Interactive visualization of volumetric white matter connectivity in DT-MRI using a parallel-hardware Hamilton-Jacobi solver

In this paper we present a method to compute and visualize volumetric white matter connectivity in diffusion tensor magnetic resonance imaging (DT-MRI) using a Hamilton-Jacobi (H-J) solver on the GPU (Graphics Processing Unit). Paths through the volume are assigned costs that are lower if they are consistent with the preferred diffusion directions. The proposed method finds a set of voxels in the DTI volume that contain paths between two regions whose costs are within a threshold of the optimal path. The result is a volumetric optimal path analysis, which is driven by clinical and scientific questions relating to the connectivity between various known anatomical regions of the brain. To solve the minimal path problem quickly, we introduce a novel numerical algorithm for solving H-J equations, which we call the Fast Iterative Method (FIM). This algorithm is well-adapted to parallel architectures, and we present a GPU-based implementation, which runs roughly 50-100 times faster than traditional CPU-based solvers for anisotropic H-J equations. The proposed system allows users to freely change the endpoints of interesting pathways and to visualize the optimal volumetric path between them at an interactive rate. We demonstrate the proposed method on some synthetic and real DT-MRI datasets and compare the performance with existing methods.     

一种去除机载LiDAR航带重叠区冗余点云的方法

机载LiDAR系统在获取高密度地表点云的同时,也带来了数据冗余的问题,特别是在航带重叠区中尤为突出。旨在研究无完整航迹信息辅助下去除航带重叠点,提出了基于点云GPS时间直方图的去除航带重叠点的方法。该方法包括三个步骤:(1)建立点云的GPS时间直方图,并根据GPS时间直方图特点获取航带重叠区外包矩形以及外包矩形中的所有点云;(2)考虑到城市中高密度点云有助于建筑物的三维重建,通过滤波分类处理获取建筑物点并予以全部保留;(3)对重叠区中除建筑物点外的其他所有点进行格网数据组织并根据GPS时间直方图逐格网去除航带冗余点。实验结果表明,该方法能较好地保留建筑物点的同时高效去除航带重叠点且不依赖于航迹信息,提高了后续数据分析处理的效率。     

基于混沌的微弱三角波信号检测方法

利用混沌系统的相轨迹对初值敏感而列噪声免疫的特性,提出采用三角波和正弦波分别作策动力对微弱三角波信号的幅值进行检测。最后,在Matlab／Simulink环境下进行仿真实验。结论表明：混沌理论可以实现对信噪比很低的三角波信号幅值进行检测,并且取得较高的精度。     

Does the difference between information and scientific visualization really matter?

Is it necessary to continue to define a difference between information and scientific visualization? Is the determined need for these differences creating confusion rather than helping investigators understand how to effectively apply visual display techniques to data and information? The author considers how cartographic and geographic information techniques seem to span both scientific and information visualization. She discusses the future directions in bioinformatics visualization.     

Graphical interface for motion editing using procedure visualization

In this paper, we introduce an interface for motion editing that visualizes editing procedures. For intuitive understanding and construction of motion editing procedures, our system represents an editing element that produces modified motion from a given input motion as a graphical node, and enables user to construct a whole motion editing procedure by connecting the nodes with a few mouse interactions. The system provides both the 3D transform manipulator and the time-line slider for intuitive controlling ...     

Curvature estimation scheme for triangle meshes using biquadratic Bézier patches

When dealing with triangle meshes, it is often important to compute curvature information for the purposes of feature recognition, segmentation, or shape analysis. Since a triangle mesh is a piecewise linear surface, curvature has to be estimated. Several different schemes have been proposed, both discrete and continuous, i.e. based on fitting surfaces locally. This paper compares commonly used discrete and continuous curvature estimation schemes. We also present a novel method which uses biquadratic Bézier patches as a local surface fitting technique.     

3D Graph cut with new edge weights for cerebral white matter segmentation

Accurate and efficient automatic or semi-automatic brain image segmentation methods are of great interest to both scientific and clinical researchers of the human central neural system. Cerebral white matter segmentation in brain Magnetic Resonance Imaging (MRI) data becomes a challenging problem due to a combination of several factors like low contrast, presence of noise and imaging artifacts, partial volume effects, intrinsic tissue variation due to neurodevelopment and neuropathologies, and the highly convoluted geometry of the cortex. In this paper, we propose a new set of edge weights for the traditional graph cut algorithm (Boykov and Jolly, 2001) to correctly segment the cerebral white matter from T1-weighted MRI sequence. In this algorithm, the edge weights of Boykov and Jolly (2001) are modified by comparing the probabilities of an individual voxel and its neighboring voxels to belong to different segmentation classes. A shape prior in form of a series of ellipses is used next to model the contours of the human skull in various 2D slices in the sequence. This shape constraint is imposed to prune the original graph constructed from the input to form a subgraph consisting of voxels within the skull contours. Our graph cut algorithm with new set of edge weights is applied to the above subgraph, thereby increasing the segmentation accuracy as well as decreasing the computation time. Average segmentation errors for the proposed algorithm, the graph cut algorithm (Boykov and Jolly, 2001), and the Expectation Maximization Segmentation (EMS) algorithm Van Leemput et al., 2001 in terms of Dice coefficients are found to be (3.72 ± 1.12)%, (14.88 ± 1.69)%, and (11.95 ± 5.2)%, respectively.     

Hybrid fixed point theory for strictly monotone increasing multi-valued mappings with applications

In this paper, a general hybrid fixed point theorem for the strict monotone increasing multi-valued mappings in ordered Banach spaces is proved via measure of noncompactness and it is further applied to perturbed functional nonconvex differential inclusions for proving the existence results for the extremal solutions under mixed Lipschitz, compactness and strict monotonic conditions.     

Categorical data visualization and clustering using subjective factors

Clustering is an important data mining problem. However, most earlier work on clustering focused on numeric attributes which have a natural ordering to their attribute values. Recently, clustering data with categorical attributes, whose attribute values do not have a natural ordering, has received more attention. A common issue in cluster analysis is that there is no single correct answer to the number of clusters, since cluster analysis involves human subjective judgement. Interactive visualization is one of the methods where users can decide a proper clustering parameters. In this paper, a new clustering approach called CDCS (Categorical Data Clustering with Subjective factors) is introduced, where a visualization tool for clustered categorical data is developed such that the result of adjusting parameters is instantly reflected. The experiment shows that CDCS generates high quality clusters compared to other typical algorithms.     

Hybrid algorithm for generalized mixed equilibrium problems and variational inequality problems and fixed point problems

In this paper, we introduce a new iterative algorithm by hybrid method for finding a common element of the set of solutions of finite general mixed equilibrium problems and the set of solutions of a general variational inequality problem for finite inverse strongly monotone mappings and the set of common fixed points of infinite family of strictly pseudocontractive mappings in a real Hilbert space. Then we prove strong convergence of the scheme to a common element of the three above described sets. Our result improves and extends the corresponding results announced by many others.     

Hybrid methods using genetic algorithms for global optimization

This paper discusses the trade-off between accuracy, reliability and computing time in global optimization. Particular compromises provided by traditional methods (Quasi-Newton and Nelder-Mead's simplex methods) and genetic algorithms are addressed and illustrated by a particular application in the field of nonlinear system identification. Subsequently, new hybrid methods are designed, combining principles from genetic algorithms and "hill-climbing" methods in order to find a better compromise to the trade-off. Inspired by biology and especially by the manner in which living beings adapt themselves to their environment, these hybrid methods involve two interwoven levels of optimization, namely evolution (genetic algorithms) and individual learning (Quasi-Newton), which cooperate in a global process of optimization. One of these hybrid methods appears to join the group of state-of-the-art global optimization methods: it combines the reliability properties of the genetic algorithms with the accuracy of Quasi-Newton method, while requiring a computation time only slightly higher than the latter.     

Conservative occlusion culling for urban visualization using a slice-wise data structure

In this paper, we propose a framework for urban visualization using a conservative from-region visibility algorithm based on occluder shrinking. The visible geometry in a typical urban walkthrough mainly consists of partially visible buildings. Occlusion-culling algorithms, in which the granularity is buildings, process these partially visible buildings as if they are completely visible. To address the problem of partial visibility, we propose a data structure, called slice-wise data structure, that represents buildings in terms of slices parallel to the coordinate axes. We observe that the visible parts of the objects usually have simple shapes. This observation establishes the base for occlusion-culling where the occlusion granularity is individual slices. The proposed slice-wise data structure has minimal storage requirements. We also propose to shrink general 3D occluders in a scene to find volumetric occlusion. Empirical results show that significant increase in frame rates and decrease in the number of processed polygons can be achieved using the proposed slice-wise occlusion-culling as compared to an occlusion-culling method where the granularity is individual buildings.     

Simulation and visualization of the Saint-Venant system using GPUs

We consider three high-resolution schemes for computing shallow-water waves as described by the Saint-Venant system and discuss how to develop highly efficient implementations using graphical processing units (GPUs). The schemes are well-balanced for lake-at-rest problems, handle dry states, and support linear friction models. The first two schemes handle dry states by switching variables in the reconstruction step, so that bilinear reconstructions are computed using physical variables for small water depths and conserved variables elsewhere. In the third scheme, reconstructed slopes are modified in cells containing dry zones to ensure non-negative values at integration points. We discuss how single and double-precision arithmetics affect accuracy and efficiency, scalability and resource utilization for our implementations, and demonstrate that all three schemes map very well to current GPU hardware. We have also implemented direct and close-to-photo-realistic visualization of simulation results on the GPU, giving visual simulations with interactive speeds for reasonably-sized grids.     

Clustering and visualization of bankruptcy trajectory using self-organizing map

Bankruptcy trajectory reflects the dynamic changes of financial situation of companies, and hence make possible to keep track of the evolution of companies and recognize the important trajectory patterns. This study aims at a compact visualization of the complex temporal behaviors in financial statements. We use self-organizing map (SOM) to analyze and visualize the financial situation of companies over several years through a two-step clustering process. Initially, the bankruptcy risk is characterized by a feature self-organizing map (FSOM), and therefore the temporal sequence is converted to the trajectory vector projected on the map. Afterwards, the trajectory self-organizing map (TSOM) clusters the trajectory vectors to a number of trajectory patterns. The proposed approach is applied to a large database of French companies spanning over four years. The experimental results demonstrate the promising functionality of SOM for bankruptcy trajectory clustering and visualization. From the viewpoint of decision support, the method might give experts insight into the patterns of bankrupt and healthy company development.     

Interactive level-of-detail selection using image-based quality metric for large volume visualization

For large volume visualization, an image-based quality metric is difficult to incorporate for level-of-detail selection and rendering without sacrificing the interactivity. This is because it is usually time-consuming to update view-dependent information as well as to adjust to transfer function changes. In this paper, we introduce an image-based level-of-detail selection algorithm for interactive visualization of large volumetric data. The design of our quality metric is based on an efficient way to evaluate the contribution of multiresolution data blocks to the final image. To ensure real-time update of the quality metric and interactive level-of-detail decisions, we propose a summary table scheme in response to runtime transfer function changes and a GPU-based solution for visibility estimation. Experimental results on large scientific and medical data sets demonstrate the effectiveness and efficiency of our algorithm     

Surface processing methods for point sets using finite elements

We present a framework for processing point-based surfaces via partial differential equations (PDEs). Our framework efficiently and effectively brings well-known PDE-based processing techniques to the field of point-based surfaces. At the core of our method is a finite element discretization of PDEs on point surfaces. This discretization is based on the local assembly of PDE-specific mass and stiffness matrices, using a local point coupling computation. Point couplings are computed using a local tangent plane construction and a local Delaunay triangulation of point neighborhoods. The definition of tangent planes relies on moment-based computation with proven scaling and stability properties. Once local stiffness matrices are obtained, we are able to easily assemble global matrices and efficiently solve the corresponding linear systems by standard iterative solvers. We demonstrate our framework by several types of PDE-based surface processing applications, such as segmentation, texture synthesis, bump mapping, and geometric fairing.     

Registration for 3-D point cloud using angular-invariant feature

This paper proposes an angular-invariant feature for 3-D registration procedure to perform reliable selection of point correspondence. The feature is a k-dimensional vector, and each element within the vector is an angle between the normal vector and one of its k nearest neighbors. The angular feature is invariant to scale and rotation transformation, and is applicable for the surface with small curvature. The feature improves the convergence and error without any assumptions about the initial transformation. Besides, no strict sampling strategy is required. Experiments illustrate that the proposed angular-based algorithm is more effective than iterative closest point (ICP) and the curvature-based algorithm.     

Structural shape optimization of shells and folded plates using two-noded finite strips

This paper deals with structural shape optimization of shells and folded plates using two-noded Mindlin-Reissner C(0) finite strips. The whole shape optimization process is carried out by integrating finite strip analysis, cubic spline shape definition, automatic mesh generation, sensitivity analysis and mathematical programming methods in an efficient way. Both thickness and shape variables defining the cross-section of the structure are considered. The objective is to minimize the strain energy with a constraint that the total material volume of the structure remains constant. It is observed that minimization of strain energy leads to optimum structures in which the deflections and stress resultants in the members are considerably reduced. This is illustrated using several examples. The relative contributions of the bending, membrane and shear strain energies are also monitored during the whole optimization process. It is found that most optimal shell and folded plate structures are membrane dominant.     

Online urban object recognition in point clouds using consecutive point information for urban robotic missions

Urban object recognition is the ability to categorize ambient objects into several classes and it plays an important role in various urban robotic missions, such as surveillance, rescue, and SLAM. However, there were several difficulties when previous studies on urban object recognition in point clouds were adopted for robotic missions: offline-batch processing, deterministic results in classification, and necessity of many training examples. The aim of this paper is to propose an urban object recognition algorithm for urban robotic missions with useful properties: online processing, classification results with probabilistic outputs, and training with a few examples based on a generative model. To achieve this, the proposed algorithm utilizes the consecutive point information (CPI) of a 2D LIDAR sensor. This additional information was useful for designing an online algorithm consisting of segmentation and classification. Experimental results show that the proposed algorithm using CPI enhances the applicability of urban object recognition for various urban robotic missions.