<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Csaba Domokos</style></author><author><style face="normal" font="default" size="100%">Jozsef Nemeth</style></author><author><style face="normal" font="default" size="100%">Zoltan Kato</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonlinear Shape Registration without Correspondences</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE</style></secondary-title><short-title><style face="normal" font="default" size="100%">IEEE T PATTERN ANAL</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.inf.u-szeged.hu/~kato/papers/TPAMI-2010-03-0146.R2_Kato.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">943 - 958</style></pages><isbn><style face="normal" font="default" size="100%">0162-8828</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;article&quot;&gt;&lt;p&gt;In this paper, we propose a novel framework to estimate the parameters of a diffeomorphism that aligns a known &lt;span class=&quot;snippet&quot;&gt;shape&lt;/span&gt; and its distorted observation. Classical &lt;span class=&quot;snippet&quot;&gt;registration&lt;/span&gt; methods first establish &lt;span class=&quot;snippet&quot;&gt;correspondences&lt;/span&gt; between the &lt;span class=&quot;snippet&quot;&gt;shapes&lt;/span&gt; and then compute the transformation parameters from these landmarks. Herein, we trace back the problem to the solution of a system of &lt;span class=&quot;snippet&quot;&gt;nonlinear&lt;/span&gt; equations which directly gives the parameters of the aligning transformation. The proposed method provides a generic framework to recover any diffeomorphic deformation &lt;span class=&quot;snippet&quot;&gt;without&lt;/span&gt; established &lt;span class=&quot;snippet&quot;&gt;correspondences&lt;/span&gt;. It is easy to implement, not sensitive to the strength of the deformation, and robust against segmentation errors. The method has been applied to several commonly used transformation models. The performance of the proposed framework has been demonstrated on large synthetic data sets as well as in the context of various applications.&lt;/p&gt;&lt;/div&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><work-type><style face="normal" font="default" size="100%">Journal article</style></work-type><accession-num><style face="normal" font="default" size="100%">12617610 </style></accession-num><notes><style face="normal" font="default" size="100%">UT: 000301747400009doi: 10.1109/TPAMI.2011.200</style></notes></record></records></xml>