基于拉普拉斯特征映射的传递函数设计方法

为降低体绘制中传递函数参数选择的盲目性和设计的复杂性,提出一种基于拉普拉斯特征映射的传递函数设计方法.提取体数据中各种特征信息构建高维传递函数参数空间,通过拉普拉斯特征映射将其映射到保持了体数据局部流形结构和高维参数空间分类能力的二维参数空间,在此嵌入空间上设计一种基于k-means聚类的传递函数,得到了较好的体数据分类和绘制结果.通过在一组体数据上的实验验证了该方法的有效性.     

一种基于交互式多维传递函数的纹理映射体绘制算法

本文设计了一种基于空间信息的交互式多维传递函数的纹理映射体绘制算法。该算法不仅可以根据体数据的强度而且还利用体素的空间位置来设定绘制的颜色和阻光度。通过采用一种独特的空间投影变换,根据用户需求,将体数据划分为不同区域,并分别定义各自的传递函数。该特点使得本文的算法可以有效地对体数据进行交互式分析。在算法实现中,利用了通用图形硬件的可编程特性,在普通PC上可以达到理想的绘制质量和交互速度。     

基于多尺度等值面设计传递函数

在体绘制中传递函数将体数据转换成光学参数,因此体绘制的效果直接由传递函数决定.本文提出了基于多尺度等值面设计传递函数的高效方法.该方法通过梯度阈值提取边界体元来简化数据场,然后将提取等值面的目标函数的计算化简为累加的拉普拉斯加权的直方图极值的计算.最后对直方图进行多尺度平滑,利用提取出的多尺度等值面来设计高斯型传递函数,提高了等值面的准确度和传递函数的设计效率.     

应用RBF神经网络的体绘制传递函数设计方法

提出一种基于RBF神经网络的体绘制多维传递函数设计方法,利用直观的交互界面,通过画笔获得感兴趣体素的特征信息作为训练样本对RBF神经网络进行训练,使用训练后的RBF神经网络实现全部体素的分类识别,对不同的分类结果赋予不同的光学参数进行显示,自动完成传递函数的设计。实验结果表明,所设计的交互界面能直观方便地定义感兴趣的对象,大幅提高人机交互的效率;RBF神经网络的自主学习能力能够避免传递函数设计的盲目性,增强感兴趣区域的绘制效果,实现传递函数设计的自动化和智能化。     

基于K-Means++聚类的体绘制高维传递函数设计方法

如何将体数据中重要的信息高质量地绘制出来是医学可视化急需解决的问题。基于高维直方图的高维传递函数交互设计法是目前流行的方法,但是该方法设计复杂且效果不理想。针对高维特征的传递函数设计问题,提出一个基于改进的K均值(K-Means++)聚类的高维传递函数自动设计与交互式的体绘制方法：首先,对三维数据场进行特征提取;然后,采用基于K-Means++聚类的传递函数自动生成方法;最后,提供便捷的交互式界面给用户进行调整。还利用基于图形处理器(GPU)的体绘制方法,充分利用图形卡的强大并行计算能力,达到实时绘制的效果。实验结果表明,该方法能消除高维传递函数设计的复杂性,并且能有效地融合多种人体组织结构特征,提高渲染效果。     

面向台风数据体绘制的传输函数研究

在直接体绘制中,传输函数定义了从数据属性到光学属性的映射关系,直接决定了绘制的效果,是体绘制研究的关键技术之一。为了使体绘制技术在气象领域真正发挥作用,必须结合气象规律研究和设计面向气象数据体绘制的传输函数。研究台风数据的特点以及针对台风数据体绘制的传输函数的设计流程,在数据值梯度直方图基础上结合光照参数设计了台风标量场数据的传输函数,采用台风的温度、湿度以及云量等数据进行体绘制,更好地显示了台风的内部结构,对于认识台风起到了促进作用。     

基于体素可见度的高效直观传递函数设计算法

针对体绘制中传递函数设计问题,提出基于直观体素可见度设计传递函数的高效方法。用高斯混合模型（GMM）对体数据自动建模,提取体数据的聚类信息;根据高斯混合模型设计体素可见度模型。将传递函数设计转化为真实体素可见度与目标体素可见度距离最小化问题,使用最速下降法求解,得到最优传递函数。同时设计一套方便、灵活的交互工具,用来调节体素可见度模型。实验结果表明了该算法的高效性和直观性。     

基于K均值聚类的传输函数设计方法

在直接体绘制中,传输函数定义了从体数据属性到光学属性的映射关系,直接决定了体绘制的效果,是体绘制研究的关键技术之一。传统的多维传输函数是基于体数据和梯度体数据进行设计的,本文提出基于K均值聚类的传输函数设计方法,并在此基础上进行气象数据的分类方法研究,实现基于GPU的气象台风数据的直接体绘制。     

基于K均值聚类的体绘制传递函数设计方法研究

提出一种基于K均值聚类算法的体绘制多维传递函数设计方法。在利用灰度—梯度直方图分析体数据内部结构信息的基础上,应用K均值聚类算法对整个体数据进行聚类分类,对属于不同聚类中的体素值进行伪彩色映射,实现体数据与彩色编码的转换关系。实验表明,利用该方法所设计的体绘制传递函数能够揭示体数据的内部结构关系,重建的三维图像逼真、质量高。     

基于平行坐标主维度的多变量传递函数设计方法

为解决多变量体数据高效绘制时操作复杂、不易分析和表达的问题,将平行坐标与交互操作应用到传递函数设计中,并通过引入平行坐标主维度概念提出一种平行坐标与传递函数曲线相结合的设计方法.针对目前基于平行坐标的高维传递函数设计方法不灵活、调节烦琐的问题改进强度梯度直方图,并提取主维度数据特征;实现平行坐标自适应范围选择并进行指导性范围调节;把满足平行坐标各维度条件的采样点空间位置映射至主维度,提取数据范围实时生成传递函数曲线.实验结果表明,该方法能有效地降低高维传递函数设计的复杂度,提高传递函数调节灵活度,在实现实时交互的同时还可有效地增强绘制效果.     

The transfer function bake-off

Direct volume rendering is a key technology for visualizing large 3D data sets from scientific or medical applications. Transfer functions are particularly important to the quality of direct volume-rendered images. A transfer function assigns optical properties, such as color and opacity, to original values of the data set being visualized. Unfortunately, finding good transfer functions proves difficult. It is one of the major problems in volume visualization. The article examines four of the currently most promising approaches to transfer function design. The four approaches are: trial and error, with minimum computer aid; data-centric, with no underlying assumed model; data-centric, using an underlying data model; and image-centric, using organized sampling     

Rule‐Enhanced Transfer Function Generation for Medical Volume Visualization

In volume visualization, transfer functions are used to classify the volumetric data and assign optical properties to the voxels. In general, transfer functions are generated in a transfer function space, which is the feature space constructed by data values and properties derived from the data. If volumetric objects have the same or overlapping data values, it would be difficult to separate them in the transfer function space. In this paper, we present a rule‐enhanced transfer function design method that allows important structures of the volume to be more effectively separated and highlighted. We define a set of rules based on the local frequency distribution of volume attributes. A rule‐selection method based on a genetic algorithm is proposed to learn the set of rules that can distinguish the user‐specified target tissue from other tissues. In the rendering stage, voxels satisfying these rules are rendered with higher opacities in order to highlight the target tissue. The proposed method was tested on various volumetric datasets to enhance the visualization of important structures that are difficult to be visualized by traditional transfer function design methods. The results demonstrate the effectiveness of the proposed method.     

On the estimation of transfer functions

This paper treats the close conceptual relationships between basic approaches to the estimation of transfer functions of linear systems. The classical methods of frequency and spectral analysis are shown to be related to the well-known time domain methods of prediction error type via a common “empirical transfer function estimate”. Asymptotic properties of the estimates obtained by the respective methods are also described and discussed. An important feature that is displayed by this treatment is a frequency domain weighting function that determines the distribution of bias in case the true system cannot be exactly described within the chosen model set. The choice of this weighting function is made in terms of noise models for time-domain methods. The noise model thus has a dual character from the system approximation point of view.     

A framework for generating tunable test functions for multimodal optimization

Multimodal function optimization, where the aim is to locate more than one solution, has attracted growing interest especially in the evolutionary computing research community. To evaluate experimentally the strengths and weaknesses of multimodal optimization algorithms, it is important to use test functions representing different characteristics and various levels of difficulty. The available selection of multimodal test problems is, however, rather limited and no general framework exists. This paper describes an attempt to construct a software framework which includes a variety of easily tunable test functions. The aim is to provide a general and easily expandable environment for testing different methods of multimodal optimization. Several function families with different characteristics are included. The framework implements new parameterizable function families for generating desired landscapes. Additionally the framework implements a selection of well known test functions from the literature, which can be rotated and stretched. The software module can easily be imported to any optimization algorithm implementation compatible with the C programming language. As an application example, 8 optimization approaches are compared by their ability to locate several global optima over a set of 16 functions with different properties generated by the proposed module. The effects of function regularity, dimensionality and number of local optima on the performance of different algorithms are studied.     

应用于传递函数设定的交互式体绘制工具

传递函数是体绘制过程中用以定出体数据与光学特征的对应关系，因此，传递函数的设定对成像质量有着直接的影响，文章提出一应用于传递函数设定、简单且有效的交互式体绘制工具，由于二维纹理硬件在通用的个人计算机上被普遍使用，因而该工具采用基于二维纹理硬件的体绘制方法，利用本工具，用户能根据体数据的直方图来交互地分别设定R、G、B和A四种传递函数，以定出体数据与光学特征的对应关系，并获得实时的反馈视觉信息(绘制结果)，该工具亦提供一虚拟轨迹球让用户交互地改变观察体数据的视点，用户不但可以交互地控制放大或缩小比率来绘制体数据，还可以选择采用光照或由多重纹理实现的三线性插值来获得不同的绘制效果，该文描述开发此工具的各种技术，并给出利用此工具得到的一些绘制结果。     

Dimensionality Reduction on Multi-Dimensional Transfer Functions for Multi-Channel Volume Data Sets

The design of transfer functions for volume rendering is a non-trivial task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel, which requires multi-dimensional transfer functions. In this paper, we propose a new method for multi-dimensional transfer function design. Our new method provides a framework to combine multiple computational approaches and pushes the boundary of gradient-based multi-dimensional transfer functions to multiple channels, while keeping the dimensionality of transfer functions at a manageable level, i.e., a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. Applying recently developed nonlinear dimensionality reduction algorithms reduces the high-dimensional data of the domain. In this paper, we use Isomap and Locally Linear Embedding as well as a traditional algorithm, Principle Component Analysis. Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. We demonstrate the effectiveness of our new dimensionality reduction algorithms with two volumetric confocal microscopy data sets.     

Texture-based transfer functions for direct volume rendering

Visualization of volumetric data faces the difficult task of finding effective parameters for the transfer functions. Those parameters can determine the effectiveness and accuracy of the visualization. Frequently, volumetric data includes multiple structures and features that need to be differentiated. However, if those features have the same intensity and gradient values, existing transfer functions are limited at effectively illustrating those similar features with different rendering properties. We introduce texture-based transfer functions for direct volume rendering. In our approach, the voxel's resulting opacity and color are based on local textural properties rather than individual intensity values. For example, if the intensity values of the vessels are similar to those on the boundary of the lungs, our texture-based transfer function will analyze the textural properties in those regions and color them differently even though they have the same intensity values in the volume. The use of texture-based transfer functions has several benefits. First, structures and features with the same intensity and gradient values can be automatically visualized with different rendering properties. Second, segmentation or prior knowledge of the specific features within the volume is not required for classifying these features differently. Third, textural metrics can be combined and/or maximized to capture and better differentiate similar structures. We demonstrate our texture-based transfer function for direct volume rendering with synthetic and real-world medical data to show the strength of our technique.     

Crop type classification using a combination of optical and radar remote sensing data: a review

Reliable and accurate crop classification maps are an important data source for agricultural monitoring and food security assessment studies. For many years, crop type classification and monitoring were focused on single-source optical satellite data classification. With advancements in sensor technologies and processing capabilities, the potential of multi-source satellite imagery has gained increasing attention. The combination of optical and radar data is particularly promising in the context of crop type classification as it allows explaining the advantages of both sensor types with respect to e.g. vegetation structure and biochemical properties. This review article gives a comprehensive overview of studies on crop type classification using optical and radar data fusion approaches. A structured review of fusion approaches, classification strategies and potential for mapping specific crop types is provided. Finally, the partially untapped potential of radar-optical fusion approaches, research gaps and challenges for upcoming future studies are highlighted and discussed.     

High-level user interfaces for transfer function design with semantics

Many sophisticated techniques for the visualization of volumetric data such as medical data have been published. While existing techniques are mature from a technical point of view, managing the complexity of visual parameters is still difficult for non-expert users. To this end, this paper presents new ideas to facilitate the specification of optical properties for direct volume rendering. We introduce an additional level of abstraction for parametric models of transfer functions. The proposed framework allows visualization experts to design high-level transfer function models which can intuitively be used by non-expert users. The results are user interfaces which provide semantic information for specialized visualization problems. The proposed method is based on principal component analysis as well as on concepts borrowed from computer animation.