{"title":"Volume Rendering Using Principal Component Analysis","authors":"Salaheddin Alakkari, J. Dingliana","doi":"10.2312/eurp.20161148","DOIUrl":"https://doi.org/10.2312/eurp.20161148","url":null,"abstract":"We investigate the use of Principal Component Analysis (PCA) for image-based volume rendering. We compute an eigenspace using training images, pre-rendered using a standard raycaster, from a spherically distributed range of camera positions. Our system is then able to synthesize novel views of the data set with minimal computation at run time. Results indicate that PCA is able to sufficiently learn the full volumetric model through a finite number of training images and generalizer of training images and generalize the computed eigenspace to produce high quality novel view images.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124084069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visual Analysis of Text Annotations for Stance Classification with ALVA","authors":"K. Kucher, A. Kerren, C. Paradis, Magnus Sahlgren","doi":"10.2312/eurp.20161139","DOIUrl":"https://doi.org/10.2312/eurp.20161139","url":null,"abstract":"The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers' feelings and attitudes towards their own and other people's utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided with a visualization of stance annotations which facilitates the analysis of categories used by the annotators. ALVA is already being used by our domain experts in linguistics and computational linguistics in order to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126414405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reem Hourieh, Holger Stitz, N. Gehlenborg, M. Streit
{"title":"TaCo: Comparative Visualization of Large Tabular Data","authors":"Reem Hourieh, Holger Stitz, N. Gehlenborg, M. Streit","doi":"10.2312/eurp.20161149","DOIUrl":"https://doi.org/10.2312/eurp.20161149","url":null,"abstract":"Tabular data plays a vital role in many different domains. In the course of a project, changes to the structure and content of tables can result in multiple instances of a table. A challenging task when working with such derived tables is to understand what exactly has changed from one version to another. Traditional comparison tools assist users in inspecting differences between multiple table instances, however, the resulting visualizations are often hard to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TaCo, an interactive comparison tool that effectively visualizes the differences between multiple tables at various levels of granularity: (1) the aggregated differences between all table instances, (2) the differences between one table compared to all others, and (3) the detailed differences between two instances.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124381826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"When Individual Data Points Matter: Interactively Analysing Classification Landscapes","authors":"Bruno Schneider, S. Mittelstädt, D. Keim","doi":"10.2312/eurp.20161130","DOIUrl":"https://doi.org/10.2312/eurp.20161130","url":null,"abstract":"The selection of classification models among several options with similar accuracy cannot be done through purely automated methods, and especially in scenarios in which the cost of misclassified instances is crucial, such as criminal intelligence analysis. To tackle this problem and illustrate our ideas, we developed a prototype for the visualization and comparison of classification landscapes. In our system, the same data is given to different classification models. Classification landscapes are shown in the scatter plots, together with their geographical location on a map and detailed textual description for each data record. To enhance model comparison, we implemented interactive anchor-points selection in classification landscapes. Using those anchors, the user can manipulate and reproject the model results in order to get more comparable classification landscapes. We provided a use case with crime data, for crime intelligence analysis.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129181338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visualization-Aware Color Design","authors":"D. Szafir, Michael Gleicher","doi":"10.2312/eurp.20161151","DOIUrl":"https://doi.org/10.2312/eurp.20161151","url":null,"abstract":"Color encoding design currently focuses on the colors themselves: visualization designers choose sets of colors that work well in isolation. However, the effectiveness of a color encoding depends on properties of the visualization it is used for, such as the size or shape of marks. We argue for a new way of thinking about color design in visualizations: designers should choose colors based on a given context rather than in isolation. We identify three categories of design constraints that contribute to the effective color choices in visualization: aesthetic constraints, perceptual constraints, and functional constraints. The conceptual framework formed by these constraints helps designers optimize color choices based on known properties of a given visualization. In this poster, we discuss this framework in detail and illustrate how it informs more effective visualization design.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128418499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Space Bundling for Continuous Parallel Coordinates","authors":"G. Palmas, T. Weinkauf","doi":"10.2312/eurovisshort.20161162","DOIUrl":"https://doi.org/10.2312/eurovisshort.20161162","url":null,"abstract":"Continuous Parallel Coordinates (CPC) are a visualization technique used to perform multivariate analysis of different scalar fields defined on the same domain. While classic Parallel Coordinates draws a line for each sample point, a CPC visualization uses a density-based representation. An interesting possibility for the classic method is to highlight higher-dimensional clusters using edge bundling, where each line becomes a spline bent towards the centroid of the cluster. This often leads to expressive, illustrative visualizations. Unfortunately, bundling lines is not possible for CPC, as they are not involved in this method. In this paper, we propose a deformation of the visualization space for Continuous Parallel Coordinates that leads to similar results as those obtained through classic edge bundling. We achieve this by performing a curved-profile transformation in image space. The approach lends itself to a computationally lightweight GPU implementation. Furthermore, we provide intuitive interactions with the bundled clusters. We show several examples of our technique applied to a commonly available data set.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128756328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Classic Techniques in New Domains: An Alternative Recipe","authors":"M. Monroe","doi":"10.2312/eurovisshort.20161172","DOIUrl":"https://doi.org/10.2312/eurovisshort.20161172","url":null,"abstract":"In this paper, we adapt the classic technique of depicting a process as a structured workflow to suit the standard recipe. Cooking can be thought of as a small data, big user task. A single recipe encompasses only a small amount of information, but is utilized across a large user base. Our goal was to understand and measure the benefits of tailoring the presentation of a recipe to suit a specific faction of users. As such, our more technical rendering was paired with a technically proficient user base, resulting in dramatic gains in both the speed and accuracy with which the information was interpreted. These benefits serve to motivate our continued work towards automatically translating recipes into a structured data format that can be easily reconfigured into this and other representations of the information to enable a more customized experience across a large and varied user base.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127788255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using Icicle Trees to Encode the Hierarchical Structure of Source Code","authors":"Ivan Bacher, Brian Mac Namee, John D. Kelleher","doi":"10.2312/eurovisshort.20161168","DOIUrl":"https://doi.org/10.2312/eurovisshort.20161168","url":null,"abstract":"This paper presents a study which evaluates the use of a tree visualisation (icicle tree) to encode the hierarchical structure of source code. The tree visualisation was combined with a source code editor in order to function as a compact overview to facilitate the process of comprehending the global structure of a source code document. Results from our study show that providing an overview visualisation led to an increase in accuracy and a decrease in completion time when participants performed counting tasks. However, in locating tasks, the presence of the visualisation led to a decrease in participants' performance.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114660643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Interactive Exploration of Student Generated Content presented in Blogs","authors":"Ilir Jusufi, M. Milrad, Xurxo Legaspi","doi":"10.2312/eurp.20161140","DOIUrl":"https://doi.org/10.2312/eurp.20161140","url":null,"abstract":"Nowadays blogs are regarded as tools for communication as well as an important source for spreading information in almost every subject. In recent years, school teachers have started to take advantage of this technology in order to support their educational practices. In this paper we focus on the data generated by a project involving more than 50 Swedish schools where teachers and pupils are posting content related to their astronomy class activities in their blogs with the aims of improving the teaching process. The challenge here is to find suitable methods to explore all these blogs in an interactive and discovery fashion. Our proposed solution to this challenge is to provide a visual and interactive tool for the exploration of blog corpora by teachers, pupils, project managers and parents.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128228168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xin Tong, Huijie Zhang, C. Jacobsen, Han-Wei Shen, P. McCormick
{"title":"Crystal Glyph: Visualization of Directional Distributions Based on the Cube Map","authors":"Xin Tong, Huijie Zhang, C. Jacobsen, Han-Wei Shen, P. McCormick","doi":"10.2312/eurovisshort.20161154","DOIUrl":"https://doi.org/10.2312/eurovisshort.20161154","url":null,"abstract":"High resolution simulations are capable of generating very large vector fields that are expensive to store and analyze. In addition, the velocity fields generated from some particle simulations are not stored on spatial grids, which become difficult to visualize using some traditional vector field visualization methods such as streamlines. Furthermore, the noise and/or uncertainty contained in the data often affects the quality of visualization by producing visual clutter that interferes with both the interpretation and identification of important features. An alternative approach is to store the distribution of many vector orientations and visualize the distribution with 3D glyphs. This paper presents the cube map histogram, a new data structure for storing the distribution of three-dimensional vector directions. We also present a glyph called the crystal glyph that effectively visualizes the directional distribution using OpenGL cube map textures. By placing crystal glyphs in the 3D data space, users can identify the directional distribution of the regional vector field from the shape and color of the glyph without visual clutter.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123827194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}