噪声干扰下的准确率分别提高25.89%、39.95%、22.79%和35.80%;在Butterfly Image数据集中,高斯噪声、椒盐噪声、泊松噪声和乘性噪声干扰下的准确率分别提高4.79、7.63%、5.65%和18.31%。同时,在上述8个测试数据集中进行对比实验以验证融合算法的有效性,结果表明本文提出的GH矩和几何矩相融合算法显著改善了图像检索效果。结论 本文提出的RMB_GHMI-5特征向量在旋转变换和旋转运动模糊下具有良好的不变性与稳定性,在图像检索抗噪性能方面表现优异。相比同类方法,本文方法更具实际应用价值。     

酒店客房的电气设计

本文说明酒店客房设计有关电气消防安全、照明、VOD系统、背景音乐应该注意的几个问题。     

Assessing similarity models for human‐motion retrieval applications

The development of motion capturing devices poses new challenges in the exploitation of human‐motion data for various application fields, such as computer animation, visual surveillance, sports, or physical medicine. Recently, a number of approaches dealing with motion data have been proposed, suggesting characteristic motion features to be extracted and compared on the basis of similarity. Unfortunately, almost each approach defines its own set of motion features and comparison methods; thus, it is hard to fairly decide which similarity model is the most suitable for a given kind of human‐motion retrieval application. To cope with this problem, we propose the human motion model evaluator, which is a generic framework for assessing candidate similarity models with respect to the purpose of the target application. The application purpose is specified by a user in form of a representative sample of categorized motion data. Respecting such categorization, the similarity models are assessed from the effectiveness and efficiency points of view using a set of space‐complexity, information‐retrieval, and performance measures. The usability of the framework is demonstrated by case studies of three practical examples of retrieval applications focusing on recognition of actions, detection of similar events, and identification of subjects. Copyright © 2015 John Wiley & Sons, Ltd.     

基于CIM/G的图形多版本管理与展示

针对智能电网调度控制系统在基于时间维度的图形管理与展示方面的不足,提出一种基于CIM/G的图形多版本管理与展示技术,使用CIM/G文件作为持久化存储介质,根据模型版本划分相应的图形版本标识,实现变化图形版本存储和图形多版本安全隔离,进而实现图形多版本的构建管理技术方案,同时给用户提供图形任意版本快速浏览、图形多版本差异集中展示的方法。该项技术已在华北电力调控分中心试点应用,可满足多项业务应用对多时态版本图形的需求,提升了调控系统对图形版本构建、存储、展示等方面的管控能力。     

基-2 FFT在音频分析及输出系统中的应用

主要介绍了一种将FFT算法移植到单片机上运行,通过对数字音频信号进行分析处理,以实现音乐频谱实时显示和声音输出的系统。系统硬件部分主要由声音输入、单片机模数转换、由LED组成的点阵单元以及声音信号放大输出等电路组成。利用高性能STC12C5A60S2单片机内建的模数转换功能,先将输入的音频信号采样、量化转换为数字信号,再通过软件编程进行FFT运算。输出处理结果点亮LED点阵,完成频谱显示。LED的明暗由音乐的频率变化决定。通过LM386运放芯片及外围电路将输入的音频信号进行放大后,由喇叭或者外接音箱输出。该设计不但具有较高的实用价值和观赏性,而且硬件电路结构简洁,开发、制作成本低。     

建筑与城市共生:城市复调音乐

阐释建筑与城市的关系犹如根茎,是一种生命结构;建筑与城市共生,建筑构成城市的物质环境和文化环境,城市也为建筑提供物质环境和文化环境。指出在历史环境中插入新建筑是一项挑战,同时也构成了建筑与城市多声部、多调性的复调音乐。     

改进的强度相干成像室内实验方法

设计了一种新的强度相干成像室内实验方法,以简化现有强度相干实验过程,获取接近实际观测的测量数据。首先,分析了强度相干成像原理及影响成像质量的因素;介绍了现有强度相干成像实验方法并分析其存在的不足。然后,利用赝热光源和CCD设计了一种新的强度相干成像方法;介绍了利用此种方法模拟强度相干成像的原理及特点。最后,进行了室内强度相干成像实验,验证了提出方法的可行性。实验结果表明:利用提出的实验方法能够较好地测量目标的空间功率谱,测量噪声主要分布在高频部分;当测量信噪比大于20时,利用相位恢复方法可恢复获得较好的目标光强分布图像。实验显示,提出的成像方法能够较好地模拟强度相干成像,实验中能够较为方便地调节光强随机涨落并观测基线参数,实现对小角直径目标的有效成像。