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A novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures / Alice Ahlem Othmani in Machine Vision and Applications, vol 27 n° 5 (July 2016)  

Public [article]  inMachine Vision and Applications > vol 27 n° 5 (July 2016) Titre : A novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures Type de document : Article/Communication Auteurs : Alice Ahlem Othmani, Auteur ; Cansen Jiang, Auteur ; Nicolas Lomenie, Auteur ; Jean-Marie Favreau, Auteur ; Alexandre Piboule, Auteur ; Lew F. C. Lew Yan Voon, Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] arbre (flore) [Termes IGN] classification [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] écorce [Termes IGN] extraction d'arbres [Termes IGN] forêt tempérée [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] reconnaissance de formes [Termes IGN] segmentation [Termes IGN] texture d'image [Termes IGN] zone saillante 3D Résumé : (auteur) Terrestrial Laser Scanning (TLS) systems have gained increasing popularity in the forestry domain and are today widely used for the automatic measurement of forest inventory attributes. Nevertheless, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. It is in this context that we present a novel Computer-Aided Tree Species Identification method based on 3D bark texture analysis. The novelty of our approach resides in the following three key points: (1) 3D salient regions extraction using a new morphological segmentation method that we have called Burst Wind Segmentation, (2) the extraction and pre-annotation of a collection of typical 3D bark patterns, known as scars, from each of the tree species. The pre-annotated scars are stored in a dictionary that we have called ScarBook and they are used as a reference for the comparison of the 3D salient segmented regions, (3) a wide variety of advanced shape, saliency, curvature and roughness features are extracted from the 3D salient segmented regions. To study the performance of our method, an experiment has been carried out on a dataset composed of 969 patches which correspond to 30 cm long segments of the trunk at breast height. Six species among the most dominant species in European forests have been tested with patches of different diameter at breast height values so as to study the identification accuracy with respect to age. The results obtained are very encouraging and promising and they confirm the possibility of identifying tree species using TLS data. Numéro de notice : A2016--134 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s00138-015-0738-2 Date de publication en ligne : 28/11/2015 En ligne : https://doi.org/10.1007/s00138-015-0738-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85267 [article]

Using textural and geometric information for an automatic bridge detection system / Roger Trias-Sanz (2004)  

Public Titre : Using textural and geometric information for an automatic bridge detection system Type de document : Article/Communication Auteurs : Roger Trias-Sanz , Auteur ; Nicolas Lomenie, Auteur ; Jérôme Barbeau, Auteur Editeur : Gand [Belgique] : Universiteit Gent Année de publication : 2004 Conférence : ACIVS 2004, Advanced Concepts for Intelligent Vision Systems 31/08/2004 03/09/2004 Bruxelles Belgique OA Abstracts only Importance : pp 325 - 332 Note générale : bibliographie PAS DE DOCUMENT AU CDOS Langues : Anglais (eng) Descripteur : [Termes IGN] classification automatique [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] géométrie de l'image [Termes IGN] image Ikonos [Termes IGN] image panchromatique [Termes IGN] image SPOT 5 [Termes IGN] pont [Termes IGN] texture d'image Résumé : (auteur) We present some results on systems for automatically detecting bridges in very high-resolution panchromatic satellite images using texture information and geometric models. The system has been tested on 2.5m per pixel and 1m per pixel aerial images processed to have the characteristics of SPOT 5 and Ikonos output. A system using simple geometric models gives good results for bridges over roads and railroads, and very bad results for bridges over larger regions such as rivers. In contrast, a system using a texture-based classification and hand-made rules applied to that classification gives good results for bridges over rivers and railroads, and bad results for bridges over roads. Numéro de notice : C2004-050 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://hal.science/hal-00136304 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103050

Automatic bridge detection in high-resolution satellite images / Roger Trias-Sanz (2003)  

Public Titre : Automatic bridge detection in high-resolution satellite images Type de document : Article/Communication Auteurs : Roger Trias-Sanz , Auteur ; Nicolas Lomenie, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2003 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 2626 Conférence : ICVS 2003, 3rd International Conference Computer Vision Systems 01/04/2003 03/04/2003 Graz Autriche Proceedings Springer Importance : pp 172 - 181 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] image panchromatique Résumé : (auteur) A set of methodologies and techniques for automatic detection of bridges in pan-chromatic, high-resolution satellite images is presented. These methods rely on (a) radiometric features and neural networks to classify each pixel into several terrain types, and (b) fixed rules to find bridges in this classification. They can be easily extended to other kinds of geographical objects, and integrated with existing techniques using geometric features. The proposed method has been tested in a number of experiments. Numéro de notice : C2003-046 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/3-540-36592-3_17 Date de publication en ligne : 14/03/2003 En ligne : https://doi.org/10.1007/3-540-36592-3_17 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101126

Integrating textural and geometric information for an automatic bridge detection system / Nicolas Lomenie (2003)  

Public Titre : Integrating textural and geometric information for an automatic bridge detection system Type de document : Article/Communication Auteurs : Nicolas Lomenie, Auteur ; Jérôme Barbeau, Auteur ; Roger Trias-Sanz , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2003 Conférence : IGARSS 2003, International Geoscience And Remote Sensing Symposium 21/07/2003 25/07/2003 Toulouse France Proceedings IEEE Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection automatique [Termes IGN] image panchromatique [Termes IGN] intégration de données [Termes IGN] pont Résumé : (auteur) We present some results on systems for automatically detecting bridges in high-resolution satellite images. We had made some preliminary explorations on the use of geometric models to detect bridges and round-abouts in panchromatic, high-resolution ((50 cm)/sup 2/ per pixel) satellite images. We had not yet systematically evaluated the system, but false alarms seemed to be its most important problem. In parallel, we had also built a system, which used local radiometric and textural features to classify terrain pixels into a number of semantically-meaningful classes, and then applied spatial relationships rules to these classes to detect and locate bridges. Validation showed a very low false alarm rate (around 5%), but also a low detection rate (around 40%). Numéro de notice : C2003-049 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2003.1295325 Date de publication en ligne : 10/05/2004 En ligne : https://doi.org/10.1109/IGARSS.2003.1295325 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101145