An image encryption scheme based on three-dimensional Brownian motion and chaotic system

At present, many chaos-based image encryption algorithms have proved to be unsafe, few encryption schemes permute the plain images as three-dimensional（3D） bit matrices, and thus bits cannot move to any position, the movement range of bits are limited, and based on them, in this paper we present a novel image encryption algorithm based on 3D Brownian motion and chaotic systems. The architecture of confusion and diffusion is adopted. Firstly, the plain image is converted into a 3D bit matrix and split into sub blocks. Secondly, block confusion based on 3D Brownian motion（BCB3DBM）is proposed to permute the position of the bits within the sub blocks, and the direction of particle movement is generated by logistic-tent system（LTS）. Furthermore, block confusion based on position sequence group（BCBPSG） is introduced, a four-order memristive chaotic system is utilized to give random chaotic sequences, and the chaotic sequences are sorted and a position sequence group is chosen based on the plain image, then the sub blocks are confused. The proposed confusion strategy can change the positions of the bits and modify their weights, and effectively improve the statistical performance of the algorithm. Finally, a pixel level confusion is employed to enhance the encryption effect. The initial values and parameters of chaotic systems are produced by the SHA 256 hash function of the plain image. Simulation results and security analyses illustrate that our algorithm has excellent encryption performance in terms of security and speed.

Construction of three-dimensional atlas of the lenticular nuclei and its subnucleus based on the cryosection images from Chinese visible human:a preliminary study

Objective To establish a 3D atlas of the lenticular nuclei and its subnucleus with the cryosection images of the male from "Atlas of Chinese Visible Human". Methods The lenticular nuclei and its subnucleus were segmented from the cryosection images and reconstructed with the software

Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images

Three-dimensional(3D)reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing units.In the coming years,most patient care will shift toward this new paradigm.However,development of fast and accurate 3D models from medical images or a set of medical scans remains a daunting task due to the number of pre-processing steps involved,most of which are dependent on human expertise.In this review,a survey of pre-processing steps was conducted,and reconstruction techniques for several organs in medical diagnosis were studied.Various methods and principles related to 3D reconstruction were highlighted.The usefulness of 3D reconstruction of organs in medical diagnosis was also highlighted.

Human Observation-Inspired Universal Image Acquisition Paradigm Integrating Multi-Objective Motion Planning and Control for Robotics

Image acquisition stands as a prerequisite for scrutinizing surfaces inspection in industrial high-end manufacturing.Current imaging systems often exhibit inflexibility,being confined to specific objects and encountering difficulties with diverse industrial structures lacking standardized computer-aided design(CAD)models or in instances of deformation.Inspired by the multidimensional observation of humans,our study introduces a universal image acquisition paradigm tailored for robotics,seamlessly integrating multi-objective optimization trajectory planning and control scheme to harness measured point clouds for versatile,efficient,and highly accurate image acquisition across diverse structures and scenarios.Specifically,we introduce an energybased adaptive trajectory optimization(EBATO)method that combines deformation and deviation with dual-threshold optimization and adaptive weight adjustment to improve the smoothness and accuracy of imaging trajectory and posture.Additionally,a multi-optimization control scheme based on a meta-heuristic beetle antennal olfactory recurrent neural network(BAORNN)is proposed to track the imaging trajectory while addressing posture,obstacle avoidance,and physical constraints in industrial scenarios.Simulations,real-world experiments,and comparisons demonstrate the effectiveness and practicality of the proposed paradigm.

A graph theory model using human nature structure

A graph theory model of the human nature structure( GMH) for machine vision and image/graphics processing is described in this paper. Independent from the motion and deformation of contours,the human nature structure( HNS) embodies the most basic movement characteristics of the body. The human body can be divided into basic units like head,torso,and limbs. Using these basic units,a graph theory model for the HNS can be constructed. GMH provides a basic model for human posture processing,and the outline in the perspective projection plane is the body contour of an image. In addition,the GMH can be applied to articulated motion and deformable objects,e. g.,in the design and analysis of body posture,by modifying mapping parameters of the GMH.

基于Leap Motion手势识别机械臂的研究

提出了一种基于Leap Motion识别手势控制机械臂的方法.该方法首先使用Leap Motion对人体手部深度点云信息进行提取,获得手部位姿态、手指位姿以及手势识别.然后将手部各关节点的空间姿态坐标转换为机械臂每个关节的控制角度.最后通过串口与下位机Arduino进行信息交互,实现机械臂向前、左右、抓取等动作.实验表明,该方法可以准确识别人体手势,实时控制机械臂.

多矢量姿态测量算法研究综述

将矢量测量转化为姿态测量,该问题在姿态估计中被称为Wahba问题。介绍了最小化Wahba问题的损失函数的不同算法,包括最优四元数估计器(ESOQ)、四元数估计器(QUEST)、快速最优姿态矩阵(FOAM)、矩阵的奇异值分解(SVD)等广泛使用的算法,以及基于快速线性四元数姿态估计器(FLAE)、黎曼流形等近年来提出的算法。给出了简单的计算原理与推导过程,并通过计算机仿真对照,归纳各类算法在计算旋转矩阵时的计算精度、鲁棒性。针对姿态测量算法应对当前应用场景的性能需求,简要介绍其在惯性测量单元(IMU)动态对准、协同集群视觉定姿、图像拼接等方面的适用可能和工作原理,并在此基础上简述了姿态测量算法当前的缺陷与未来的发展趋势。

基于STGCN算法的视频图像人体动作轮廓动态识别

人体动作轮廓在视频中的呈现具有多样性和连续性。人体动作不仅涉及到时间上的变化,还包括空间上的位置关系,受其姿势、速度、方向等影响。人体动作时空信息之间的关联难以充分捕捉,导致动作轮廓识别精度较低。为此,引入时空图卷积网络(STGCN)算法,提出一种视频图像人体动作轮廓动态识别方法。文中采用OpenPose模型从视频图像中提取描述关节点位置的置信图和描述人体关节间连接情况的二维矢量场,构建人体动作骨架图。结合视频帧时间序列组建人体动作骨架时空图,将其作为STGCN模型的输入,通过时空图卷积操作充分捕捉人体动作的时空特征后,采用Softmax层获取动态识别到的视频图像人体动作轮廓;并在STGCN模型中引入两种注意力模块,强化网络特征提取能力,提高动作轮廓识别精度。实验结果表明,所提方法可以有效实现视频图像人体动作轮廓的动态识别,引入的两种注意力模块对STGCN模型进行改进,可提升其动作轮廓识别效果。

IVIM、DKI联合DCE-MRI的影像组学在预测乳腺癌HER-2表达状态中的应用价值

目的 探讨联合体素内不相干运动(intravoxel incoherent motion, IVIM)、扩散峰度成像(diffusion kurtosis imaging,DKI)和动态对比增强磁共振成像(dynamic contrast-enhanced magnetic resonance imaging, DCE-MRI)、参数图构建影像组学模型预测乳腺癌患者人类表皮生长因子受体2(human epidermal growth factor receptor 2, HER-2)的表达状态。材料与方法 回顾性分析192例乳腺癌患者病例资料,根据患者的病理结果分为HER-2表达阳性组(48例)和HER-2表达阴性组(144例),术前均行IVIM、DKI及DCE-MRI。并按照8∶2的比例将病例随机分为训练集(154例)和测试集(38例)。在灌注分数(perfusion fraction, f)、灌注相关扩散系数(perfusion related diffusion coefficient, D^(*))、真实扩散系数(real diffusion coefficient, D)、平均扩散率(mean diffusivity, MD)和平均扩散峰度值(mean kurtosis, MK)参数图和第2期DCE-MRI(DCE-2)图像中勾画出病变区域的三维感兴趣区(region of interest, ROI),并提取其中的影像组学特征。采用Z分数归一化对特征进行标准化处理,并使用K最佳、最小冗余最大相关(max-relevance and min-redundancy, mRMR)、最小绝对收缩与选择算子回归(least absolute shrinkage and selection operator, LASSO)算法依次对特征进行降维和选择,通过logistic逻辑回归(logistic regression, LR)分类器分别建立参数图模型及联合模型,并采用5折交叉验证法验证模型的稳定性。通过受试者工作特征(receive operating characteristic,ROC)曲线和曲线下面积(area under the curve, AUC)对不同参数图像模型及联合模型的诊断效能进行分析,使用DeLong检验对各模型间ROC曲线进行比较,使用决策曲线分析(decision curve analysis, DCA)对模型的临床价值进行评估。结果 从每个ROI中提取了2286个MRI特征,在f、D^(*)、D、MD、MK参数图、第2期DCE-MRI和联合序列中分别筛选得到7、6、7、6、7、12、10个特征与HER-2表达状态相关。f、D^(*)、D、MD、MK参数图模型及第2期DCE模型在测试集中的AUC分别为0.693、0.679、0.586、0.682、0.661、0.732;联合模型在测试集中的AUC为0.861 (95%CI:0.775~0.958),敏感度和特异度分别为100.0%和71.4%,经DeLong检验,训练集中联合模型与f、D、D^(*)、MD、MK参数图模型及DCE-2模型之间AUC差异均有统计学意义(P均<0.05)。结果表明联合模型对预测HER-2的表达状态优于单一模型。结论 基于DCE-MRI、IVIM和DKI的影像组学联合模型可以在术前有效预测乳腺癌患者的HER-2表达状态,有助于临床对乳腺癌进行诊断、分型、制订治疗方案及预后。

DPT‐tracker:Dual pooling transformer for efficient visual tracking

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input images.To alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time dimensions.MCPT aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking prediction.DPT tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking methods.Extensive experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

基于改进稠密网络的视频监控人脸识别算法研究

为了提升视频监控中的人脸识别能力,研究利用运动历史图像算法来实现人体跟踪,提出了一种改进稠密网络。在结果中显示,研究采用的人体跟踪算法的跟踪准确率高达99.5%,同时提出的识别算法的识别准确率能够稳定在99.7%以上,且能够针对不同表情特征的人脸表现出较高的识别准确率。以上结果表明,改进稠密网络能够有效提升视频监控人脸识别能力,对城市监控的智能化发展具有重要意义。

基于条件GAN的人体复杂动作轮廓智能捕捉研究

为了精确捕捉人体在各种复杂动作中的姿态变化,并减少动作轮廓捕捉的误判和漏判情况,研究一种基于条件GAN的人体复杂动作轮廓智能捕捉方法。构建人体复杂动作图像前景模型,利用该前景模型去除人体复杂动作图像内的干扰背景,获得其前景图像。将人体复杂动作前景图像输入到条件GAN模型内,该模型使用生成器,依据叠加条件提取人体复杂动作前景图像特征,并生成人体复杂动作轮廓图像,将该轮廓图像输入到判别器内,判别器对生成器生成的人体复杂动作轮廓图像进行判别,输出其判别值;同时,生成器依据该判别值对人体复杂动作轮廓图像进行调整和优化。生成器和判别器不断对抗,当条件GAN模型的损失函数达到最小时,生成器输出最终人体复杂动作轮廓图像,实现人体复杂动作轮廓智能捕捉。实验结果表明,所提方法可有效捕捉人体复杂动作轮廓,且智能捕捉的人体复杂动作轮廓周长与其实际轮廓周长差异较小。

基于Kinect深度图像信息的人体运动检测

人体运动检测是计算机视觉人体运动分析的关键环节。根据Kinect深度图像的特点,引入并改进Vi Be算法处理深度图像进行人体运动检测。考虑到深度图像中地面像素值连续性造成的地面附近运动检测困难,提出了一种自适应的图像分层处理和不同邻域模式的建模方式,增加了去除"鬼影"现象的参考模型。像素分类时增加了前景点检验步骤,通过当前像素与参考模型的比较消除"鬼影"。在模型更新方面增加了基于前景点的背景模型更新策略,解决了"黑影"现象问题。采用阈值法对分类结果进行了误检点消噪处理。实验结果表明所提出的改进Vi Be算法能够比较准确地检测出人体运动。

基于模型的人体运动跟踪

在计算机视觉领域，人体运动分析的研究正因其广泛的应用前景而越来越受到研究者的重视．对图像序列中的人体运动进行跟踪是其中的关键技术．由于人体运动的特殊复杂性，已有的研究方法都对人体加上了许多限制条件．文中提出了一种新的方法来进行人体运动跟踪．该方法不针对特定人体运动方式，能对复杂、变化背景中的人体各部位的大幅度运动进行跟踪．其基本思想是在建立人体模型的基础上，按自上而下的顺序依次跟踪人体各部分，并在其中采用了基于图像差分的运动预测和分区域直方图相似度算法．

Three-dimensional CT angiography and surgical correlation in the evaluation of intracranial aneurysms

OBJECTIVE: To evaluate the diagnostic accuracy of three-dimensional CT angiography in the surgical treatment of intracranial aneurysms. METHODS: Twenty-four patients suspected of intracranial aneurysms underwent routine catheter four-vessel angiography, three dimensional CT angiography (3D-CTA), magnetic resonance angiography (MRA) or conventional digital subtraction angiography (DSA). RESULTS: A total of 28 aneurysms were detected by CT angiography in this study. Twenty-one patients each had a single aneurysm, two patients each had two aneurysms, and one had three aneurysms. The shapes of aneurysms revealed by 3D-CTA were round in 20 lesions, elliptical in 5, and 1 obulated in 3. Of the 24 lesions which were completely disclosed during surgery, the shapes correlated well with the 3D-CT angiograms. The mean diameter of the aneurysmal neck was 5.9 mm in 3D-CTA images, with the smallest being 1.6 mm and the largest 13.7 mm. The size was very close to the actual size measured at surgery (P

基于普通摄像机的人体运动信息检测系统

介绍了一种新型基于普通摄像技术的人体运动信息检测系统。探讨了运用图像处理技术快速准确识别与跟踪关节位置标志点的方法，解决了影响系统检测精度和运行速度的关键问题。该系统用普通摄像机取代专用设备，降低了系统成本；使用操作方便，具有较高的精度，不仅适用于人体运动信息的检测，而且可用作其它较低速运动物体的运动图像检测与分析。

一种新型人体运动图像实时分析系统

目的 :研制人体运动图像实时分析系统。方法 :应用普通面阵电荷耦合器件摄像头 ,采用动态图像采集以及标志点图像自动识别与跟踪技术进行人体运动图像检测分析。结果 :分析系统能实现人体运动图像的在线检测与分析。结论 :采用通用硬件制做的人体运动图像分析系统成本低 ,性能可靠 ,维护与升级方便。

虚拟人面部行为的合成

虚拟人是虚拟现实环境中很重要的一部分，对于虚拟人行为的研究除了应从宏观上考虑虚拟人的群体行为属性之外，对个体行为用性的研究也非常重要．个体行为包括自然行为和意识行为．自然行为主要是和脸部、头部以及四肢运动有关的行为．而意识行为则包括与语言和心理活动相关联的表情、发声以及对应的唇动手势动作等．本文旨在研究与意识行为有关的虚拟人面部图像合成技术，讨论了标准人脸图像的参数合成方法，给出了特定人脸图像与标准人脸图像的映射以及面部表情图像和发音口型的合成方法．

利用图像序列和人体模型重构人体的三维运动

利用图像序列对人体运动进行重构是人体运动分析研究中的一项关键技术。在直接线性变换技术以及人体生物力学模型的基础上,给出了用单个固定摄像机所拍摄的运动图像序列进行了人体运动空间重构的方法。本文提出了一种新的摄像机标定方法,根据实际情况可以选择世界坐标系以简化定标工作。对一段人体运动录像进行了重构计算,将计算结果和试验测试结果进行了比较,二者吻合良好。对该方法的优缺点进行了分析论述。

基于联合双边滤波器的Kinect深度图像滤波算法

针对Kinect镜头采集的深度图像一般有噪声和黑洞现象,直接应用于人体动作跟踪和识别等系统中效果差的问题,提出一种基于联合双边滤波器的深度图像滤波算法。算法利用联合双边滤波原理,将Kinect镜头同一时刻采集的深度图像和彩色图像作为输入,首先,用高斯核函数计算出深度图像的空间距离权值和RGB彩色图像的灰度权值;然后,将这两个权值相乘得到联合滤波权值,并利用快速高斯变换替换高斯核函数,设计出联合双边滤波器;最后,用此滤波器的滤波结果与噪声图像进行卷积运算实现Kinect深度图像滤波。实验结果表明,所提算法应用在人体动作识别和跟踪系统后,可显著提高在背景复杂场景中的抗噪能力,识别正确率提高17.3%,同时所提算法的平均耗时为371 ms,远低于同类算法。所提算法保持了联合双边滤波平滑保边的优点,由于引入彩色图像作为引导图像,去噪的同时也能对黑洞进行修补,因此该算法在Kinect深度图像上的去噪和修复效果优于经典的双边滤波算法和联合双边滤波算法,且实时性强。