<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marta Marron</style></author><author><style face="normal" font="default" size="100%">Miguel Angel Sotelo</style></author><author><style face="normal" font="default" size="100%">Garcia, Juan Carlos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparing Improved Versions of ‘K-Means’ and ‘Subtractive’ Clustering in a Tracking Application</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-Object Tracking</style></keyword><keyword><style  face="normal" font="default" size="100%">Particle Filters</style></keyword><keyword><style  face="normal" font="default" size="100%">Probabilistic Algorithms</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/t372500g4j22/#section=379081&amp;page=1&amp;locus=0</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">https://mail.geintra-uah.org/en/system/files/private/lncs-eurocast07.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Springer-Verlag </style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin/Heideberg</style></pub-location><volume><style face="normal" font="default" size="100%">4739</style></volume><pages><style face="normal" font="default" size="100%">717-724</style></pages><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">A partitional and a fuzzy clustering algorithm are compared in this
paper in terms of accuracy, robustness and efficiency. 3D position data
extracted from a stereo-vision system have to be clustered to use them in a
tracking application in which a particle filter is the kernel of the estimation task.
‘K-Means’ and ‘Subtractive’ algorithms have been modified and enriched with
a validation process in order improve its functionality in the tracking system.
Comparisons and conclusions of the clustering results both in a stand-alone
process and in the proposed tracking task are shown in the paper.</style></abstract><accession-num><style face="normal" font="default" size="100%">0,402 </style></accession-num><call-num><style face="normal" font="default" size="100%">Computer Science, Theory &amp; Methods </style></call-num></record></records></xml>