Animation of Biological Organ Growth Based on L-systems

In contrast with the growth of plants and trees, human organs can undergo significant changes in shape through a variety of global transformations during the growth period, such as bending or twisting. In our approach, the topology of a human organ is represented by a skeleton in the form of a tree or cycled graph. The length of skeleton growth can be simulated by an algebraic L-system that also produces discrete events. The paper shows how to include global transformations into the formalism of L-systems to obtain a continuous process. The shape of the organ is approximated by a number of ellipsoidal clusters centred at points on the skeleton. The proposed growth model of the organ continually responds to the positional changes of surrounding organs, thereby changing the organ shape locally. In our study, the stomach of a human embryo is used for the demonstration of organ development, and the methodology employed is also applicable to the animation of animal organs and their development.     

骨架驱动的作物叶片变形模拟

针对作物器官的变形问题,将骨架驱动物体变形的方法应用于作物器官的局部变形,提出了一种骨架驱动的叶片变形方法：生成叶片骨架模型,驱动骨架模型发生变形,并根据变形后的叶片骨架将变形操作应用到叶片曲面上,进而实现叶片曲面变形。应用该方法,分别对小麦叶片曲面进行弯曲和扭曲变形模拟。实验结果表明,基于骨架驱动的作物叶片形变方法能灵活控制叶片弯曲和扭曲程度,从而获得自然的叶片曲面变形效果。     

一种新的运动捕获数据转换方法

传统数据转换方法在计算骨骼关节点旋转信息时,会降低旋转信息的精确度。为此,提出一种新的运动捕获数据转换方法。以树型结构建立人体骨骼模型,根据关节点自身在骨架中的结构关系,通过构造分解法求解关节点的三自由度旋转信息,利用该信息驱动人体骨骼模型。实验结果验证该方法的有效性。     

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

First performed in 1954, organ transplantation is a universally practiced clinical procedure. This study uses ant colony optimization (ACO), radial basis function neural network (RBFNN), Kohonen’s self-organizing maps (SOM), and support vector machines (SVMs) to examine the effect of various cognitive, psychographic, and attitudinal factors on organ donation. ACO, RBFNN, SOM, and SVMs are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are altruistic values, perceived risks/benefits, knowledge, attitudes toward organ donation, and intention to donate organs. The paper shows how it is possible to identify various dimensions of organ donation behavior by uncovering complex patterns in the dataset and also shows the classification and clustering abilities of machine-learning systems.     

基于残差时空图卷积网络的3D人体行为识别

人体行为识别是智能监控、人机交互等诸多应用领域的一项基本技术。人体骨骼的动态变化为人体行为识别提供了重要的信息。传统方法通常只是采取人工信息标注或遍历规则,从而导致模型的表征能力有限、泛化性能差。采用一种引入了残差项的动态骨架模型——基于残差连接的时空图卷积网络,不仅克服了以往方法的限制,而且能够学习骨骼数据中的时空模型。在大型骨骼NTU-RGB+D数据集上,该网络模型不仅提高了人体行为特征的表征能力,而且增强了泛化能力,取得了比现有的模型更好的识别效果。     

基于凸壳与有向包围盒的骨架提取方法

为获取三维模型的几何及拓扑信息,提出一种基于凸壳与有向包围盒(OBB)的线性骨架提取方法.首先将三维网格模型进行分割生成多个子网格模型;然后对各子网格中的点集求取凸壳作为该子网格点集的近似,由凸壳顶点的形心构成原始骨架点;再用OBB进行重叠计算求出相交点集,以生成关节骨架点;最后对原始骨架点与关节骨架点进行连接,经冗余检测后形成完整骨架.实验结果表明,该方法快速、有效,提取出的骨架能保证连通性与中心性且能很好地提取关节骨架点,为蒙皮关节动画、模型形状分析等提供有效信息.     

Color pornographic image detection based on color-saliency preserved mixture deformable part model

To utilize the rich semantic information of sexual organs, we propose a new framework for pornographic image detection based on sexual organ detectors. Traditional sexual organ detectors are built on shape features. Since the color distribution of sexual organ in same pose is consistent, color is an important visual clue to represent sexual organs. We use color attribute to describe the local color of sexual organs and concatenate it with histogram of oriented gradients based shape feature to represent sexual organs. Based on the concatenated feature, we train sexual organ detectors by the color-saliency preserved mixture deformable part model (CPMDPM). We detect pornographic images sequentially with sexual organ detectors. In experiments, the optimal part number of the deformable part model is chosen experimentally. We evaluate the performance of each CPMDPM based sexual organ detector, which is superior over the shape feature based detector. The proposed pornographic detection method is superior over methods based on low level features of skin regions, bag of words model and color incorporated SIFT features etc.     

运动捕捉数据在Jack平台上的重定向技术研究

提出了一种BVH格式运动捕捉数据驱动Jack三维骨架模型产生人体运动效果的方法。将Peabody结构的Jack虚拟人模型简化成能够映射BVH数据的树状骨骼结构,使用欧拉角旋转方程建立运动捕捉数据与Jack角色模型的关节数据映射公式,最后在Jack平台上用Python等脚语言进行了编程实现。为在Jack平台中大规模重用运动捕捉数据提供了条件。     

A novel unsupervised 3D skeleton detection in RGB-D images for video surveillance

In this paper we present a novel moment-based skeleton detection for representing human objects in RGB-D videos with animated 3D skeletons. An object often consists of several parts, where each of them can be concisely represented with a skeleton. However, it remains as a challenge to detect the skeletons of individual objects in an image since it requires an effective part detector and a part merging algorithm to group parts into objects. In this paper, we present a novel fully unsupervised learning framework to detect the skeletons of human objects in a RGB-D video. The skeleton modeling algorithm uses a pipeline architecture which consists of a series of cascaded operations, i.e., symmetry patch detection, linear time search of symmetry patch pairs, part and symmetry detection, symmetry graph partitioning, and object segmentation. The properties of geometric moment-based functions for embedding symmetry features into centers of symmetry patches are also investigated in detail. As compared with the state-of-the-art deep learning approaches for skeleton detection, the proposed approach does not require tedious human labeling work on training images to locate the skeleton pixels and their associated scale information. Although our algorithm can detect parts and objects simultaneously, a pre-learned convolution neural network (CNN) can be used to locate the human object from each frame of the input video RGB-D video in order to achieve the goal of constructing real-time applications. This much reduces the complexity to detect the skeleton structure of individual human objects with our proposed method. Using the segmented human object skeleton model, a video surveillance application is constructed to verify the effectiveness of the approach. Experimental results show that the proposed method gives good performance in terms of detection and recognition using publicly available datasets.

     

A psycho-cognitive segmentation of organ donors in Egypt using Kohonen’s self-organizing maps

This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on organ donation in Egypt. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and nonlinear patterns. The results show that major variables affecting organ donation are related to perceived benefits/risks of organ donation, organ donation knowledge, attitudes toward organ donation, and intention to donate organs. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.     

基于Forge云的艺用人体解剖绘画仿真系统

针对目前人物动画制作以及绘画过程中对人体结构认识不足的问题,以及虚拟现实技术在教育中的优势,提出了一个基于Forge云的艺用人体解剖绘画仿真系统方案.系统按照人体结构比例,采用块面加线的模式完成人物模型的构建及可视化过程,完全遵照动画运动规律,以骨骼动画结合三维动作捕捉的方式实现人体运动仿真.通过Forge云平台和Three.js完成人机交互.最后,将漫画模块和Forge云模块双向通信,完成漫画人物姿态仿真.通过测试证明,该系统的仿真度和易用性较高,为数字化学习和移动学习提供了环境,有助于学习者深入理解人体解剖结构,并正确掌握漫画人物的造型方法.     

基于外观模型的人体下肢关节点定位方法

近年来,人体下肢关节点定位成为了人体运动跟踪与分析中一个重要研究课题。提出了一种下肢关节点自动定位方法,从无关节标记的人体运动图像序列中定位下肢关节点。该方法首先采用背景剪除技术从图像序列中分割人体目标对象,并建立人体下肢骨架模型。然后,利用关节角度预测方法估计膝踝关节点的位置,在基于下肢外观模型的匹配计算基础上获得下肢关节点的真实位置。实验结果表明,该方法简单有效,下肢关节点定位结果令人满意。     

基于人体骨架信息的行为检测系统设计

为了解决现有行为检测系统中依赖惯性传感器、检测结果不够准确的问题,设计了基于人体骨架信息的行为检测系统;系统采用Jetson Nano人工智能计算设备作为主控模块,结合图像采集模块、显示模块和以Atmega328单片机为主的报警模块构成;系统利用图像采集模块采集行为视频信息,通过主控模块中的行为检测器对视频中人体行为进行检测,报警模块通过串口接收检测结果并对危险行为进行预警;同时,利用人体骨架的关节空间运动幅度、肢体关联差异,建立了关节帧间位移矢量和骨骼夹角变化的关节行为模型,再借助长短时记忆网络提取行为特征,并训练实时行为检测器;经实验测试,该系统能够有效检测常见的人体行为并对危险行为类别进行报警提示。     

基于形态学的视频序列人体骨架提取

本文在数学形态学算法的基础上,出了一种新的序列图像中人体目标骨架提取方法。首先运用数学形态学对二值图像的滤波、腐蚀和膨胀功能平滑图像中人体目标的边缘,去除背景中误分割出来的噪声。之后运用形态学算法中的击中击不中变换细化目标,提取骨架。运用单人和多人运动图像序列进行实验,结果证明,本文提出的方法效果较好。     

一种快速人体骨架建模方法

提出从序列视频中快速建立人体骨架模型的方法。基于阴影特征采用Otsu算法完成运动目标检测中的阴影消除,得到准确的人体轮廓;对人体轮廓进行形态学的细化处理,采用新建立连通性结构标准和肢体关节点定位算法处理骨架建立人体骨架模型。实验结果表明,该方法对人体肢体部位各端点定位获得较高的准确率,能快速定位关节点,较好地得到人体骨架模型。     

From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations

We propose an algorithm allowing the construction of a structural representation of the cortical topography from a T1-weighted 3D MR image. This representation is an attributed relational graph (ARG) inferred from the 3D skeleton of the object made up of the union of gray matter and cerebro-spinal fluid enclosed in the brain hull. In order to increase the robustness of the skeletonization, topological and regularization constraints are included in the segmentation process using an original method: the homotopically deformable regions. This method is halfway between deformable contour and Markovian segmentation approaches. The 3D skeleton is segmented in simple surfaces (SSs) constituting the ARG nodes (mainly cortical folds). The ARG relations are of two types: first, theSS pairs connected in the skeleton; second, theSS pairs delimiting a gyrus. The described algorithm has been developed in the frame of a project aiming at the automatic detection and recognition of the main cortical sulci. Indeed, the ARG is a synthetic representation of all the information required by the sulcus identification. This project will contribute to the development of new methodologies for human brain functional mapping and neurosurgery operation planning.     

扦插植物地下组织构型建模与形态仿真

很多植物都采用扦插育苗,为实现扦插植物的工厂化繁殖,研究其地下组织的生长规律及可视化模拟模型是研发扦插育苗机械化装备的基础之一。为此,基于几何建模的方法,结合扦插植物的繁殖特点,对扦插植物地下组织的构型进行建模;采用VC++及Direct3D图形接口,开发扦插植物地下组织的三维可视化动态仿真程序,并以扦插蔷薇的地下组织为样本进行模拟。结果表明：模拟仿真图形与实际扦插蔷薇的地下组织形态特征相似度较高,模拟数值与实测结果的相对误差小于10%。该研究为植物构型建模仿真提供了一个新的方向。     

骨骼信息的人体行为识别综述

基于骨骼信息的人体行为识别旨在从输入的包含一个或多个行为的骨骼序列中,正确地分析出行为的种类,是计算机视觉领域的研究热点之一。与基于图像的人体行为识别方法相比,基于骨骼信息的人体行为识别方法不受背景、人体外观等干扰因素的影响,具有更高的准确性、鲁棒性和计算效率。针对基于骨骼信息的人体行为识别方法的重要性和前沿性,对其进行全面和系统的总结分析具有十分重要的意义。本文首先回顾了9个广泛应用的骨骼行为识别数据集,按照数据收集视角的差异将它们分为单视角数据集和多视角数据集,并着重探讨了不同数据集的特点和用法。其次,根据算法所使用的基础网络,将基于骨骼信息的行为识别方法分为基于手工制作特征的方法、基于循环神经网络的方法、基于卷积神经网络的方法、基于图卷积网络的方法以及基于Transformer的方法,重点阐述分析了这些方法的原理及优缺点。其中,图卷积方法因其强大的空间关系捕捉能力而成为目前应用最为广泛的方法。采用了全新的归纳方法,对图卷积方法进行了全面综述,旨在为研究人员提供更多的思路和方法。最后,从8个方面总结现有方法存在的问题,并针对性地提出工作展望。     

基于GPU的虚拟经络系统构建的研究

在GPU渲染及虚拟人理论基础上建立一个包含人体脏腑结构、骨骼和穴位的虚拟经络的研究系统.分析系统的整体结构,并阐述系统各模块的具体实现,内容包括模型获取、模型操作、穴位拾取算法和GPU渲染,最后通过GPU渲染与传统方式渲染的比较,给出实验结果.     

基于CNN与Transformer的医学图像分割

医学图像对疾病的诊断、治疗和评估均有所帮助,准确分割医学图像中的器官对于辅助医生的诊断具有重要的实际意义.由于医学图像中各器官部位与周围组织的图像对比度低,不同器官的边缘和形状也会存在很大差异,从而增加了分割的难度.针对这些问题,本文提出了一种基于卷积神经网络和Transformer的医学图像语义分割网络,有效提高了医学图像语义分割的精度.特征提取部分使用ResNet-50网络结构,在特征提取后使用Transformer模块来扩大感受野.在上采样过程中加入多个跳跃连接层,充分利用各阶段的特征提取信息,来恢复至与输入图像相近的分辨率.在胃肠道医学图像分割数据集上的实验结果证明本文的方法可以有效分割医学图像中的器官组织,提升分割准确率.