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The analysis and measurement of building patterns using texton co-occurrence matrices / Wenhao Yu in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) Titre : The analysis and measurement of building patterns using texton co-occurrence matrices Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur ; Tinghua Ai, Auteur ; Pengcheng Liu, Auteur ; Xiaoqiang Cheng, Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] données vectorielles [Termes IGN] matrice de co-occurrence [Termes IGN] métrique [Termes IGN] modèle géométrique du bâti [Termes IGN] reconnaissance de formes [Termes IGN] reconstruction 2D du bâti [Termes IGN] tessellation [Termes IGN] triangulation de Delaunay [Termes IGN] voisinage (relation topologique) Résumé : (auteur) The representation and analysis of building patterns are critical for characterizing urban scenes and making decisions in urban planning. The evaluation of building patterns is a difficult spatial analysis problem that exhibits properties of symbolization, homogeneity and regularity. Open issues in this field include the development of approaches for representing building patterns and vector-based methods for computing various pattern metrics. In the image analysis domain, there are many methods for pattern recognition (e.g., texture analysis), but there are few corresponding solutions for vector data. The aim of this research is to develop several building pattern metrics and offer a texton co-occurrence matrix (TCM)-based method to quantitatively evaluate the features of building patterns. The procedure first constructs a spatial field based on a Delaunay triangulation skeleton to partition a set of buildings into a set of tessellation cells. The tessellations of building clusters have a similar structure as image representations, in that each cell corresponds to an image pixel. We then use the texton analysis to establish a matrix to describe the tessellation structure, including the neighboring relationships and individual attribute information. Finally, a set of feature descriptors is obtained from the TCM to capture the texture-related information of building groups. Through experiments on building pattern analysis and spatial queries, we show that the results of TCM-based evaluation of building patterns are consistent with those of human cognition. Numéro de notice : A2017-242 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1265121 En ligne : http://dx.doi.org/10.1080/13658816.2016.1265121 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85178 [article]

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