<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1215</id>
    <completedYear/>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2011-02-10</completedDate>
    <publishedDate>2011-02-10</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Combinatorial Feature Flow Fields: Tracking Critical Points in Discrete Scalar Fields</title>
    <abstract language="eng">We propose a combinatorial algorithm to track critical points of 2D&#13;
time-dependent scalar fields. Existing tracking algorithms such as Feature&#13;
Flow Fields apply numerical schemes utilizing derivatives of the data,&#13;
which makes them prone to noise and involve a large number of computational&#13;
parameters. In contrast, our method is robust against noise&#13;
since it does not require derivatives, interpolation, and numerical integration.&#13;
Furthermore, we propose an importance measure that combines the&#13;
spatial persistence of a critical point with its temporal evolution. This&#13;
leads to a time-aware feature hierarchy, which allows us to discriminate&#13;
important from spurious features. Our method requires only a single,&#13;
easy-to-tune computational parameter and is naturally formulated in an&#13;
out-of-core fashion, which enables the analysis of large data sets. We apply&#13;
our method to a number of data sets and compare it to the stabilized&#13;
continuous Feature Flow Field tracking algorithm.</abstract>
    <identifier type="serial">11-02</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-12151</identifier>
    <author>Jan Reininghaus</author>
    <submitter>-empty- (Opus4 user: )</submitter>
    <author>Jens Kasten</author>
    <author>Tino Weinkauf</author>
    <author>Ingrid Hotz</author>
    <series>
      <title>ZIB-Report</title>
      <number>11-02</number>
    </series>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>data analysis</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>feature detection</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>time-varying data</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>topology-based techniques</value>
    </subject>
    <collection role="msc" number="05C85">Graph algorithms [See also 68R10, 68W05]</collection>
    <collection role="msc" number="37D15">Morse-Smale systems</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="projects" number="CVT">CVT</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1215/ZR-11-02.pdf</file>
  </doc>
</export-example>
