Publications du MATIS
Auteur : Martin Weinmann

Articles de revues avec comité de lecture

M. Weinmann, M. Weinmann, C. Mallet, M. Brédif. A classification-segmentation framework for the detection of individual trees in dense MMS point cloud data acquired in urban areas. Remote Sensing, vol. 9(3), 277, 2017.  

L. Landrieu, H. Raguet, B. Vallet, C. Mallet, M. Weinmann. A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 132, pp.102-118, 2017.  

M. Weinmann, B. Jutzi, S. Hinz, C. Mallet. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS Journal of Photogrammetry and Remote Sensing 105, pp. 286-304, 2015.  

M. Weinmann, S. Urban, S. Hinz, B. Jutzi, C. Mallet. Distinctive 2D and 3D Features for Automated Large-Scale Scene Analysis in Urban Areas. Computer & Graphics 49, pp.47–57, 2015.  

M. Weinmann, C. Mallet, B. Jutzi, S. Hinz. Efficient interpretation of 3D point clouds by assessing feature relevance. Allgemeine Vermessungs-Nachrichten (AVN), 10/2015, pp.308-315, 2015.  

Articles de conférences sans comité de lecture

M. Weinmann, C. Mallet, B. Jutzi. Involving different neighborhood types for the analysis of low-level geometric 2D and 3D features and their relevance for point cloud classification. Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (37. Wissenschaftlich-Technische Jahrestagung der DGPF), n°26, Würzburg (Germany), March 2017.  

M. Weinmann, C. Mallet, M. Brédif. Segmentation and Localization of Individual Trees from MMS Point Cloud Data Acquired in Urban Areas. in Publikationen der DGPF, Bern, 2016.  

M. Weinmann, B. Jutzi, C. Mallet. Describing Paris: Automated 3D Scene Analysis via Distinctive Low-Level Geometric Features. IQmulus Workshop on Processing Large Geospatial Data, in conjunction with SGP’14, Cardiff, UK, July 2014.  

M. Weinmann, B. Jutzi, C. Mallet. 3D Scene Analysis Based on Optimal Neighborhoods and Relevant Features. International Computer Vision Summer School, Catania, Sicily, July 2014.  

Articles de conférences avec comité de lecture

L. Landrieu, C. Mallet, M. Weinmann. Comparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data. Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, USA, July 2017.  

M. Weinmann, B. Jutzi, C. Mallet, M. Weinmann. Geometric features and their relevance for 3D point cloud classification. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017.  

M. Weinmann, C. Mallet, M. Brédif. Detection, Segmentation and Localization of Individual Trees from MMS Point Cloud Data. GEOBIA 2016, Best Paper Award, Enschede, The Netherlands, 14-16 September 2016.  

M. Weinmann, A. Schmidt, C. Mallet, S. Hinz, F. Rottensteiner, B. Jutzi. Contextual classification of point cloud data by exploiting individual 3D neighborhoods. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, PIA'15, Munich, Germany, 2015.  

M. Weinmann, B. Jutzi, C. Mallet. Semantic 3D scene interpretation: a framework combining optimal neighborhood size selection with relevant features. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3, Zürich, Switzerland, September 2014.  

M. Weinmann, B. Jutzi, C. Mallet. Feature relevance assessment for the semantic interpretation of 3D point cloud data. ISPRS Annals of Photogrammetry, Remote Sensing and the Spatial Information Sciences, Antalya, Turkey, November 2013.  

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