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Semi-automatic reconstruction of object lines using a smartphone’s dual camera / Mohammed Aldelgawy in Photogrammetric record, Vol 36 n° 176 (December 2021)  

Public [article]  inPhotogrammetric record > Vol 36 n° 176 (December 2021) . - pp 381 - 401 Titre : Semi-automatic reconstruction of object lines using a smartphone’s dual camera Type de document : Article/Communication Auteurs : Mohammed Aldelgawy, Auteur ; Isam Abu-Qasmieh, Auteur Année de publication : 2021 Article en page(s) : pp 381 - 401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] acquisition d'images [Termes IGN] appariement d'images [Termes IGN] appariement semi-automatique [Termes IGN] chambre non métrique [Termes IGN] correction d'image [Termes IGN] étalonnage [Termes IGN] extraction de traits caractéristiques [Termes IGN] forme linéaire [Termes IGN] intersection de ligne [Termes IGN] objectif grand angulaire [Termes IGN] reconstruction d'objet [Termes IGN] téléphone intelligent [Termes IGN] transformation de Hough Résumé : (Auteur) In this paper, the possibility of reconstructing object lines using a smartphone’s rear dual camera (wide-angle and telephoto) was examined through designing a semi-automatic system. After calibrating both cameras, six scenes for each of three objects were captured and rectified. Object lines were categorised into six groups based on the distance and angle to the dual camera system. Image lines were extracted using the linear Hough transform technique and points of intersection detected. Stereo pairing of conjugate points then allowed the calculation of object coordinates and the lengths of object lines were compared to their lengths measured by a digital caliper. The best line reconstruction results were achieved with the smallest distance and angle to the dual camera system. Numéro de notice : A2021-915 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12388 Date de publication en ligne : 19/10/2021 En ligne : https://doi.org/10.1111/phor.12388 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99330 [article]

Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101628 Titre : Flood depth mapping in street photos with image processing and deep neural networks Type de document : Article/Communication Auteurs : Bahareh Alizadeh Kharazi, Auteur ; Amir H. Behzadan, Auteur Année de publication : 2021 Article en page(s) : n° 101628 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Canada [Termes IGN] centre urbain [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] crue [Termes IGN] détection de contours [Termes IGN] Etats-Unis [Termes IGN] image Streetview [Termes IGN] inondation [Termes IGN] profondeur [Termes IGN] signalisation routière [Termes IGN] système d'aide à la décision [Termes IGN] traitement d'image [Termes IGN] transformation de Hough [Termes IGN] zone urbaine Résumé : (auteur) Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in urban areas using traditional methods such as remote sensing and digital elevation models (DEMs) yields large errors due to reshaped surface topography and microtopographic variations combined with vegetation bias. This paper presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads and intersections, coupled with Canny edge detection and probabilistic Hough transform to calculate pole length and estimate floodwater depth. Additionally, a tilt correction technique is implemented to address the problem of sideways tilt in visual analysis of submerged stop signs. An in-house dataset, named BluPix 2020.1 consisting of paired web-mined photos of submerged stop signs across 10 FEMA regions (for U.S. locations) and Canada is used to evaluate the models. Overall, pole length is estimated with an RMSE of 17.43 and 8.61 in. in pre- and post-flood photos, respectively, leading to a mean absolute error of 12.63 in. in floodwater depth estimation. Findings of this research are sought to equip jurisdictions, local governments, and citizens in flood-prone regions with a simple, reliable, and scalable solution that can provide (near-) real time estimation of floodwater depth in their surroundings. Numéro de notice : A2021-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101628 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97620 [article]

Reconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles in XYZ, n° 167 (juin 2021)

Public [article]  inXYZ > n° 167 (juin 2021) . - pp 33 - 38 Titre : Reconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne Type de document : Article/Communication Auteurs : Valentin Desbiolles, Auteur Année de publication : 2021 Article en page(s) : pp 33 - 38 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] Autocad Map [Termes IGN] automatisation des processus [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dessin assisté par ordinateur [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] image aérienne [Termes IGN] Jumeau numérique [Termes IGN] orthoimage [Termes IGN] transformation de Hough [Termes IGN] voie ferrée Résumé : (Auteur) Ce projet propose une étude sur l’insertion automatique d’objets utiles au fonctionnement d’une voie ferrée dans un plan DAO. Ces objets sont visibles sur des orthophotos acquises par moyens aéroportés (drone ou hélicoptère). La solution se scinde en deux grands axes : 1- la détection et la localisation des objets d’intérêt sur une orthophoto ; 2- leurs insertions dans un plan DAO. Ce PFE parcourt ainsi les différentes techniques pour automatiser une phase de reconnaissance de certains éléments cibles sur une image pour finir sur le développement d’une méthode permettant de les reporter dans un plan DAO automatiquement. Numéro de notice : A2021-462 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Date de publication en ligne : 01/06/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97928 [article]

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Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours / Amir Hossein Safaie in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 19 - 34 Titre : Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours Type de document : Article/Communication Auteurs : Amir Hossein Safaie, Auteur ; Heidar Rastiveis, Auteur ; Alireza Shams, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 19 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre remarquable [Termes IGN] arbre urbain [Termes IGN] détection d'arbres [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] sécurité routière [Termes IGN] semis de points [Termes IGN] tessellation [Termes IGN] transformation de Hough Résumé : (auteur) Trees are important road-side objects, and their geometric information plays an essential role in road studies and safety analyses. This paper proposes an efficient method for the automated creation of a road-side tree inventory using Mobile Terrestrial Lidar System (MTLS) point clouds. In the proposed method ground points are filtered through preprocessing to reduce processing time. Next, tree trunks are detected by performing a Hough Transform (HT) algorithm on several generated raster images from the point clouds. By initiating an approximate area of a tree’s foliage through a Voronoi Tessellation (VT) algorithm, the accurate boundary of the foliage is identified by applying Active Contour (AC) models. By extracting the points within this foliage boundary the geometric characteristics of each tree are obtained. This method was evaluated with two sample point clouds from different MTLS systems, and the algorithm correctly extracted all of the trees from both datasets. Additionally, comparing the calculated parameters with manually observed measures, the accuracy of the obtained geometric parameters were promising. Numéro de notice : A2021-206 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.026 Date de publication en ligne : 14/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.026 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97183 [article]

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Automatic object extraction from airborne laser scanning point clouds for digital base map production / Elyta Widyaningrum (2021)  

Public Titre : Automatic object extraction from airborne laser scanning point clouds for digital base map production Type de document : Thèse/HDR Auteurs : Elyta Widyaningrum, Auteur Editeur : Delft [Pays-Bas] : Delft University of Technology Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] axe médian [Termes IGN] chaîne de traitement [Termes IGN] détection d'objet [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction du réseau routier [Termes IGN] image aérienne [Termes IGN] orthoimage [Termes IGN] squelettisation [Termes IGN] transformation de Hough [Termes IGN] vectorisation Résumé : (auteur) A base map provides essential geospatial information for applications such as urban planning, intelligent transportation systems, and disaster management. Buildings and roads are the main ingredients of a base map and are represented by polygons. Unfortunately, manually delineating their boundaries from remote sensing data is time consuming and labour intensive. Airborne laser scanning (ALS) point clouds provide dense and accurate 3D positional information. Automatic extraction of buildings and roads from 3D point clouds is challenging because of their irregular shapes, occlusions in the data, and irregularity of ALS point clouds. This study focuses on two particular objectives: (i) accurate classification of a large volume of ALS 3D point clouds; and (ii) smooth and accurate building and road outline extraction. To achieve the classification objective, we perform point-wise deep learning to classify an ALS point cloud of a complex urban scene in Surabaya, Indonesia. The point cloud is colored by airborne orthophotos. Training data is obtained from an existing 2D topographic base map by a semi-automatic method proposed in this research. A dynamic-graph convolutional neural network is used to classify the point cloud into four classes: bare land, trees, buildings, and roads. We investigate effective input feature combinations for outdoor point cloud classification. A highly acceptable classification result of 91.8% overall accuracy is achieved when using the full combination of RGB color and LiDAR features. To address the objective of outline extraction, we propose building and road outline extraction methods that run directly on ALS point cloud data. For accurate and smooth building outline extraction, we propose two different methods. First, we develop the ordered Hough transform (OHT), which is an extension of the traditional Hough transform, by explicitly incorporating the sequence of points to form the outline. Second, we propose a new method based on Medial Axis Transform (MAT) skeletons which takes advantage of the skeleton points to detect building corners. The OHT method is resistant to noise but it requires prior knowledge on a building’s main directions. On the contrary, the MAT-based method does not require such orientation initialization but is more sensitive to noise on building edges. We compare the results of our building outline extraction methods to an existing RANSAC-based method, in terms of geometric accuracy, completeness of building corners, and computation time, and demonstrate that the MAT-based approach has the highest geometric accuracy, results in more complete building corners, and is slightly faster than other methods. For road network extraction, we develop a method based on skeletonization, which results in complete and continuous road centerlines and boundaries. In our study area, several roads are disrupted and disconnected due to trees. We design a tree-constrained approach to fill road gaps and integrate road width estimated from a medial axis algorithm. Comparison to reference data shows that the proposed method is able to extract almost all existing roads in the study area, and even detects roads that were not present in the reference due to human errors. We conclude that our object extraction methods enable a complete automatic procedure, extracting more accurate building and road outlines from ALS point cloud data. This contributes to a higher automation readiness level for a faster and cheaper base map production. Numéro de notice : 17664 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Sciences : TU Delft: 2021 Date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.4233/uuid:8900fac8-a76c-482a-b280-e1758783b5b3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97984

Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

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Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

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Planar polygons detection in lidar scans based on sensor topology enhanced Ransac / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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A point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

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Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data / Joris Ravaglia in Forests, vol 10 n° 7 (July 2019)  

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Mise en place d’un processus de dessin automatisé de plans d’intérieurs à partir de nuages de points acquis par LIDAR / Léa Talec (2017) 

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Geometrical consistency voting strategy for outlier detection in image matching / Luping Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)  

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A multi-scale plane-detection method based on the Hough transform and region growing / Xiaoxu Leng in Photogrammetric record, vol 31 n° 154 (June - August 2016)  

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Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests / Yongtao Yu in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)  

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