<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>4402</id>
    <completedYear/>
    <publishedYear>2004</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>222</pageFirst>
    <pageLast>230</pageLast>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>14</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Improving Statistical Object Recognition Approaches by a Parameterization of Normal Distributions</title>
    <abstract language="eng">As statistical approaches play an important role in object recognition, we present a novel approach which is based on object mod- els consisting of normal distributions for each training image. We show how to parameterize the mean vector and covariance matrix independently from the interpolation technique and formulate the classification and localization as a continuous optimization problem. This enables the computation of object poses which have never been seen during training. For interpolation, we present four different techniques which are compared in an experiment with real images. The results show the benefits of our method both in classification rate and pose estimation accuracy.</abstract>
    <parentTitle language="eng">6th German-Russian IAPR Workshop on Pattern Recognition and Image Understanding</parentTitle>
    <identifier type="issn">1054-6618</identifier>
    <identifier type="url">https://www.researchgate.net/profile/Frank-Deinzer/publication/215748368_Improving_Statistical_Object_Recognition_Approaches_by_a_Parameterization_of_Normal_Distributions/links/0912f51017f34be4cb000000/Improving-Statistical-Object-Recognition-Approaches-by-a-Parameterization-of-Normal-Distributions.pdf</identifier>
    <enrichment key="opus.import.data">@articleGraessl04:ISO, author = Gräßl, Christoph and Deinzer, Frank and Mattern, F. and Niemann, Heinrich, title = Improving Statistical Object Recognition Approaches by a Parameterization of Normal Distributions, year = 2004, volume = 14, number = 2, journal = Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications), publisher = International Academic Publishing Concern/Interperiodica Publishing, address = Moskau, Russion Federation, pages = 222–230, issn = 1054-6618,</enrichment>
    <enrichment key="opus.import.dataHash">md5:0c8c002f8aa0f29b4a6f6267d1491c6c</enrichment>
    <enrichment key="opus.import.date">2023-10-04T08:12:27+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phpAd751O</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">651d1e6bf1a098.41039532</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Christoph Gräßl</author>
    <author>Frank Deinzer</author>
    <author>F. Mattern</author>
    <author>Heinrich Niemann</author>
  </doc>
</export-example>
