<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zagouras, A.a</style></author><author><style face="normal" font="default" size="100%">Argiriou, A.A.b</style></author><author><style face="normal" font="default" size="100%">Flocas, H.A.c</style></author><author><style face="normal" font="default" size="100%">Economou, G.a</style></author><author><style face="normal" font="default" size="100%">Fotopoulos, S.a</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A machine vision based method for atmospheric circulation classification</style></title><secondary-title><style face="normal" font="default" size="100%">DSP 2009: 16th International Conference on Digital Signal Processing, Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">atmospheric circulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Chain code</style></keyword><keyword><style  face="normal" font="default" size="100%">Chain codes</style></keyword><keyword><style  face="normal" font="default" size="100%">Classification scheme</style></keyword><keyword><style  face="normal" font="default" size="100%">Climatology</style></keyword><keyword><style  face="normal" font="default" size="100%">Codes (symbols)</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer vision</style></keyword><keyword><style  face="normal" font="default" size="100%">Descriptors</style></keyword><keyword><style  face="normal" font="default" size="100%">Digital signal processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Digital signal processors</style></keyword><keyword><style  face="normal" font="default" size="100%">Feature extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Feature representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Feature space</style></keyword><keyword><style  face="normal" font="default" size="100%">K- nearest neighbors algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine vision</style></keyword><keyword><style  face="normal" font="default" size="100%">Meteorological data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multidimensional vectors</style></keyword><keyword><style  face="normal" font="default" size="100%">Navigation</style></keyword><keyword><style  face="normal" font="default" size="100%">Nearest neighbors</style></keyword><keyword><style  face="normal" font="default" size="100%">regional climate</style></keyword><keyword><style  face="normal" font="default" size="100%">Semiautomatic techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Side chains</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Synoptic climatology</style></keyword><keyword><style  face="normal" font="default" size="100%">Training sets</style></keyword><keyword><style  face="normal" font="default" size="100%">Weather maps</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?eid=2-s2.0-70449572359&amp;partnerID=40&amp;md5=3cdf83b43b24549e0ddc26aa1b181869</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Santorini</style></pub-location><isbn><style face="normal" font="default" size="100%">9781424432981</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Weather maps refer to meteorological data that characterize the atmospheric circulation in a region. The classification of weather maps into categories becomes an important task for understanding regional climate. Towards this goal, manual and semiautomatic techniques have been used, requiring manpower and supervision. In this paper, we propose a machine vision based method for the classification of weather maps into distinct classes. The chain code descriptor is applied to extract the feature of isobaric lines and we introduce the Double-Side Chain Code (DSCC) histogram for feature representation. Handling DSCC histograms as multidimensional vectors, the A:-nearest neighbors (k- NN) algorithm classifies the objects to an appropriate number of classes, based on closest training set in the feature space. This method provides an automated and more ’objective’ classification scheme, applying straightforward to the input weather map’s image. © 2009 IEEE.</style></abstract><notes><style face="normal" font="default" size="100%">cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@291258ee ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@738c8652 Through org.apache.xalan.xsltc.dom.DOMAdapter@ec6acc2; Conference Code:78362</style></notes></record></records></xml>