A parallel coordinates style interface for exploratory volume visualization

We present a user interface, based on parallel coordinates, that facilitates exploration of volume data. By explicitly representing the visualization parameter space, the interface provides an overview of rendering options and enables users to easily explore different parameters. Rendered images are stored in an integrated history bar that facilitates backtracking to previous visualization options. Initial usability testing showed clear agreement between users and experts of various backgrounds (usability, graphic design, volume visualization, and medical physics) that the proposed user interface is a valuable data exploration tool.     

Interactive visibility retargeting in VR using conformal visualization

In Virtual Reality, immersive systems such as the CAVE provide an important tool for the collaborative exploration of large 3D data. Unlike head-mounted displays, these systems are often only partially immersive due to space, access, or cost constraints. The resulting loss of visual information becomes a major obstacle for critical tasks that need to utilize the users' entire field of vision. We have developed a conformal visualization technique that establishes a conformal mapping between the full 360° field of view and the display geometry of a given visualization system. The mapping is provably angle-preserving and has the desirable property of preserving shapes locally, which is important for identifying shape-based features in the visual data. We apply the conformal visualization to both forward and backward rendering pipelines in a variety of retargeting scenarios, including CAVEs and angled arrangements of flat panel displays. In contrast to image-based retargeting approaches, our technique constructs accurate stereoscopic images that are free of resampling artifacts. Our user study shows that on the visual polyp detection task in Immersive Virtual Colonoscopy, conformal visualization leads to improved sensitivity at comparable examination times against the traditional rendering approach. We also develop a novel user interface based on the interactive recreation of the conformal mapping and the real-time regeneration of the view direction correspondence.     

Visualization exploration and encapsulation via a spreadsheet-likeinterface

Exploring complex, very large data sets requires interfaces to present and navigate through the visualization of the data. Two types of audience benefit from such coherent organization and representation: first, the user of the visualization system can examine and evaluate their data more efficiently; second, collaborators or reviewers can quickly understand and extend the visualization. The needs of these two groups are addressed by the spreadsheet-like interface described in this paper. The interface represents a 2D window in a multidimensional visualization parameter space. Data is explored by navigating this space via the interface. The visualization space is presented to the user in a manner that clearly identifies which parameters correspond to which visualized result. Operations defined on this space can be applied which generate new parameters or results. Combined with a general-purpose interpreter, these functions can be utilized to quickly extract desired results. Finally, by encapsulating the visualization process, redundant exploration is eliminated and collaboration is facilitated. The efficacy of this novel interface is demonstrated through examples using a variety of data sets in different domains     

智能可视化与可视分析

可视化与可视分析已成为众多领域中结合人类智能与机器智能协同理解、分析数据的常见手段。人工智能可以通过对大数据的学习分析提高数据质量,捕捉关键信息,并选取最有效的视觉呈现方式,从而使用户更快、更准确、更全面地从可视化中理解数据。利用人工智能方法,交互式可视化系统也能更好地学习用户习惯及用户意图,推荐符合用户需求的可视化形式、交互操作和数据特征,从而降低用户探索的学习及时间成本,提高交互分析的效率。人工智能方法在可视化中的应用受到了极大关注,产生了大量学术成果。本文从最新工作出发,探讨人工智能在可视化流程的关键步骤中的作用。包括如何智能地表示和管理数据、如何辅助用户快速创建和定制可视化、如何通过人工智能扩展交互手段及提高交互效率、如何借助人工智能辅助数据的交互分析等。具体而言,本文详细梳理每个步骤中需要完成的任务及解决思路,介绍相应的人工智能方法（如深度网络结构）,并以图表数据为例介绍智能可视化与可视分析的应用,最后讨论智能可视化方法的发展趋势,展望未来的研究方向及应用场景。     

LOD map--A visual interface for navigating multiresolution volume visualization

In multiresolution volume visualization, a visual representation of level-of-detail (LOD) quality is important for us to examine, compare, and validate different LOD selection algorithms. While traditional methods rely on ultimate images for quality measurement, we introduce the LOD map--an alternative representation of LOD quality and a visual interface for navigating multiresolution data exploration. Our measure for LOD quality is based on the formulation of entropy from information theory. The measure takes into account the distortion and contribution of multiresolution data blocks. A LOD map is generated through the mapping of key LOD ingredients to a treemap representation. The ordered treemap layout is used for relative stable update of the LOD map when the view or LOD changes. This visual interface not only indicates the quality of LODs in an intuitive way, but also provides immediate suggestions for possible LOD improvement through visually-striking features. It also allows us to compare different views and perform rendering budget control. A set of interactive techniques is proposed to make the LOD adjustment a simple and easy task. We demonstrate the effectiveness and efficiency of our approach on large scientific and medical data sets.     

A practical approach to spectral volume rendering

To make a spectral representation of color practicable for volume rendering, a new low-dimensional subspace method is used to act as the carrier of spectral information. With that model, spectral light material interaction can be integrated into existing volume rendering methods at almost no penalty. In addition, slow rendering methods can profit from the new technique of postillumination-generating spectral images in real-time for arbitrary light spectra under a fixed viewpoint. Thus, the capability of spectral rendering to create distinct impressions of a scene under different lighting conditions is established as a method of real-time interaction. Although we use an achromatic opacity in our rendering, we show how spectral rendering permits different data set features to be emphasized or hidden as long as they have not been entirely obscured. The use of postillumination is an order of magnitude faster than changing the transfer function and repeating the projection step. To put the user in control of the spectral visualization, we devise a new widget, a "light-dial", for interactively changing the illumination and include a usability study of this new light space exploration tool. Applied to spectral transfer functions, different lights bring out or hide specific qualities of the data. In conjunction with postillumination, this provides a new means for preparing data for visualization and forms a new degree of freedom for guided exploration of volumetric data sets     

Relation-aware volume exploration pipeline

Volume exploration is an important issue in scientific visualization. Research on volume exploration has been focused on revealing hidden structures in volumetric data. While the information of individual structures or features is useful in practice, spatial relations between structures are also important in many applications and can provide further insights into the data. In this paper, we systematically study the extraction, representation, exploration, and visualization of spatial relations in volumetric data and propose a novel relation-aware visualization pipeline for volume exploration. In our pipeline, various relations in the volume are first defined and measured using region connection calculus (RCC) and then represented using a graph interface called relation graph. With RCC and the relation graph, relation query and interactive exploration can be conducted in a comprehensive and intuitive way. The visualization process is further assisted with relation-revealing viewpoint selection and color and opacity enhancement. We also introduce a quality assessment scheme which evaluates the perception of spatial relations in the rendered images. Experiments on various datasets demonstrate the practical use of our system in exploratory visualization.     

可视化数据挖掘技术

迄今为止,数据挖掘与知识发现软件的功能不再停留在"挖掘"这个单一功能的实现,而已延伸到数据挖掘与知识发现的过程,即包括数据的预处理、数据挖掘、模型评估与可视化,在单纯的模型可视化基础上扩充了数据可视化与数据挖掘过程可视化.主要讨论了数据挖掘的方法与可视化技术,指出了未来的研究方向.     

Image-based streamline generation and rendering

Seeding streamlines in 3D flow fields without considering their projections in screen space can produce visually cluttered rendering results. Streamlines will overlap or intersect with each other in the output image, which makes it difficult for the user to perceive the underlying flow structure. This paper presents a method to control the seeding and generation of streamlines in image space to avoid visual cluttering and allow a more flexible exploration of flow fields. In our algorithm, 2D images with depth maps generated by a variety of visualization techniques can be used as input from which seeds are placed and streamlines are generated. The density and rendering styles of streamlines can be flexibly controlled based on various criteria to improve visual clarity. With our image space approach, it is straightforward to implement the level of detail rendering, depth peeling, and stylized rendering of streamlines to allow for more effective visualization of 3D flow fields.     

Interactive, internet delivery of visualization via structured prerendered multiresolution imagery

We present a novel approach for latency-tolerant delivery of visualization and rendering results where client-side frame rate display performance is independent of source dataset size, image size, visualization technique or rendering complexity. Our approach delivers pre-rendered, multiresolution images to a remote user as they navigate through different viewpoints, visualization or rendering parameters. We employ demand-driven tiled, multiresolution image streaming and prefetching to efficiently utilize available bandwidth while providing the maximum resolution user can perceive from a given viewpoint. Since image data is the only input to our system, our approach is generally applicable to all visualization and graphics rendering applications capable of generating image files in an ordered fashion. In our implementation, a normal web server provides on-demand images to a remote custom client application, which uses client-pull to obtain and cache only those images required to fulfill the interaction needs. The main contributions of this work are: (1) an architecture for latency-tolerant, remote delivery of precomputed imagery suitable for use with any visualization or rendering application capable of producing images in an ordered fashion; (2) a performance study showing the impact of diverse network environments and different tunable system parameters on end-to-end system performance in terms of deliverable frames per second.     

Information visualization and visual data mining

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section     

Applying Visual Analytics to Physically Based Rendering

Physically based rendering is a well‐understood technique to produce realistic‐looking images. However, different algorithms exist for efficiency reasons, which work well in certain cases but fail or produce rendering artefacts in others. Few tools allow a user to gain insight into the algorithmic processes. In this work, we present such a tool, which combines techniques from information visualization and visual analytics with physically based rendering. It consists of an interactive parallel coordinates plot, with a built‐in sampling‐based data reduction technique to visualize the attributes associated with each light sample. Two‐dimensional (2D) and three‐dimensional (3D) heat maps depict any desired property of the rendering process. An interactively rendered 3D view of the scene displays animated light paths based on the user's selection to gain further insight into the rendering process. The provided interactivity enables the user to guide the rendering process for more efficiency. To show its usefulness, we present several applications based on our tool. This includes differential light transport visualization to optimize light setup in a scene, finding the causes of and resolving rendering artefacts, such as fireflies, as well as a path length contribution histogram to evaluate the efficiency of different Monte Carlo estimators.     

Advanced interactive medical visualization on the GPU

Interactive visual analysis of a patient’s anatomy by means of computer-generated 3D imagery is crucial for diagnosis, pre-operative planning, and surgical training. The task of visualization is no longer limited to producing images at interactive rates, but also includes the guided extraction of significant features to assist the user in the data exploration process. An effective visualization module has to perform a problem-specific abstraction of the dataset, leading to a more compact and hence more efficient visual representation. Moreover, many medical applications, such as surgical training simulators and pre-operative planning for plastic and reconstructive surgery, require the visualization of datasets that are dynamically modified or even generated by a physics-based simulation engine.     

体绘制技术在医学可视化中的新发展

科学计算可视化体绘制算法能反映出体数据的内部信息，在医学，它已经从辅诊断发展成为辅助治疗的重要手段，体可视化技术是医学可视化的重要研究内容，其处理过程包括体数据的获取，模型的建立，数据的映射，绘制等操作，该文介绍了医学可视化中常使用的几种光照模型，针对基于图象空间和对象空间两种体绘制算法，介绍了它们的基本思想方法，并详细阐述了在近期的主要加速技术和提高图象质量方法的新进展，最后给出了实验数据和结论。     

A kernel-based framework for image collection exploration

While search engines have been a successful tool to search text information, image search systems still face challenges. The keyword-based query paradigm used to search in image collection systems, which has been successful in text retrieval, may not be useful in scenarios where the user does not have the precise way to express a visual query. Image collection exploration is a new paradigm where users interact with the image collection to discover useful and relevant pictures. This paper proposes a framework for the construction of an image collection exploration system based on kernel methods, which offers a mathematically strong basis to address each stage of an image collection exploration system: image representation, summarization, visualization and interaction. In particular, our approach emphasizes a semantic representation of images using kernel functions, which can be seamlessly harnessed across all system components. Experiments were conducted with real users to verify the effectiveness and efficiency of the proposed strategy.     

From visual data exploration to visual data mining: a survey

We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.     

Creative parameter selection for volume visualization

Most of the available algorithms for scalar volume visualization offer predefined techniques such as display of volumetric regions defined by scalar threshold values. The regions can usually be drawn opaque or transparent or appear in combinations. This paper presents an implementation of a volume visualization concept where several modelling and rendering techniques can be applied in any combination, mainly bounded by the creativity of the user. The concept is based on the use of a model for light scattering in a field of varying density emitters, and the use of fixed visual references to improve readability and disambiguate interpretation. An image is computed by means of an interval-based mapping from scalar range to visual parameters. Each interval has a set of associated parameters, such as colour and attenuation. In addition, each interval of the scalar range must be mapped into relative density by means of a transfer function which is selected in a ‘natural way’ depending on application. A methodology is suggested which enable the piecewise transfer functions to be easily determined. A prototype user-interface for the mapping from scalar values to visual parameters is demonstrated. The interface is easy to use for the beginner, while it also encourages creativity and intuition in the process of selecting parameters.     

Research issues in model-based visualization of complex data sets

At the most abstract level, data visualization maps discrete values computed over an n-dimensional domain onto pixel colors. It is largely a dimension-reducing process justified by its leverage on human perceptual capacities for extracting information from visual stimuli. The difficulty is to implement a mapping that reveals the data characteristics relevant to the application at hand. Effective visualization solutions let the user control the process parameters interactively and enhance the automatically extracted features. We argue for an intelligent, model-based approach to visualization, which extracts the intrinsic data characteristics and constructs multiresolution graphics models suitable for interactive rendering on commercially available hardware adapters. The model-based approach has four parts, which we summarize     

基于用户模型匹配的水情信息可视化研究与实现

水利信息化平台发展迅速,但水情信息具有来源广泛、类型丰富的特点,用户难以在多而复杂的水情信息中快速找到自己想要的水情信息。通过水情信息可视化的方法,将水情数据转化为用户可以直观理解的视觉形式,定制个性化的地图、设计不同用户界面风格。为解决大量水情信息可视化产生的视觉混淆问题,采用基于用户模型匹配的推荐算法分别对水情站点进行推荐、实现水情信息的过滤,对推荐站点重点可视化。同时,为了增强个性化展示的效果,还采用基于概率的用户模型匹配算法为不同的用户匹配个性化水情信息可视化界面。