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Improving image segmentation with boundary patch refinement / Xiaolin Hu in International journal of computer vision, vol 130 n° 11 (November 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 11 (November 2022) . - pp 2571 - 2589 Titre : Improving image segmentation with boundary patch refinement Type de document : Article/Communication Auteurs : Xiaolin Hu, Auteur ; Chufeng Tang, Auteur ; Hang Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2571 - 2589 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contour [Termes IGN] détection de contours [Termes IGN] distance euclidienne [Termes IGN] masque [Termes IGN] segmentation d'image [Termes IGN] segmentation fondée sur les contours [Termes IGN] segmentation sémantique Résumé : (auteur) Tremendous efforts have been made on image segmentation but the mask quality is still not satisfactory. The boundaries of predicted masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework, termed BPR, to improve the boundary quality of the prediction of any image segmentation model. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted boundaries. The refinement is accomplished by a boundary patch refinement network at the higher resolution. The trained BPR model can be easily transferred to refine the results of other models as well. Extensive experiments show that the proposed BPR framework yields significant improvements on the semantic, instance, and panoptic segmentation tasks over a variety of baselines on the Cityscapes dataset. Numéro de notice : A2022-741 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01662-0 Date de publication en ligne : 12/08/2022 En ligne : https://doi.org/10.1007/s11263-022-01662-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101719 [article]

The iterative convolution–thresholding method (ICTM) for image segmentation / Dong Wang in Pattern recognition, vol 130 (October 2022)  

Public [article]  inPattern recognition > vol 130 (October 2022) . - n° 108794 Titre : The iterative convolution–thresholding method (ICTM) for image segmentation Type de document : Article/Communication Auteurs : Dong Wang, Auteur ; Xiaoping Wang, Auteur Année de publication : 2022 Article en page(s) : n° 108794 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] contour [Termes IGN] convergence [Termes IGN] filtrage numérique d'image [Termes IGN] image à haute résolution [Termes IGN] itération [Termes IGN] segmentation d'image [Termes IGN] seuillage Résumé : (auteur) Variational methods, which have been tremendously successful in image segmentation, work by minimizing a given objective functional. The objective functional usually consists of a fidelity term and a regularization term. Because objective functionals may vary from different types of images, developing an efficient, simple, and general numerical method to minimize them has become increasingly vital. However, many existing methods are model-based, converge relatively slowly, or involve complicated techniques. In this paper, we develop a novel iterative convolution–thresholding method (ICTM) that is simple, efficient, and applicable to a wide range of variational models for image segmentation. In ICTM, the interface between two different segment domains is implicitly represented by the characteristic functions of domains. The fidelity term is usually written into a linear functional of the characteristic functions, and the regularization term is approximated by a functional of characteristic functions in terms of heat kernel convolution. This allows us to design an iterative convolution–thresholding method to minimize the approximate energy. The method has the energy-decaying property, and thus the unconditional stability is theoretically guaranteed. Numerical experiments show that the method is simple, easy to implement, robust, and applicable to various image segmentation models. Numéro de notice : A2022-779 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.patcog.2022.108794 Date de publication en ligne : 14/05/2022 En ligne : https://doi.org/10.1016/j.patcog.2022.108794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101857 [article]

3D building model simplification method considering both model mesh and building structure / Jiangfeng She in Transactions in GIS, vol 26 n° 3 (May 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 3 (May 2022) . - pp 1182 - 1203 Titre : 3D building model simplification method considering both model mesh and building structure Type de document : Article/Communication Auteurs : Jiangfeng She, Auteur ; Bo Chen, Auteur ; Junzhong Tan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1182 - 1203 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] contour [Termes IGN] contrainte géométrique [Termes IGN] empreinte [Termes IGN] maillage [Termes IGN] maillage par triangles [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] saillance [Termes IGN] simplification de maillage [Termes IGN] simplification de surface Résumé : (auteur) The simplification of three-dimensional (3D) building models to improve rendering efficiency has gained widespread attention. To maintain the model's overall appearance features while increasing the simplification rate, we propose a novel 3D building simplification method that considers both the model mesh and building structure. The method divides a 3D building into a primary structure and subsidiary structures. It then organizes these structures using StructureTree, a multi-way tree. The structures are organized according to the dependency relationships between building structures. When simplifying a building, the decision whether to simplify the mesh or remove the subsidiary structure in the leaf node of the StructureTree depends on the volume change caused by the edge collapse and the visual saliency of the removed structure. The experimental results show that our method exhibits a better simplification effect than the traditional simplification method, and the proposed method can achieve a high simplification rate while maintaining the simplification quality. Furthermore, the results of some spatial analyses based on the highly simplified building model are consistent with those of the original model. Numéro de notice : A2022-464 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1111/tgis.12907 Date de publication en ligne : 14/02/2022 En ligne : https://doi.org/10.1111/tgis.12907 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100792 [article]

A cost-effective method for reconstructing city-building 3D models from sparse Lidar point clouds / Marek Kulawiak in Remote sensing, vol 14 n° 5 (March-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 5 (March-1 2022) . - n° 1278 Titre : A cost-effective method for reconstructing city-building 3D models from sparse Lidar point clouds Type de document : Article/Communication Auteurs : Marek Kulawiak, Auteur Année de publication : 2022 Article en page(s) : n° 1278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Bâti-3D [Termes IGN] contour [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Gdansk [Termes IGN] maillage [Termes IGN] modélisation 3D [Termes IGN] reconstruction d'objet [Termes IGN] semis de points clairsemés [Termes IGN] triangulation de Delaunay Résumé : (auteur) The recent popularization of airborne lidar scanners has provided a steady source of point cloud datasets containing the altitudes of bare earth surface and vegetation features as well as man-made structures. In contrast to terrestrial lidar, which produces dense point clouds of small areas, airborne laser sensors usually deliver sparse datasets that cover large municipalities. The latter are very useful in constructing digital representations of cities; however, reconstructing 3D building shapes from a sparse point cloud is a time-consuming process because automatic shape reconstruction methods work best with dense point clouds and usually cannot be applied for this purpose. Moreover, existing methods dedicated to reconstructing simplified 3D buildings from sparse point clouds are optimized for detecting simple building shapes, and they exhibit problems when dealing with more complex structures such as towers, spires, and large ornamental features, which are commonly found e.g., in buildings from the renaissance era. In the above context, this paper proposes a novel method of reconstructing 3D building shapes from sparse point clouds. The proposed algorithm has been optimized to work with incomplete point cloud data in order to provide a cost-effective way of generating representative 3D city models. The algorithm has been tested on lidar point clouds representing buildings in the city of Gdansk, Poland. Numéro de notice : A2022-211 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14051278 Date de publication en ligne : 05/03/2022 En ligne : https://doi.org/10.3390/rs14051278 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100044 [article]

Archaeological 3D GIS / Nicolo Dell’Unto (2022)  

Public Titre : Archaeological 3D GIS Type de document : Monographie Auteurs : Nicolo Dell’Unto, Auteur ; Giacomo Landeschi, Auteur Editeur : Londres : Routledge Année de publication : 2022 Importance : 176 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-1-00-303413-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] ArcGIS [Termes IGN] archéologie [Termes IGN] contour [Termes IGN] géoréférencement [Termes IGN] GRASS [Termes IGN] modèle numérique de surface [Termes IGN] modélisation 3D [Termes IGN] SIG 3D [Termes IGN] système de gestion de base de données Résumé : (éditeur) Archaeological 3D GIS provides archaeologists with a guide to explore and understand the unprecedented opportunities for collecting, visualising, and analysing archaeological datasets in three dimensions. With platforms allowing archaeologists to link, query, and analyse in a virtual, georeferenced space information collected by different specialists, the book highlights how it is possible to re-think aspects of theory and practice which relate to GIS. It explores which questions can be addressed in such a new environment and how they are going to impact the way we interpret the past. By using material from several international case studies such as Pompeii, Çatalhöyük, as well as prehistoric and protohistoric sites in Southern Scandinavia, this book discusses the use of the third dimension in support of archaeological practice. This book will be essential for researchers and scholars who focus on archaeology and spatial analysis, and is designed and structured to serve as a textbook for GIS and digital archaeology courses. Note de contenu : 1- Geographical information systems in archaeology 2- 3D models and knowledge production 3- 3D GIS in archaeology 4- Deploying 3D GIS at the Trowel's edge 5- Surface and subsurface analysis 6- 3D visibility analysis 7- Volumes 8- Future developments Numéro de notice : 28663 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Monographie DOI : 10.4324/9781003034131 En ligne : https://doi.org/10.4324/9781003034131 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99844

Deep learning architectures for onboard satellite image analysis / Gaétan Bahl (2022)  

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Comparative analysis for methods of building digital elevation models from topographic maps using geoinformation technologies / Vadim Belenok in Geodesy and cartography, vol 47 n° 4 (December 2021)  

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A method of extracting high-accuracy elevation control points from ICESat-2 altimetry data / Binbin Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)  

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Metaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs / Arnaud Flori in Machine Vision and Applications, vol 32 n° 5 (September 2021)  

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Quantifying coherence between TDM90, SRTM90 and ASTER90 / Umut Gunes Sefercik in Geocarto international, vol 36 n° 15 ([15/08/2021])  

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A scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

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Panoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)  

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Supplementary material for: Panoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021) 

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Unsupervised vision methods based on image perceptual information / Eric Bazan (2021)  

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Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

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