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Fusion of thermal imagery with point clouds for building façade thermal attribute mapping / Dong Lin in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 162 - 175 Titre : Fusion of thermal imagery with point clouds for building façade thermal attribute mapping Type de document : Article/Communication Auteurs : Dong Lin, Auteur ; Malgorzata Jarząbek-Rychard, Auteur ; Xiaochong Tong, Auteur ; Hans-Gerd Maas, Auteur Année de publication : 2019 Article en page(s) : pp 162 - 175 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] façade [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] texturage Résumé : (Auteur) Thermal image data are widely used to assess the insulation quality of buildings and to detect thermal leakages. In our approach, we merge terrestrial thermal image data and 3D point clouds to perform thermal texture mapping for building facades. Since geo-referencing data of a hand-held thermal camera is usually not available in such applications, registration between thermal images and a 3D point cloud (for instance generated from RGB image data by structure-from-motion techniques) is essential. In our approach, thermal image data registration is conducted in four steps: First, another point cloud is generated from the thermal image data. Next, a coarse registration between thermal point cloud and RGB point cloud is performed using the fast global registration (FGR) algorithm. The best corresponding thermal-RGB image pairs are acquired by picking up the lowest Euclidean distance between the exterior orientation parameters of thermal images and transformed exterior orientation parameters of RGB images. Subsequently, radiation-invariant feature transform (RIFT), normalized barycentric coordinate system (NBCS) and random sample consensus (RANSAC) are employed to extract reliable matching features on thermal-RGB image pairs. Afterwards, a fine registration is performed by mono-plotting of the RGB image, followed by image resection of the thermal image. Finally, in terms of texture mapping algorithms, in order to remove the blur effects caused by small misalignments for different candidate images, a global image pose refinement approach, which aims to minimize the temperature disagreements provided by different images for the same object points, is proposed. In addition, in order to ensure high geometric and radiant accuracy, camera calibrations are performed. Experiments showed that the proposed method could not only achieve high geometric registration accuracy, but also provide a good radiometric accuracy with RMSE lower than 1.5 °C. Numéro de notice : A2019-208 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.010 date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92674 [article]

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Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 147 (January 2019) . - pp 19 - 41 Titre : Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation Type de document : Article/Communication Auteurs : Xin Wang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 19 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] compensation par faisceaux [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] matrice de rotation [Termes descripteurs IGN] orientation relative [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] recouvrement d'images [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) In this paper, we present a new fast and robust method for structure from motion (SfM) for data sets potentially comprising thousands of ordered or unordered images. Our work focuses on the two most time-consuming procedures: (a) image matching and (b) pose estimation. For image matching, a new method employing a random k-d forest is proposed to quickly obtain pairs of overlapping images from an unordered set. After that, image matching and the estimation of relative orientation parameters are performed only for pairs found to be very likely to overlap. For pose estimation, we use a two-stage global approach, separating the determination of rotation matrices and translation parameters; the latter are computed simultaneously using a new method. In order to cope with outliers in the relative orientations, which global approaches are particularly sensitive to, we present a new constraint based on triplet loop closure errors of rotation and translation. Finally, a robust bundle adjustment is carried out to refine the image orientation parameters. We demonstrate the potential and limitations of our pipeline using various real-world datasets including ordered image data acquired from UAV (unmanned aerial vehicle) and other platforms as well as unordered data from the internet. The experiments show that our work performs better than comparable state-of-the-art SfM systems in terms of run time, while we achieve a similar accuracy and robustness. Numéro de notice : A2019-033 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.009 date de publication en ligne : 15/11/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91970 [article]

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Robust vehicle detection in aerial images using bag-of-words and orientation aware scanning / Hailing Zhou in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 12 (December 2018) . - pp 7074 - 7085 Titre : Robust vehicle detection in aerial images using bag-of-words and orientation aware scanning Type de document : Article/Communication Auteurs : Hailing Zhou, Auteur ; Lei Wei, Auteur ; Chee Peng Lim, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 7074 - 7085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] accentuation d'image [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] modèle sac-de-mots [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] PMVS [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] transformation de Radon [Termes descripteurs IGN] véhicule Résumé : (auteur) This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with “one-window-one-car” results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images. Numéro de notice : A2018-555 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2848243 date de publication en ligne : 17/07/2018 En ligne : http://dx.doi.org/10.1109/TGRS.2018.2848243 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91654 [article]

Automatic registration of images to untextured geometry using average shading gradients / Tobias Plötz in International journal of computer vision, vol 125 n° 1-3 (December 2017)  

Public [article]  inInternational journal of computer vision > vol 125 n° 1-3 (December 2017) . - pp 65 - 81 Titre : Automatic registration of images to untextured geometry using average shading gradients Type de document : Article/Communication Auteurs : Tobias Plötz, Auteur ; Stefan Roth, Auteur Année de publication : 2017 Article en page(s) : pp 65 - 81 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] attribut géomètrique [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] gradient [Termes descripteurs IGN] maillage par triangles [Termes descripteurs IGN] ombre [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] superposition d'images Résumé : (Auteur) Many existing approaches for image-to-geometry registration assume that either a textured 3D model or a good initial guess of the 3D pose is available to bootstrap the registration process. In this paper we consider the registration of photographs to 3D models even when no texture information is available. This is very challenging as we cannot rely on texture gradients, and even shading gradients are hard to estimate since the lighting conditions are unknown. To that end, we propose average shading gradients, a rendering technique that estimates the average gradient magnitude over all lighting directions under Lambertian shading. We use this gradient representation as the building block of a registration pipeline based on matching sparse features. To cope with inevitable false matches due to the missing texture information and to increase robustness, the pose of the 3D model is estimated in two stages. Coarse pose hypotheses are first obtained from a single correct match each, subsequently refined using SIFT flow, and finally verified. We apply our algorithm to registering images of real-world objects to untextured 3D meshes of limited accuracy. Moreover, we show that registration can be performed even for paintings despite lacking photo-realism. Numéro de notice : A2017-813 Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s11263-017-1022-x En ligne : https://doi.org/10.1007/s11263-017-1022-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89255 [article]

Automatic illumination-invariant image-to-geometry registration in outdoor environments / Christian Kehl in Photogrammetric record, vol 32 n° 158 (June - july 2017)  

Public [article]  inPhotogrammetric record > vol 32 n° 158 (June - july 2017) . - pp 93 - 118 Titre : Automatic illumination-invariant image-to-geometry registration in outdoor environments Type de document : Article/Communication Auteurs : Christian Kehl, Auteur ; Simon J. Buckley, Auteur ; Sophie Viseur, Auteur ; Robert L. Gawthorpe, Auteur ; John Howell, Auteur Année de publication : 2017 Article en page(s) : pp 93 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] algorithme d'appariement [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] géométrie de l'image [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] traitement d'image Résumé : (Auteur) Image-to-geometry registration is the basis of many applications for texturing and interpreting 3D surface models. Feature-based matching is an established, automatic approach which creates 2D–3D correspondences based on salient points and their radiometric neighbourhood. This paper presents an experimental approach for assessing the accuracy of several matching algorithms in challenging imaging environments that are subject to significant outdoor illumination variations. Furthermore, a collection of accuracy assessment metrics and quality heuristics emerge from the presented approach to guide a user during the examination of registration results. As a result of the experiments, two novel salient point descriptor matching combinations outperform the standard scale-invariant feature transform (SIFT) operator on the task of image-to-image and image-to-geometry registration under varying illumination conditions. Numéro de notice : A2017-361 Thématique : IMAGERIE Nature : Article En ligne : http://doi.org/10.1111/phor.12188 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85772 [article]

Airborne Lidar/INS/GNSS : algorithm uses fuzzy controlled Scale Invariant Feature Transform (SIFT) / Haowei Xu in GPS world, vol 28 n° 3 (March 2017)

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Autonomous ortho-rectification of very high resolution imagery using SIFT and genetic algorithm / Pramod Kumar Konugurthi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)  

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Evaluation of SIFT and SURF for vision based localization / Xiaozhi Qu (2016)    

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Distinctive order based self-similarity descriptor for multi-sensor remote sensing image matching / Amin Sedaghat in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)  

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Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation / J.G. Martins in Machine Vision and Applications, vol 26 n° 2-3 (April 2015)  

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SAR-SIFT: a SIFT-like algorithm for SAR images / Flora Dellinger in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)

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A robust image matching method based on optimized BaySAC / Zhizhong Kang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 11 (November 2014)  

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Image matching using SIFT features and relaxation labeling technique—A constraint initializing method for dense stereo matching / Jyoti Joglekar in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)

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Semi-automated registration of close-range hyperspectral scans using oriented digital camera imagery and a 3D model / Alessandra A. Sima in Photogrammetric record, vol 29 n° 145 (March - May 2014)

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Automatic orientation and 3D modelling from markerless rock art imagery / J. Lerma in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)

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