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Hidden Markov map matching based on trajectory segmentation with heading homogeneity / Ge Cui in Geoinformatica [en ligne], vol 25 n° 1 (January 2021)  

Public [article]  inGeoinformatica [en ligne] > vol 25 n° 1 (January 2021) . - pp 179 - 206 Titre : Hidden Markov map matching based on trajectory segmentation with heading homogeneity Type de document : Article/Communication Auteurs : Ge Cui, Auteur ; Wentao Bian, Auteur ; Xin Wang, Auteur Année de publication : 2021 Article en page(s) : pp 179 - 206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] appariement de cartes [Termes IGN] appariement de données localisées [Termes IGN] modèle de Markov caché [Termes IGN] réseau routier [Termes IGN] segmentation [Termes IGN] trajectographie par GPS [Vedettes matières IGN] Généralisation Résumé : (Auteur) Map matching is to locate GPS trajectories onto the road networks, which is an important preprocessing step for many applications based on GPS trajectories. Currently, hidden Markov model is one of the most widely used methods for map matching. However, both effectiveness and efficiency of conventional map matching methods based on hidden Markov model will decline in the dense road network, as the number of candidate road segments enormously increases around GPS point. To overcome the deficiency, this paper proposes a segment-based hidden Markov model for map matching. The proposed method first partitions GPS trajectory into several GPS sub-trajectories based on the heading homogeneity and length constraint; next, the candidate road segment sequences are searched out for each GPS sub-trajectory; last, GPS sub-trajectories and road segment sequences are matched in hidden Markov model, and the road segment sequences with the maximum probability is identified. A case study is conducted on a real GPS trajectory dataset, and the experiment result shows that the proposed method improves the effectiveness and efficiency of the conventional HMM map matching method. Numéro de notice : A2021-094 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00429-4 date de publication en ligne : 02/01/2021 En ligne : https://doi.org/10.1007/s10707-020-00429-4 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96934 [article]

Robust and fast global image orientation / Xin Wang (2021)  

Public Titre : Robust and fast global image orientation Type de document : Thèse/HDR Auteurs : Xin Wang, Auteur ; Christian Heipke, Directeur de thèse Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2021 Collection : DGK - C, ISSN 0065-5325 num. 871 Importance : 141 p. Note générale : bibliographie Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover ISSN 0174-1454, Nr. 373, Hannover 2021 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] appariement d'images [Termes IGN] chaîne de traitement [Termes IGN] estimation de pose [Termes IGN] méthode robuste [Termes IGN] orientation d'image [Termes IGN] orientation relative [Termes IGN] rotation [Termes IGN] structure-from-motion [Termes IGN] translation [Termes IGN] valeur aberrante Résumé : (auteur) The estimation of image orientation (also called pose) has always played a crucial role in the field of photogrammetry since it is a fundamental prerequisite for the subsequent works of multi-view dense matching, generating DEM and DSM, etc. In the community of computer vision, the task is also well known as Structure-from-Motion (SfM), which reveals that image pose, while positions of object points are determined interdependently. Despite a lot of efforts over the last decades, it has recently gained the photogrammetrists’ interests again due to the fast-growing number of different resources of images. New challenges are posed for accurately and efficiently orienting various image datasets (e.g., unordered datasets with a large number of images, or images compromised of critical stereo pairs). In this thesis, the relevant ambition is to develop a new fast and robust method for the estimation of image orientation which is capable of coping with different types of datasets. To achieve this goal, the two most time-consuming steps of image orientation are in particular taken care of: (a) image matching and (b) the estimation process. To accelerate the image matching process, a new method employing a random k-d forest is proposed to quickly obtain pairs of overlapping images from an unordered image set. After that, image matching and the estimation of relative orientation parameters are performed only for pairs found to be very likely overlapping. On the other hand, to estimate the image poses in a time efficient manner, a global image orientation strategy is advocated. Its basic idea is to first simultaneously solve all available images’ poses, before a final bundle adjustment is carried out once for refinement. The conventional two-step global approach is pursued in this work, separating the determination of rotation matrices and translation parameters; the former is solved by an existing popular method of Chatterjee and Govindu [2013], and the latter are estimated globally using a newly developed method: translation estimation integrating both the relative translations and tie points. Tie points within triplets are adopted to firstly calculate global unified scale factors for each available pairwise relative translation. Then, analogous to rotation estimation, translations are determined by performing an averaging operation on the scaled relative translations. In order to improve the robustness of the solution, efforts in this thesis are also focused on coping with outliers in the relative orientations (ROs), which global image orientation approaches are particularly sensitive to. A general method based on triplet compatibility with respect to loop closure errors of relative rotations and translations is presented for detecting blunders in relative orientations. Although this procedure eliminated many gross errors in the input ROs, it typically cannot sort out blunders which are caused by repetitive structures and critical configurations, such as inappropriate baselines (very short baseline or baselines parallel to the viewing direction). Therefore, another new method is proposed to eliminate wrong ROs which have resulted from repetitive structures and very short baselines. Two corresponding criteria that indicate the quality of ROs are introduced. Repetitive structure is detected based on counts of conjugate points of the various image pairs, while very short baselines are found by inspecting the intersection angles of corresponding image rays. By analyzing these two criteria, incorrect ROs are detected and eliminated. As correct ROs of image pairs with a wider baseline nearly parallel to both viewing directions can be valuable, a method to identify and keep these ROs is also a part of this research. The validation and evaluation of the proposed method are thoroughly conducted on various benchmarks including ordered and unordered sets of images, images with repetitive structures and inappropriate baselines, etc. In particular, robustness is investigated by demonstrating the efficacy of the corresponding RO outlier detection methods. The performance and time efficiency of determining image orientation are evaluated and compared with several state-of-the-art global image orientation approaches. In summary, based on the experimental results, the developed methods demonstrateto be able to accomplish the image orientation taskfast and robustlyon different kinds of datasets. Numéro de notice : 17672 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD dissertation : Fachrichtung Geodäsie und Geoinformatik : Hanovre : 2021 En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-871.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97997

Behavior-based location recommendation on location-based social networks / Seyyed Mohammadreza Rahimi in Geoinformatica [en ligne], vol 24 n° 3 (July 2020)  

Public [article]  inGeoinformatica [en ligne] > vol 24 n° 3 (July 2020) . - pp 477 – 504 Titre : Behavior-based location recommendation on location-based social networks Type de document : Article/Communication Auteurs : Seyyed Mohammadreza Rahimi, Auteur ; Behrouz Far, Auteur ; Xin Wang, Auteur Année de publication : 2020 Article en page(s) : pp 477 – 504 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse spatiale [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] interface web [Termes IGN] modèle conceptuel de données localisées [Termes IGN] réseau social géodépendant [Termes IGN] système de recommandation Résumé : (auteur) Location recommendation methods on location-based social networks (LBSN) discover the locational preference of users along with their spatial movement patterns from users’ check-ins and provide users with recommendations of unvisited places. The growing popularity of LBSNs and abundance of shared location information has made location recommendation an active research area in the recent years. However, the existing methods suffer from one or more deficiencies such as data sparsity, cold-start users, ignoring users’ specific spatial and temporal behaviors, not utilizing the shared behaviors of the users. In this paper, we propose a novel location recommendation method, namely Behavior-based Location Recommendation (BLR). BLR recommends a location to a user based on the users’ repetitive behaviors and behaviors of similar users. Additionally, to better integrate the spatial information, BLR has two spatial components, a user-based spatial component to find the spatial preferences of the user, and a behavior-based spatial component to find locations of interest for different behaviors. Experimental studies on three real-world datasets show that BLR produces better location recommendations and can effectively address data sparsity and cold-start problems. Numéro de notice : A2020-370 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-019-00360-3 date de publication en ligne : 25/05/2019 En ligne : https://doi.org/10.1007/s10707-019-00360-3 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95265 [article]

Robust structure from motion based on relative rotations and tie points / Xin Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 5 (May 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 5 (May 2019) . - pp 347 - 359 Titre : Robust structure from motion based on relative rotations and tie points Type de document : Article/Communication Auteurs : Xin Wang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 347 - 359 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] compensation locale par faisceaux [Termes IGN] équation linéaire [Termes IGN] orientation relative [Termes IGN] point de liaison (imagerie) [Termes IGN] rotation [Termes IGN] structure-from-motion Résumé : (Auteur) In this article, we present two new approaches for image orientation with a focus on robustness, starting with relative orientations of available image pairs, an incremental and a global one, and compare their performance. For the incremental approach, we first choose a suitable initial image pair, and we then iteratively extend the image cluster by adding new images. The rotations of these newly added images are estimated from relative rotations by single rotation averaging. In the next step, a linear equation system is set up for each new image to solve the translation parameters with triangulated tie points that can be viewed in that new image, followed by a resection for refinement. Finally, we refine the orientation parameters of the images by a local bundle adjustment. We also present a global method that consists of two parts: global rotation averaging, followed by setting up a large linear equation system to solve for all image translation parameters simultaneously; a final bundle adjustment is carried out to refine the results. We compare these two methods by analyzing results on different benchmark sets, including ordered and unordered image data sets from the Internet and two other challenging data sets to demonstrate the performance of our two approaches. We conclude that while the incremental method typically yields results of higher accuracy and performs better on the challenging data sets, our global method runs significantly faster. Numéro de notice : A2019-438 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.5.347 date de publication en ligne : 01/05/2019 En ligne : https://doi.org/10.14358/PERS.85.5.347 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92769 [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 IGN] appariement d'images [Termes IGN] chaîne de traitement [Termes IGN] classification barycentrique [Termes IGN] classification par forêts aléatoires [Termes IGN] compensation par faisceaux [Termes IGN] estimation de pose [Termes IGN] image captée par drone [Termes IGN] matrice de rotation [Termes IGN] orientation relative [Termes IGN] Ransac (algorithme) [Termes IGN] recouvrement d'images [Termes IGN] SIFT (algorithme) [Termes IGN] structure-from-motion [Termes 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 Affiliation des auteurs : non IGN 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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