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Detection of roadway sign condition changes using multi-scale sign image matching (M-SIM) / Y.J. Tsai in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 4 (April 2010)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 4 (April 2010) . - pp 391 - 405 Titre : Detection of roadway sign condition changes using multi-scale sign image matching (M-SIM) Type de document : Article/Communication Auteurs : Y.J. Tsai, Auteur ; Z. Hu, Auteur ; C. Alberti, Auteur Année de publication : 2010 Article en page(s) : pp 391 - 405 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse d'image numérique [Termes IGN] appariement d'images [Termes IGN] classification [Termes IGN] détection de changement [Termes IGN] données multiéchelles [Termes IGN] image vidéo [Termes IGN] Louisiane (Etats-Unis) [Termes IGN] positionnement par GPS [Termes IGN] réseau routier [Termes IGN] signalisation routière Résumé : (Auteur) Roadway signs are important for safety, and transportation agencies need to identify sign condition changes to perform timely maintenance, including replacement. Currently, sign condition changes are inspected manually in the field, which is time consuming, costly, and some--times dangerous. This paper first proposes a novel algorithm to detect three condition changes: missing, tilted, and blocked signs, using GPS data and video log images. The algorithm consists of three steps: (a) Multi-Scale Sign Image Matching (m-sim), (b) Image feature analysis, and (c) Sign condition change detection and classification. The algorithm was tested using images with simulated sign condition changes and actual video images taken in Fiscal Year (fy) 2003 and 2005 by the Louisiana Department of Transportation and Development (ladotd). The tests demonstrate the algorithm is effective to detect three types of sign condition changes. Out of 34,000 actual video log images, the algorithm detected and classified 100 percent of the missing signs, 72.7 percent of the tilted signs, and 66.7 percent of the blocked signs, for an overall 74.3 percent detection rate. These results show that the algorithm is useful for developing an intelligent roadway sign condition change detection system. Copyright ASPRS Numéro de notice : A2010-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.4.391 En ligne : https://doi.org/10.14358/PERS.76.4.391 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30316 [article]

Automatic segmentation of Lidar data into coplanar point clusters using an octree-based split-and-merge algorithm / M. Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 4 (April 2010)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 4 (April 2010) . - pp 407 - 420 Titre : Automatic segmentation of Lidar data into coplanar point clusters using an octree-based split-and-merge algorithm Type de document : Article/Communication Auteurs : M. Wang, Auteur ; Yi-Hsing Tseng, Auteur Année de publication : 2010 Article en page(s) : pp 407 - 420 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fusion d'images [Termes IGN] modélisation 3D [Termes IGN] octree [Termes IGN] segmentation [Termes IGN] segmentation en plan [Termes IGN] semis de points Résumé : (Auteur) Lidar (light detection and ranging) point cloud data contain abundant three-dimensional (3D) information. Dense distribution of scanned points on object surfaces prominently implies surface features. Particularly, plane features commonly appear in a typical lidar dataset of artificial structures. To explore implicitly contained spatial information, this study developed an automatic scheme to segment a lidar point cloud dataset into coplanar point clusters. The central mechanism of the proposed method is a split-and-merge segmentation based on an octree structure. Plane fitting serves as an engine in the mechanism that evaluates how well a group of points fits to a plane. Segmented coplanar points and derived parameters of their best-fit plane are obtained through the process. This paper also provides algorithms to derive various geometric properties of segmented coplanar points, including inherent properties of a plane, intersections of planes, and properties of point distribution on a plane. Several successful cases of handling airborne and terrestrial lidar data as well as a combination of the two are demonstrated. This method should improve the efficiency of object modelling using lidar data. Copyright ASPRS Numéro de notice : A2010-121 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.4.407 En ligne : https://doi.org/10.14358/PERS.76.4.407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30317 [article]