<\/p>\n
Beginner\/Intermediate<\/p>\n
Chaoli Wang, University of California, Davis
\nHan-Wei Shen, The Ohio State University
\nKlaus Mueller, Stony Brook University
\nHuamin Qu, Hong Kong University of Science and Technology<\/p>\n
Data visualization is an iterative and exploratory process, which involves choices of parameters for queries of different types. Examples of visualization parameters include level-of-detail, color and opacity transfer function, camera position and path, lighting and so on. To reveal the important aspects of data, the users often have to go through a lengthy and expensive process to obtain a large ensemble of visualization results. With the ever-increasing size of volume data, manual data browsing through the immense, high-dimensional parameter space is no longer a viable solution. Efficient and effective solutions that search and narrow down the parameter space for assisting the users in their decision making become imperative.<\/p>\n
In this tutorial, we introduce recent advances and emerging techniques in volume visualization towards perception-driven data analysis, rendering and presentation. The fundamental visual perception and cognitive principles are incorporated into the visualization process, thus enable presentation of relevant information for gleaning insights from the data. Selective topics include perception-informed color and highlighting, saliency-aware rendering techniques, perception-guided transfer function specification, quality enhancement of direct volume rendered images, view selection for three-dimensional and time-varying volume visualization, level-of-detail (LOD) selection for multiresolution visualization, and multiscale volume data quality assessment. The tutorial covers principles and practice of perception and cognition (such as color perception), mathematics and statistics (such as entropy theory, frequency-domain foundation, and conjoint analysis), as well as user study and evaluation. The tutorial also demonstrates the applications of those principles in visualization. The goal of this tutorial is to inform visualization researchers and practitioners the state-of-the-art technologies that leverage human perception for effective visualization of large volume data.<\/p>\n
ALL Material [URL<\/a>] Perception-Driven Techniques for Effective Visualization of Large Volume Data Columbus, OH, 19 Oct 2008 Level Beginner\/Intermediate Organizers Chaoli Wang, University of California, Davis Han-Wei Shen, The Ohio State University Klaus Mueller, Stony Brook University Huamin Qu, Hong Kong University of Science and Technology Abstract Data visualization is an iterative and exploratory process, which involves choices […]<\/p>\n","protected":false},"author":2576,"featured_media":0,"parent":101,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-362","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.nd.edu\/chaoli-wang\/wp-json\/wp\/v2\/pages\/362","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.nd.edu\/chaoli-wang\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.nd.edu\/chaoli-wang\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/chaoli-wang\/wp-json\/wp\/v2\/users\/2576"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/chaoli-wang\/wp-json\/wp\/v2\/comments?post=362"}],"version-history":[{"count":8,"href":"https:\/\/sites.nd.edu\/chaoli-wang\/wp-json\/wp\/v2\/pages\/362\/revisions"}],"predecessor-version":[{"id":2084,"href":"https:\/\/sites.nd.edu\/chaoli-wang\/wp-json\/wp\/v2\/pages\/362\/revisions\/2084"}],"up":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/chaoli-wang\/wp-json\/wp\/v2\/pages\/101"}],"wp:attachment":[{"href":"https:\/\/sites.nd.edu\/chaoli-wang\/wp-json\/wp\/v2\/media?parent=362"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nSo Many Parameters, So Little Time: Guiding Users To Obtain Better Visualizations [URL<\/a>]
\nPerception-Based Transfer Function Design [URL<\/a>]
\nInformation and Visualization [URL<\/a>]
\nPerception-Driven Techniques for Large Volume Data Analysis and Visualization [URL<\/a>]<\/p>\n","protected":false},"excerpt":{"rendered":"