Research On Visualization Of Time-Varying Volume Data

Scientific Visualization 15 an imPortant assistant tool of scientific research.It helPsscientists to observe and understand the essence and meaning of scientific data.Scientific ComPuting Produces data that 15 vast in sPatial,temPoral,and variabledomains.As a hot toPic,visualizaing time一varying volume data can visualizetime一varying Phenomena and ensure correct interPretation and analysis,Provokeinsights.In volume data visualization,the design oftransfer functions 15 a key Process.Wedid lots of research work on transfer function.In addition,there also has much researchwork on fearure extraction and tracking.What we have accomPlished are:1 .One transfer function that 15 suitable for a data set usually dose not suit others.50 it 15 difficult and time一consuming for users to design new ProPer transfer functionwhen the tyPes ofthe studied data sets are changed.By introducing neural networks intothe transfer function design,a general adaPtive transfer function(GATF)15 ProPosed inthis PaPer.EXPerimental results showed that by using neural networks to guide thetransfer function design,the robustness of volume rendering 15 Promoted and thecorresPonding classification Process 15 oPtimized.2 .Feattlre analysis and visualizing 15 crucial to helP interPret all the information.Inthis PaPer,we Present a technique which isolates and tracks full volume rePresentationsof regions of interest from 3D datasets.Features are extracted from each time steP andmatched to features in subsequent time stePs.SPatial overlaP 15 used to determinematching.The features from each time steP are stored in octree forests to sPeed thematching Process.Once feattlres are identied and tracked,ProPerties of the features andtheir evolutionary history can be comPuted.This information can be used to enhanceisosurface visualization and volume rendering by color coding individual regions.3 .In our Previous work,we have develoPed a methodology for analyzingtime一varying datasets which tracks 3D amorPhous features as they evolve in time.However,the imPlementation 15 for single一Processor non一adaPtive grids.For massivemultiresolution datasets this aPProach needs to be distributed and enhanced.In thisPaPer,we describe extensions to our feature extraction and tracking methodology.Twodifferent Paradigms are described,a"fully distributed"anda"Partial一merge"strategy.4 .An imPortant aPPlication field of visualization 15 CFD visualization.CFDProduces exPlosive time一varying data which can not use traditional methods to renderthem.In order to do it,designed a software system which 15 based on my Previous work.The system can render the CFD 3D time一varying data in four styles.