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Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior / Maximilian Alexander Coenen in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 27 - 47 Titre : Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior Type de document : Article/Communication Auteurs : Maximilian Alexander Coenen, Auteur ; Franz Rottensteiner, Auteur Année de publication : 2021 Article en page(s) : pp 27 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] estimation de pose [Termes IGN] modèle stochastique [Termes IGN] problème inverse [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] robotique [Termes IGN] véhicule automobile [Termes IGN] vision par ordinateur Résumé : (auteur) The 3D reconstruction of objects is a prerequisite for many highly relevant applications of computer vision such as mobile robotics or autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstruction approach by establishing a 3D model and aligning it to the 2D image plane. However, current approaches are limited due to inadequate shape priors and the insufficiency of the derived image observations for a reliable alignment with the 3D model. The goal of this paper is to show how 3D object reconstruction can profit from a more sophisticated shape prior and from a combined incorporation of different observation types inferred from the images. We introduce a subcategory-aware deformable vehicle model that makes use of a prediction of the vehicle type for a more appropriate regularisation of the vehicle shape. A multi-branch CNN is presented to derive predictions of the vehicle type and orientation. This information is also introduced as prior information for model fitting. Furthermore, the CNN extracts vehicle keypoints and wireframes, which are well-suited for model-to-image association and model fitting. The task of pose estimation and reconstruction is addressed by a versatile probabilistic model. Extensive experiments are conducted using two challenging real-world data sets on both of which the benefit of the developed shape prior can be shown. A comparison to state-of-the-art methods for vehicle pose estimation shows that the proposed approach performs on par or better, confirming the suitability of the developed shape prior and probabilistic model for vehicle reconstruction. Numéro de notice : A2021-772 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.006 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98829 [article]

Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([04/10/2021])  

Public [article]  inGeocarto international > vol 36 n° 18 [04/10/2021] . - pp 2065 - 2084 Titre : Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification Type de document : Article/Communication Auteurs : Ming Cong, Auteur ; Zhiye Wang, Auteur ; Yiting Tao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2065 - 2084 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] chromatopsie [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] échantillonnage d'image [Termes IGN] filtrage numérique d'image [Termes IGN] image captée par drone [Termes IGN] vision [Termes IGN] vision par ordinateur Résumé : (auteur) Unmanned aerial vehicle remote sensing images need to be precisely and efficiently classified. However, complex ground scenes produced by ultra-high ground resolution, data uniqueness caused by multi-perspective observations, and need for manual labelling make it difficult for current popular deep learning networks to obtain reliable references from heterogeneous samples. To address these problems, this paper proposes an optic nerve microsaccade (ONMS) classification network, developed based on multiple dilated convolution. ONMS first applies a Laplacian of Gaussian filter to find typical features of ground objects and establishes class labels using adaptive clustering. Then, using an image pyramid, multi-scale image data are mapped to the class labels adaptively to generate homologous reliable samples. Finally, an end-to-end multi-scale neural network is applied for classification. Experimental results show that ONMS significantly reduces sample labelling costs while retaining high cognitive performance, classification accuracy, and noise resistance—indicating that it has significant application advantages. Numéro de notice : A2021-707 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2019.1687593 Date de publication en ligne : 07/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98602 [article]

3D map creation using crowdsourced GNSS data / Terence Lines in Computers, Environment and Urban Systems, vol 89 (September 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 89 (September 2021) Titre : 3D map creation using crowdsourced GNSS data Type de document : Article/Communication Auteurs : Terence Lines, Auteur ; Anahid Basiri, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] approche participative [Termes IGN] Bootstrap (statistique) [Termes IGN] cartographie 3D [Termes IGN] données GNSS [Termes IGN] données localisées 2,5D [Termes IGN] hauteur du bâti [Termes IGN] interface de programmation [Termes IGN] régression logistique [Termes IGN] signal GNSS [Termes IGN] trajet multiple [Termes IGN] vision par ordinateur Résumé : (auteur) 3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard. Numéro de notice : A2021-535 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101671 Date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101671 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97998 [article]

GIScience integrated with computer vision for the examination of old engravings and drawings / Motti Zohar in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 9 (September 2021) . - pp 1703 - 1724 Titre : GIScience integrated with computer vision for the examination of old engravings and drawings Type de document : Article/Communication Auteurs : Motti Zohar, Auteur ; Ilan Shimshoni, Auteur Année de publication : 2021 Article en page(s) : pp 1703 - 1724 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte en 3D [Termes IGN] carte profonde [Termes IGN] dessin [Termes IGN] extraction de données [Termes IGN] Israël [Termes IGN] Jérusalem [Termes IGN] patrimoine culturel [Termes IGN] paysage [Termes IGN] Ransac (algorithme) [Termes IGN] système d'information géographique [Termes IGN] vision par ordinateur Résumé : (auteur) Landscape reconstructions and deep maps are two major approaches in cultural heritage studies. In general, they require the use of historical visual sources such as maps, graphic artworks, and photographs presenting areal scenes, from which one can extract spatial information. However, photographs, the most accurate and reliable source for scenery reconstruction, are available only from the second half of the 19th century onward. Thus, for earlier periods one can rely only on old artworks. Nevertheless, the accuracy and inclusiveness of old artworks are often questionable and must be verified carefully. In this paper, we use GIScience methods with computer-vision capabilities to interrogate old engravings and drawings as well as to develop a new approach for extracting spatial information from these scenic artworks. We have inspected four old depictions of Jerusalem and Tiberias (Israel) created between the 17th and 19th centuries. Using visibility analysis and a RANSAC algorithm we identified the locations of the artists when they drew the artworks and evaluated the accuracy of their final products. Finally, we re-projected 3D map digitized features onto the drawing canvases, thus embedding features not originally drawn. These were then identified, enabling potential extraction of the spatial information they may reflect. Numéro de notice : A2021-592 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1874957 Date de publication en ligne : 25/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1874957 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98213 [article]

A shape transformation-based dataset augmentation framework for pedestrian detection / Zhe Chen in International journal of computer vision, vol 129 n° 4 (April 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 4 (April 2021) . - pp 1121 - 1138 Titre : A shape transformation-based dataset augmentation framework for pedestrian detection Type de document : Article/Communication Auteurs : Zhe Chen, Auteur ; Wanli Ouyang, Auteur ; Tongliang Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1121 - 1138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage par transformation [Termes IGN] apprentissage profond [Termes IGN] déformation d'objet [Termes IGN] détection de piéton [Termes IGN] jeu de données [Termes IGN] synthèse d'image [Termes IGN] vision par ordinateur Résumé : (auteur) Deep learning-based computer vision is usually data-hungry. Many researchers attempt to augment datasets with synthesized data to improve model robustness. However, the augmentation of popular pedestrian datasets, such as Caltech and Citypersons, can be extremely challenging because real pedestrians are commonly in low quality. Due to the factors like occlusions, blurs, and low-resolution, it is significantly difficult for existing augmentation approaches, which generally synthesize data using 3D engines or generative adversarial networks (GANs), to generate realistic-looking pedestrians. Alternatively, to access much more natural-looking pedestrians, we propose to augment pedestrian detection datasets by transforming real pedestrians from the same dataset into different shapes. Accordingly, we propose the Shape Transformation-based Dataset Augmentation (STDA) framework. The proposed framework is composed of two subsequent modules, i.e. the shape-guided deformation and the environment adaptation. In the first module, we introduce a shape-guided warping field to help deform the shape of a real pedestrian into a different shape. Then, in the second stage, we propose an environment-aware blending map to better adapt the deformed pedestrians into surrounding environments, obtaining more realistic-llooking pedestrians and more beneficial augmentation results for pedestrian detection. Extensive empirical studies on different pedestrian detection benchmarks show that the proposed STDA framework consistently produces much better augmentation results than other pedestrian synthesis approaches using low-quality pedestrians. By augmenting the original datasets, our proposed framework also improves the baseline pedestrian detector by up to 38% on the evaluated benchmarks, achieving state-of-the-art performance. Numéro de notice : A2021-354 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s11263-020-01412-0 Date de publication en ligne : 09/01/2021 En ligne : https://doi.org/10.1007/s11263-020-01412-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97606 [article]

A skyline-based approach for mobile augmented reality / Mehdi Ayadi in The Visual Computer, vol 37 n° 4 (April 2021)  

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Visual positioning in indoor environments using RGB-D images and improved vector of local aggregated descriptors / Longyu Zhang in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)  

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Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios / Xiao Ke in Machine Vision and Applications, vol 32 n° 2 (March 2021)  

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Unsupervised deep representation learning for real-time tracking / Ning Wang in International journal of computer vision, vol 129 n° 2 (February 2021)  

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Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control / Florence Carton (2021)  

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Geometric computer vision: omnidirectional visual and remotely sensed data analysis / Pouria Babahajiani (2021)  

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Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

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A novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

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Water level prediction from social media images with a multi-task ranking approach / P. Chaudhary in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

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Indoor positioning using PnP problem on mobile phone images / Hana Kubickova in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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