Fast Mean‐Curvature Flow via Finite‐Elements Tracking

In this paper, we present a novel approach for efficiently evolving meshes using mean‐curvature flow. We use a finite‐elements hierarchy that supports an efficient multigrid solver for performing the semi‐implicit time‐stepping. Although expensive to compute, we show that it is possible to track this hierarchy through the process of surface evolution. As a result, we provide a way to efficiently flow the surface through the evolution, without requiring a costly initialization at the beginning of each time‐step. Using our approach, we demonstrate a factor of nearly seven‐fold improvement over the non‐tracking implementation, supporting the evolution of surfaces consisting of 1M triangles at a rate of just a few seconds per update.     

Direct isosurface visualization of hex-based high-order geometry and attribute representations

In this paper, we present a novel isosurface visualization technique that guarantees the accurate visualization of isosurfaces with complex attribute data defined on (un)structured (curvi)linear hexahedral grids. Isosurfaces of high-order hexahedral-based finite element solutions on both uniform grids (including MRI and CT scans) and more complex geometry representing a domain of interest that can be rendered using our algorithm. Additionally, our technique can be used to directly visualize solutions and attributes in isogeometric analysis, an area based on trivariate high-order NURBS (Non-Uniform Rational B-splines) geometry and attribute representations for the analysis. Furthermore, our technique can be used to visualize isosurfaces of algebraic functions. Our approach combines subdivision and numerical root finding to form a robust and efficient isosurface visualization algorithm that does not miss surface features, while finding all intersections between a view frustum and desired isosurfaces. This allows the use of view-independent transparency in the rendering process. We demonstrate our technique through a straightforward CPU implementation on both complex-structured and complex-unstructured geometries with high-order simulation solutions, isosurfaces of medical data sets, and isosurfaces of algebraic functions.     

Nested Tracking Graphs

Tracking graphs are a well established tool in topological analysis to visualize the evolution of components and their properties over time, i.e., when components appear, disappear, merge, and split. However, tracking graphs are limited to a single level threshold and the graphs may vary substantially even under small changes to the threshold. To examine the evolution of features for varying levels, users have to compare multiple tracking graphs without a direct visual link between them. We propose a novel, interactive, nested graph visualization based on the fact that the tracked superlevel set components for different levels are related to each other through their nesting hierarchy. This approach allows us to set multiple tracking graphs in context to each other and enables users to effectively follow the evolution of components for different levels simultaneously. We demonstrate the effectiveness of our approach on datasets from finite pointset methods, computational fluid dynamics, and cosmology simulations.     

On‐The‐Fly Tracking of Flame Surfaces for the Visual Analysis of Combustion Processes

The visual analysis of combustion processes is one of the challenges of modern flow visualization. In turbulent combustion research, the behaviour of the flame surface contains important information about the interactions between turbulence and chemistry. The extraction and tracking of this surface is crucial for understanding combustion processes. This is impossible to realize as a post‐process because of the size of the involved datasets, which are too large to be stored on disk. We present an on‐the‐fly method for tracking the flame surface directly during simulation and computing the local tangential surface deformation for arbitrary time intervals. In a massively parallel simulation, the data are distributed over many processes and only a single time step is in memory at any time. To satisfy the demands on parallelism and accuracy posed by this situation, we track the surface with independent micro‐patches and adapt their distribution as needed to maintain numerical stability. With our method, we enable combustion researchers to observe the detailed movement and deformation of the flame surface over extended periods of time and thus gain novel insights into the mechanisms of turbulence–chemistry interactions. We validate our method on analytic ground truth data and show its applicability on two real‐world simulations.     

Application of Superpixels to Segment Several Landmarks in Running Rodents

Examining locomotion has improved our basic understanding of motor control and aided in treating motor impairment. Mice and rats are the model system of choice for basic neuroscience studies of human disease. High frame rates are needed to quantify the kinematics of running rodents, due to their high stride frequency. Manual tracking, especially for multiple body landmarks, becomes extremely time-consuming. To overcome these limitations, we proposed the use of superpixels based image segmentation as superpixels utilized both spatial and color information for segmentation. We segmented some parts of the body and tested the success of segmentation as a function of color space and SLIC segment size. We used a simple merging function to connect the segmented regions considered as a neighbor and having the same intensity value range. In addition, 28 features were extracted, and t-SNE was used to demonstrate how much the methods are capable to differentiate the regions. Finally, we compared the segmented regions to a manually outlined region. The results showed for segmentation, using the RGB image was slightly better compared to the hue channel. For merging and classification, however, the hue representation was better as it captures the relevant color information in a single channel.     

基于时间一致性局部颜色特征的无纹理3D物体实时跟踪

针对无纹理3D物体跟踪算法在复杂背景和运动模糊的情况下容易跟踪失败、跟踪速度难以达到强实时等问题,提出一种基于时间一致性局部颜色特征的3D物体实时跟踪算法.首先在物体3D模型投影轮廓法向搜索线上计算像素颜色的加权均值作为局部颜色特征,增强颜色特征在复杂环境中的表征能力,并对局部颜色特征进行时间一致性更新,剔除前景背景颜色相似的局部颜色特征,以避免相似前景背景颜色导致的跟踪失败;然后定义基于局部颜色特征的能量函数,并推导该能量函数的解析导函数;最后改进了优化物体姿态的高斯牛顿法,通过添加阻尼参数防止姿态优化陷入局部极值,提高姿态估计精度和跟踪速度.实验使用7组测试视频验证文中算法,结果表明,该算法能更有效地克服复杂背景和运动模糊的干扰,在未使用并行计算的前提下可实现强实时跟踪.     

基于模糊PID的UEGO传感器控制方法研究

宽域废气氧传感器具有较宽的空燃比测量范围,广泛地应用于稀燃发动机空燃比控制系统;UEGO传感器需要配套控制器才能正常工作,通过控制传感器的温度和泵电流,实现空燃比的测量;然而,UEGO传感器温度具有非线性,泵电流参数存在摄动,给其控制带来了困难;为了缩短冷启动时间,采用斜坡加热与模糊PI相结合的分段温度控制策略,以克服温度的非线性和冷启动的加热限制;采用基于OE模型的系统辨识法,建立不同工况下的泵电流模型,据此采用模糊PID的控制方法提高泵电流的控制精度与响应速度,克服泵电流参数不确定性的影响,从而提高传感器的动态性能;在空气环境和混合气配气平台上进行实验,结果表明:UEGO传感器冷启动时间少于20 s,温度控制精度较高,泵电流调节时间为191 ms,动态响应速度快、抗干扰能力强。     

非对称高斯函数的时变体数据特征跟踪及可视化

目的 在传统基于时序曲线的时变体数据特征的识别与跟踪过程中,不仅需要用户具备丰富的先验知识来确定感兴趣特征的时序曲线形状,而且时序曲线段的匹配和抽取过程亦复杂,难以交互,这一定程度上降低了时变体数据可视化及分析效率.提出了一种新的基于高斯时序曲线的时变体数据可视化及分析方法.方法 首先,对时序曲线进行低通滤波,准确地查找极小值点,对时序曲线进行分段;进而,引入非对称高斯函数对时序曲线段进行拟合获得高斯时序曲线;为了进一步方便用户识别感兴趣特征,观察特征在时间域上的演变过程,提供一种便捷的交互技术,由用户单击任一时间步绘制结果图像中感兴趣的特征信息,分析视线方向上的特征可见性,以确定感兴趣特征及其对应的高斯曲线,进而由参数匹配获得所有时间步中感兴趣特征信息.结果 为了验证本文算法的高效性与可行性,对时序仿真的飓风数据进行特征跟踪与交互式可视化,可以看出,本文算法不仅可以准确跟踪飓风中心特征,而且特征分析与可视化效率亦大大提升.结论 相比于传统的时变体数据可视化方法,本文算法不需要用户先验知识的前提下,利用非对称高斯模拟时序曲线变化,进而由高斯参数匹配代替复杂的时序曲线匹配过程,有效地提升了时变体数据可视化及分析效率.     

Interactive comparison of scalar fields based on largest contours with applications to flow visualization

Understanding fluid flow data, especially vortices, is still a challenging task. Sophisticated visualization tools help to gain insight. In this paper, we present a novel approach for the interactive comparison of scalar fields using isosurfaces, and its application to fluid flow datasets. Features in two scalar fields are defined by largest contour segmentation after topological simplification. These features are matched using a volumetric similarity measure based on spatial overlap of individual features. The relationships defined by this similarity measure are ranked and presented in a thumbnail gallery of feature pairs and a graph representation showing all relationships between individual contours. Additionally, linked views of the contour trees are provided to ease navigation. The main render view shows the selected features overlapping each other. Thus, by displaying individual features and their relationships in a structured fashion, we enable exploratory visualization of correlations between similar structures in two scalar fields. We demonstrate the utility of our approach by applying it to a number of complex fluid flow datasets, where the emphasis is put on the comparison of vortex related scalar quantities.     

Fuzzy region assignment for visual tracking

In this work we propose a new approach based on fuzzy concepts and heuristic reasoning to deal with the visual data association problem in real time, considering the particular conditions of the visual data segmented from images, and the integration of higher-level information in the tracking process such as trajectory smoothness, consistency of information, and protection against predictable interactions such as overlap/occlusion, etc. The objects’ features are estimated from the segmented images using a Bayesian formulation, and the regions assigned to update the tracks are computed through a fuzzy system to integrate all the information. The algorithm is scalable, requiring linear computing resources with respect to the complexity of scenarios, and shows competitive performance with respect to other classical methods in which the number of evaluated alternatives grows exponentially with the number of objects.     

FASTER–RCNN for Skin Burn Analysis and Tissue Regeneration

Skin is the largest body organ that is prone to the environment most specifically. Therefore the skin is susceptible to many damages, including burn damage. Burns can endanger life and are linked to high morbidity and mortality rates. Effective diagnosis with the help of accurate burn zone and wound depth evaluation is important for clinical efficacy. The following characteristics are associated with the skin burn wound, such as healing, infection, painand stress and keloid formation. Tissue regeneration also takes a significant amount of time for formation while considering skin healing after a burn injury. Deep neural networks can automatically assist in the extraction of features from a burn image. In our approach to burn wound analysis and regeneration of the tissue of the skin burn wound, we use the Faster RCNN (Regional Convolutional Neural Network), which is based on their severity of the burn wound. The success rates of skin cure for burning injuries can be dramatically increased with the use of different skin replacements. Our objective is to analyze different deep learning techniques that may help to analyze and classify burn wounds in a superficial, partial and complete thickness, while treating burn wounds more accurately. The application of Faster RCNN effectively classifies wound without first degree, second and third degree confusion, thus providing a suitable solution to burning wounds. The advancement in the field of profound training offers an important path in the field of the processing and burning of trauma.     

Interactive isosurface ray tracing of time-varying tetrahedral volumes

We describe a system for interactively rendering isosurfaces of tetrahedral finite-element scalar fields using coherent ray tracing techniques on the CPU. By employing state-of-the art methods in polygonal ray tracing, namely aggressive packet/frustum traversal of a bounding volume hierarchy, we can accomodate large and time-varying unstructured data. In conjunction with this efficiency structure, we introduce a novel technique for intersecting ray packets with tetrahedral primitives. Ray tracing is flexible, allowing for dynamic changes in isovalue and time step, visualization of multiple isosurfaces, shadows, and depth-peeling transparency effects. The resulting system offers the intuitive simplicity of isosurfacing, guaranteed-correct visual results, and ultimately a scalable, dynamic and consistently interactive solution for visualizing unstructured volumes.     

A signer-independent Arabic Sign Language recognition system using face detection, geometric features, and a Hidden Markov Model

In this paper, we propose an image-based system for Arabic Sign Language (ArSL) recognition. The algorithm starts by detecting the face of the signer using a Gaussian skin color model. The centroid of the detected face is then used as a reference point for tracking the hands’ movements. The hands regions are segmented using a region growing algorithm assuming the signer wears a yellow and an orange colored gloves. From the segmented hands regions, an optimal set of features is extracted. To represent the time varying feature patterns, a Hidden Markov Model (HMM) is then used. Before using HMM in testing, the number of states and the number of Gaussian mixtures are optimized. The proposed system was implemented for both signer dependent and signer independent conditions. The experimental results show that an accuracy of more than 95% can be achieved with a large database of 300 signs. The results outperform previous work on ArSL mainly restricted to small vocabulary size.     

Level Set Curve Matching and Particle Image Velocimetry for Resolving Chemistry and Turbulence Interactions in Propagating Flames

We present an imaging, image processing, and image analysis framework for facilitating the separation of flow and chemistry effects on local flame front structures. Image data of combustion processes are obtained by a novel technique that combines simultaneous measurements of distribution evolutions of OH radicals and of instantaneous velocity fields in turbulent flames. High-speed planar laser induced fluorescence (PLIF) of OH radicals is used to track the response of the flame front to the turbulent flow field. Instantaneous velocity field measurements are simultaneously performed using particle image velocimetry (PIV). Image analysis methods are developed to process the experimentally captured data for the quantitative study of turbulence/chemistry interactions. The flame image sequences are smoothed using nonlinear diffusion filtering and flame boundary contours are automatically segmented using active contour models. OH image sequences are analyzed using a curve matching algorithm that incorporates level sets and geodesic path computation to track the propagation of curves representing successive flame contours within a sequence. This makes it possible to calculate local flame front velocities, which are strongly affected by turbulence/chemistry interactions. Since the PIV data resolves the turbulent flow field, the combined technique allows a more detailed investigation of turbulent flame phenomena.     

Segmentation of multi-material CT data of mechanical parts for extracting boundary surfaces

We introduce a method for segmentation of materials segmented in volumetric models of mechanical parts created by X-ray CT scanning for the purpose of generating their boundary surfaces. When the volumetric model is composed of two materials, one for the object and the other for the background (Air), these boundary surfaces can be extracted as isosurfaces using a surface contouring method. For a volumetric model composed of more than two materials, we need to classify the voxel types into segments by material and then use a surface contouring method that can deal with both CT values and material types. Here we propose a method for precisely classifying the volumetric model into its component materials using a modified and combined method of two well-known algorithms in image segmentation, region growing and Graph-cut. We then apply our non-manifold iso-contouring method to generate triangulated mesh surfaces. In addition, we demonstrate the effectiveness of our method by constructing high-quality triangular mesh models of the segmented parts.     

Robust adaptive fault‐tolerant tracking control for uncertain linear systems with time‐varying performance bounds

In this paper, the problem of robust adaptive fault‐tolerant tracking control with time‐varying performance bounds is investigated for a class of linear systems subject to parameter uncertainties, external disturbances and actuator failures. In order to ensure the norm of the tracking error less than the user‐defined time‐varying performance bounds, we propose a new control strategy which is predicated on the generalized restricted potential function. Compared with the existing result, a novel method which provides two design freedoms is developed to reduce the tracking error. According to the online estimation information provided by adaptive mechanism, a fault‐tolerant tracking control method guaranteeing time‐varying performance bounds is developed for robust tracking of reference model. It is shown that the closed‐loop signals are bounded and the tracking error within an a priori given, time‐varying performance bounds. A simulation result is provided to demonstrate the efficacy of the proposed fault‐tolerant tracking control method.     

基于D-S证据理论的彩色航空影像阴影提取方法

阴影特征在影像,特别是遥感影像中具有重要的意义. 本文针对彩色航空遥感影像的颜色信息进行分析, 提出了表述阴影在 RGB 和 HIS 空间的三种不同特征, 并利用 Mean Shift 分割的方法对彩色航空影像进行区域分割. 选取分割后的区域, 统计每个区域的多个颜色特征并定义相应的概率分配函数, 利用其互补特性, 使用 Dempster-Shafer(D-S) 证据理论中的合成法则对其进行合成, 最终判别区域是阴影或非阴影. 对实验图像进行阴影特征提取,取得了较好的效果     

Two-dimensional visual simulation of flames,smoke and the spread of fire

Since CG simulation of natural phenomena on the basis of their forms and motions has many applications, such as various landscape designs and special effects in films, it is very important and interesting to develop efficient techniques for their visual simulation. It is especially interesting to produce realistic images and animations of flames and smoke, on account of their complicated patterns of behaviour. Effective simulation methods for flames and smoke are expected to satisfy the following requirements:

• 1 The motion of flames or smoke produced in an interaction with obstacles can be simulated.
• 2 The motion of flames and smoke can be easily controlled according to scenarios.
• 3 The spread of fire can be simulated.

Although several useful representation methods have been proposed so far, simulating flames and smoke still remains a challenging problem. In this paper, we first describe our basic two-dimensional visual simulation method based on particle-based simulation, but not based on exact physical simulation. Roughly speaking, our method assumes that the images of flames and smoke are basically obtained by visualizing turbulence, that is, the particles of flames and smoke play the role of tracers in the field of turbulence. Next, we present an improved method for simulating the spread of fire and the appearance of smoke produced in an incomplete combustion. Simultaneously, we show several examples of the simulation. Finally, we will touch slightly on further problems for extending the model to one which works in three-dimensional space.     

A large margin framework for single camera offline tracking with hybrid cues

We introduce MMTrack (max-margin tracker), a single-target tracker that linearly combines constant and adaptive appearance features. We frame offline single-camera tracking as a structured output prediction task where the goal is to find a sequence of locations of the target given a video. Following recent advances in machine learning, we discriminatively learn tracker parameters by first generating suitable bad trajectories and then employing a margin criterion to learn how to distinguish among ground truth trajectories and all other possibilities. Our framework for tracking is general, and can be used with a variety of features. We demonstrate a system combining a variety of appearance features and a motion model, with the parameters of these features learned jointly in a coherent learning framework. Further, taking advantage of a reliable human detector, we present a natural way of extending our tracker to a robust detection and tracking system. We apply our framework to pedestrian tracking and experimentally demonstrate the effectiveness of our method on two real-world data sets, achieving results comparable to state-of-the-art tracking systems.