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Titre : Developing graphics frameworks with Python and OpenGL Type de document : Guide/Manuel Auteurs : Lee Stemkoski, Auteur ; Michael Pascale, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2021 Importance : 345 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-00-318137-8 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] image 3D
[Termes IGN] interface de programmation
[Termes IGN] OpenGL
[Termes IGN] processeur graphique
[Termes IGN] programmation informatique
[Termes IGN] Python (langage de programmation)
[Termes IGN] scène 3D
[Termes IGN] texture d'image
[Termes IGN] transformation géométriqueRésumé : (éditeur) Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds. You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results. Note de contenu : 1- Introduction to computer graphics
2- Introduction to Pygame and OpenGL
3- Matrix algebra and transformations
4- A scene graph framework
5- Textures
6- Light and shadowNuméro de notice : 28306 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel DOI : 10.1201/9781003181378 En ligne : https://doi.org/10.1201/9781003181378 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98077 Geometric and semantic joint approach for the reconstruction of digital models of buildings / Pierre-Alain Langlois (2021)
Titre : Geometric and semantic joint approach for the reconstruction of digital models of buildings Type de document : Thèse/HDR Auteurs : Pierre-Alain Langlois, Auteur ; Renaud Marlet, Directeur de thèse ; Alexandre Boulch, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2021 Importance : 131 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat de l’Ecole des Ponts ParisTech, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] jeu de données localisées
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] reconnaissance de surface
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] texture d'imageIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) The advent of Building Information Models (BIM) in the field of construction and city management revolutionizes the way we design, build, operate and maintain our buildings. BIM models not only include the geometric aspect of the buildings but also semantic information which identifies its logical components (walls, slabs, windows, doors, etc..). While this information is fairly reasonable to create during the building design, only 1% of the building stock is renewed each year. There is therefore an increasing need for automated methods to generate BIM models on existing buildings from sensors such as simple RGB cameras or more advanced Lidar sensors which directly provide a point cloud.In this context, the goal of this thesis is to develop approaches for BIM reconstruction, including both geometric reconstruction and semantic analysis.These tasks have been explored, but an important research effort is conducted to make them more robust to the variety of use cases found in practice.3D reconstruction is usually operated based on direct 3D acquisitions such as Lidars or using photogrammetry, i.e., using pictures to triangulate key point locations and reconstruct the surface afterward. In the context of buildings, the later case is usually limited by the presence of textureless areas which make it hard for the algorithms to find key points and to triangulate them. Moreover, some parts of the buildings might be missing from the input data because of occlusions or omission from the acquisition operator.Regarding semantics in point clouds, important ambiguities exist between semantic classes: the discontinuity between a wall and a door can be hard to distinguish; a slab, a roof and a ceiling sometimes need additional context to be disentangled.In this thesis, we present three technical contributions to address these issues.First, for 3D reconstruction of building scenes, we propose the first method to reconstruct piecewise-planar scenes from images using line segments as primitives. While wide textureless areas exist in indoor scenes (e.g., walls), making it particularly difficult to detect key points, lines are often more visible and easier to detect (e.g., change of illumination at the intersection of two walls) and therefore should be used to ensure robustness of image-based reconstruction approaches. We leverage the presence of these line segments and the possibility to detect and triangulate them. This makes the method robust to textureless surfaces, and we show that we can reconstruct scenes on which point-based methods fail.The second contribution is more theoretical and addresses the problem of mesh reconstruction from multiple calibrated images of low resolution. In this context, traditional methods completely fail and directly learning priors on a large scale dataset of 3D shapes allows us to still perform reconstruction. More specifically, our method uses the learned priors to provide an initial rough shape which is further refined by incorporating geometric constraints. Our method directly outputs a mesh and competes with state of the art methods which can only output a noisy point cloud.Finally, the third technical contribution is VASAD, a dataset for volumetric and semantic reconstruction, which we created from raw BIM models available online. It is the first large scale dataset (62000m²) to offer both geometric and semantic annotation at point and mesh level. With this dataset, we propose two methods to jointly reconstruct both geometry and semantics from a point cloud and we show that the proposed dataset is challenging enough to stimulate research. Note de contenu : 1. Introduction
1.1 Motivation
1.2 Approach
1.3 Contributions
1.4 Organization of the dissertation
SURFACE RECONSTRUCTION FROM 3D LINE SEGMENTS
2. Introduction
2.1 Reconstructing textureless surfaces
2.2 Related Work
3. Method
3.1 Line extraction
3.2 Plane detection from 3D line segments
3.3 Surface reconstruction
4. Results
4.1 Datasets
4.2 Observations on the input data
4.3 Qualitative evaluation of reconstructions
4.4 Quantitative evaluation of reconstructions
4.5 Ablation study
4.6 Limitations and perspectives
4.7 Conclusion
3D RECONSTRUCTION BY PARAMETERIZED SURFACE MAPPING
5. Introduction
5.1 Learning 3D reconstruction
5.2 Related work
6. Method
6.1 Learning a Multi-View Parameterized Surface Mapping
6.2 Design choices
7. Results
7.1 Dataset
7.2 Evaluation criteria
7.3 Experimental results
7.4 Ablation study
7.5 Discussion and limitations
7.6 Conclusion
VASAD: A VOLUME AND SEMANTIC DATASET FOR BUILDING RECONSTRUCTION FROM POINT CLOUDS
8. Introduction
8.1 3D Reconstruction for buildings
8.2 Related work
9. DATASET
9.1 Building information models
9.2 Presentation of the dataset
9.3 3D representation
9.4 Point cloud simulation
9.5 Train/test split
10. Method
10.1 Reconstruction approaches
10.2 PVSRNet
10.3 Semantic Convolutional Occupancy Network
10.4 Data preparation
11. RESULTS
11.1 Metrics
11.2 Surface reconstruction
11.3 Semantic segmentation
11.4 Discussion
11.5 Conclusion
EPILOGUE
12. Conclusion
12.1 Looking back
12.2 Looking aheadNuméro de notice : 26822 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : informatique : Champs-Sur-Marne : 2021 Organisme de stage : Laboratoire d'Informatique Gaspard Monge LIGM nature-HAL : Thèse DOI : sans Date de publication en ligne : 11/04/2022 En ligne : https://tel.hal.science/tel-03637158/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100564 Remote sensing analysis of small scale dynamic phenomena in the atmospheric boundary layer / Kostas Cheliotis (2021)
Titre : Remote sensing analysis of small scale dynamic phenomena in the atmospheric boundary layer Type de document : Thèse/HDR Auteurs : Kostas Cheliotis, Auteur ; Hervé Delbarre, Auteur Editeur : Dunkerque : Université du Littoral-Côte-d'Opale Année de publication : 2021 Importance : 139 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée pour obtenir le grade de Docteur de l’Université du Littoral Côte d’Opale, Mention : Physique, Spécialité : Milieux dilués et optiques fondamentalesLangues : Français (fre) Descripteur : [Vedettes matières IGN] Atmosphère
[Termes IGN] analyse discriminante
[Termes IGN] analyse texturale
[Termes IGN] balayage laser
[Termes IGN] classification dirigée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] effet atmosphérique
[Termes IGN] lidar à effet Doppler
[Termes IGN] lidar atmosphérique
[Termes IGN] météorologie
[Termes IGN] ventIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The aim of this thesis project was to study the coherent turbulent structures (convective rolls & streaks) and more generally the medium to large fluctuations in the atmospheric boundary layer using the observations recorded by a single Doppler (wind) lidar during a 2-month campaign in Paris, France. An innovative method was developed in order to classify automatically the radial wind speed patterns visible on the quasi-horizontal lidar scans, based on texture analysis parameters and supervised machine learning algorithms. A 150-case training ensemble was built using ancillary data (satellite pictures and weather observations) to ascertain the manual classification into four types: rolls, thermals, streaks and “others”. The performance of the classification process was assessed on the training ensemble using the 10-fold cross-validation method. A very satisfying 9% error was obtained for the Quadratic Discriminant Analysis algorithm, using only 5 texture analysis parameters classifiers. This process was then applied to classify the whole dataset (4577 lidar scans) and the results showed that the classified structures respected a plausible diurnal cycle and were associated with the meteorological parameters as expected by the theoretical knowledge. The size of the coherent structures in the direction transverse to the mean wind were estimated from the wind spectrums on a four-day case study. They ranged from 400 to 800 m for the cases classified as streaks, and from 1.3 to 2.0 km for the cases classified as rolls. These results pave the way for future long-term studies providing statistical insight on the frequency of occurrence of the different structure types, their physical properties, and their impact on pollutants’ concentrations. Numéro de notice : 28616 Affiliation des auteurs : non IGN Nature : Thèse française Note de thèse : Thèse de Doctorat : Milieux dilués et optiques fondamentales : Côte d'Opale : France : 2021 Organisme de stage : Laboratoire de Physico-Chimie de l'Atmosphère DOI : sans En ligne : https://hal.science/tel-03259369/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99504
Titre : Unsupervised vision methods based on image perceptual information Type de document : Thèse/HDR Auteurs : Eric Bazan, Auteur ; Petr Dokladal, Directeur de thèse ; Eva Dokladalova, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2021 Importance : 227 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l'Université Paris Sciences et Lettres, Préparée à MINES ParisTech, spécialité Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] compréhension de l'image
[Termes IGN] contour
[Termes IGN] couleur (variable spectrale)
[Termes IGN] décomposition spectrale
[Termes IGN] filtre de Gabor
[Termes IGN] image captée par drone
[Termes IGN] segmentation d'image
[Termes IGN] texture d'image
[Termes IGN] visionIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis work deals with extracting features and low-level primitives from perceptual image information to understand scenes. Motivated by the needs and problems in Unmanned Aerial Vehicles (UAVs) vision based navigation, we propose novel methods focusing on image understanding problems. This work explores three main pieces of information in an image: intensity, color, and texture. In the first chapter of the manuscript, we work with the intensity information through image contours. We combine this information with human perception concepts, such as the Helmholtz principle and the Gestalt laws, to propose an unsupervised framework for object detection and identification. We validate this methodology in the last stage of the drone navigation, just before the landing. In the following chapters of the manuscript, we explore the color and texture information contained in the images. First, we present an analysis of color and texture as global distributions of an image. This approach leads us to study the Optimal Transport theory and its properties as a true metric for color and texture distributions comparison. We review and compare the most popular similarity measures between distributions to show the importance of a metric with the correct properties such as non-negativity and symmetry. We validate such concepts in two image retrieval systems based on the similarity of color distribution and texture energy distribution. Finally, we build an image representation that exploits the relationship between color and texture information. The image representation results from the image’s spectral decomposition, which we obtain by the convolution with a family of Gabor filters. We present in detail the improvements to the Gabor filter and the properties of the complex color spaces. We validate our methodology with a series of segmentation and boundary detection algorithms based on the computed perceptual feature space. Numéro de notice : 15285 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Morphologie Mathématique : Paris Sciences et Lettres : 2021 Organisme de stage : Centre de Morphologie Mathématique DOI : sans En ligne : https://hal.science/tel-03690309 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101418 Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal / Santa Pandit in Geocarto international, vol 35 n° 16 ([01/12/2020])
[article]
Titre : Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal Type de document : Article/Communication Auteurs : Santa Pandit, Auteur ; Satoshi Tsuyuki, Auteur ; Timothy Dube, Auteur Année de publication : 2020 Article en page(s) : pp 1832 - 1849 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse multibande
[Termes IGN] analyse texturale
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] forêt
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] NépalRésumé : (auteur) The potential of the improved resolution Sentinel-2 MSI data was explored through texture metrics, vegetation indices (VIs) and pooled dataset using the Random Forest (RF) machine learning algorithm to estimate Above-ground Biomass (AGB) in a sub-tropical forest of Nepal. Texture metrics were derived based on different working window sizes (3 × 3, 5 × 5, 7 × 7 and 9 × 9), and the results were compared with those obtained, using raw traditional bands (Analysis set 1: 2, 3, 4, 8, 11 and 12), raw traditional and red edge bands (Analysis set 2: Set 1 + Band 5, 6, 7 and 8A), and red edge bands (Analysis set 3) only. Comparatively, the use of pooled data (texture and VIs) yielded higher biomass estimates. The results from pooled data based on the 7 × 7 window size resulted in models with better model fitting parameters. For instance, pooled data produced an R2 = 0.99 and a RMSE = 4.51 t ha−1 (relRMSE = 2.82). Further, the RF model selected dissimilarity, variance and mean from Band 2 and SAVI (Soil adjusted vegetation index) as the most important AGB predictor variables. The results demonstrated that like the red-edge bands, traditional bands were equally important in AGB estimation. Numéro de notice : A2020-727 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1588390 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1588390 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96334
in Geocarto international > vol 35 n° 16 [01/12/2020] . - pp 1832 - 1849[article]Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection / Chandi Witharana in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)PermalinkMapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)PermalinkTextural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])PermalinkThermal and spatial data integration for recreating rebuilding stages of wooden and masonry buildings / Paulina Lewińska in Photogrammetric record, vol 35 n° 171 (September 2020)PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkClassification of sea ice types in Sentinel-1 SAR data using convolutional neural networks / Hugo Boulze in Remote sensing, vol 12 n° 13 (July-1 2020)PermalinkPotential of texture from SAR tomographic images for forest aboveground biomass estimation / Zhanmang Liao in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)PermalinkRegion level SAR image classification using deep features and spatial constraints / Anjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)PermalinkAdaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation / Deliang Xiang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkExtracting impervious surfaces from full polarimetric SAR images in different urban areas / Sara Attarchi in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)PermalinkSimultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkAutocovariance-based perceptual textural features corresponding to human visual perception / N. Abbadeni (2020)PermalinkPermalinkPermalinkUsing a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia / Neil Flood in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkAlbedo estimation for real-time 3D reconstruction using RGB-D and IR data / Patrick Stotko in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkMethod for an automatic alignment of imagery and vector data applied to cadastral information in Poland / Juan J. Ruiz-Lendínez in Survey review, vol 51 n° 365 (March 2019)PermalinkTree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkPermalinkPermalink