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Detecting roads in stabilized video with the spatio-temporal structure tensor / R. Plessl in Geoinformatica, vol 10 n° 1 (March - May 2006)  

Public [article]  inGeoinformatica > vol 10 n° 1 (March - May 2006) . - pp 37 - 53 Titre : Detecting roads in stabilized video with the spatio-temporal structure tensor Type de document : Article/Communication Auteurs : R. Plessl, Auteur Année de publication : 2006 Conférence : ACM GIS 2004, 12th ACM symposium on geographic information systems 12/11/2004 13/11/2004 Arlington Etats-Unis Selected papers Article en page(s) : pp 37 - 53 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] capteur aérien [Termes IGN] données spatiotemporelles [Termes IGN] extraction du réseau routier [Termes IGN] géoréférencement [Termes IGN] image vidéo [Termes IGN] mise à jour de base de données [Termes IGN] temps réel [Termes IGN] tenseur [Termes IGN] trafic routier [Termes IGN] valeur propre Résumé : (Auteur) Video provides strong cues for automatic road extraction that are not available in static aerial images. In video from a static camera, or stabilized (or geo-referenced) aerial video data, motion patterns within a scene enable fonction attribution of scene regions. A "road," for example, may be defined as a path of consistent motion - a definition which is valid in a large and diverse set of environments. The spatio-temporal structure tensor field is an ideal representation of the image derivative distribution at each pixel because it can be updated in real time as video is acquired. An eigen-decomposition of the structure tensor encodes both the local scene motion and the variability in the motion. Additionally, the structure tensor field can be factored into motion components, allowing explicit determination of traffic patterns in intersections. Example results of a real time system are shown for an urban scene with both well-traveled and infrequently traveled roads, indicating that both can be discovered simultaneously. The method is ideal in urban traffic scenes, which are the most difficult to analyze using static imagery. Numéro de notice : A2006-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-005-4885-x En ligne : https://doi.org/10.1007/s10707-005-4885-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27824 [article]

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