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Dynamic human body reconstruction and motion tracking with low-cost depth cameras / Kangkan Wang in The Visual Computer, vol 37 n° 3 (March 2021)
[article]
Titre : Dynamic human body reconstruction and motion tracking with low-cost depth cameras Type de document : Article/Communication Auteurs : Kangkan Wang, Auteur ; Guofeng Zhang, Auteur ; Jian Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 603 - 618 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de formes
[Termes IGN] déformation de projection
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] filtrage spatiotemporel
[Termes IGN] maillage
[Termes IGN] modèle dynamique
[Termes IGN] modélisation 3D
[Termes IGN] objet mobile
[Termes IGN] reconstruction d'objet
[Termes IGN] squelettisationRésumé : (auteur) We present a novel approach for dynamic human body reconstruction and motion tracking using low-cost depth cameras. Our reconstruction system is able to produce a sequence of dynamic 3D human body models from the noisy input depth data. To accurately align the template model with noisy input data, we combine skeleton-driven deformation and mesh deformation techniques to enhance the registration robustness to depth missing, occlusions, and severe noise. In addition, a novel data-driven 3D human body model is introduced to efficiently reconstruct human body models with wide shape and pose variations only using a limited number of training databases with standard standing pose. We perform quantitative and qualitative experiments to evaluate our method and compare it with other methods for body reconstruction on both synthetic and real datasets. Experimental results demonstrate the effectiveness of the proposed approach. Numéro de notice : A2021-341 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01826-4 Date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1007/s00371-020-01826-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97579
in The Visual Computer > vol 37 n° 3 (March 2021) . - pp 603 - 618[article]Feature detection and description for image matching: from hand-crafted design to deep learning / Lin Chen in Geo-spatial Information Science, vol 24 n° 1 (March 2021)
[article]
Titre : Feature detection and description for image matching: from hand-crafted design to deep learning Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2021 Article en page(s) : pp 58 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement automatique
[Termes IGN] appariement d'images
[Termes IGN] appariement de formes
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image aérienne oblique
[Termes IGN] orientation d'image
[Termes IGN] SIFT (algorithme)Résumé : (Auteur) In feature based image matching, distinctive features in images are detected and represented by feature descriptors. Matching is then carried out by assessing the similarity of the descriptors of potentially conjugate points. In this paper, we first shortly discuss the general framework. Then, we review feature detection as well as the determination of affine shape and orientation of local features, before analyzing feature description in more detail. In the feature description review, the general framework of local feature description is presented first. Then, the review discusses the evolution from hand-crafted feature descriptors, e.g. SIFT (Scale Invariant Feature Transform), to machine learning and deep learning based descriptors. The machine learning models, the training loss and the respective training data of learning-based algorithms are looked at in more detail; subsequently the various advantages and challenges of the different approaches are discussed. Finally, we present and assess some current research directions before concluding the paper. Numéro de notice : A2021-297 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1843376 Date de publication en ligne : 17/11/2020 En ligne : https://doi.org/10.1080/10095020.2020.1843376 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97379
in Geo-spatial Information Science > vol 24 n° 1 (March 2021) . - pp 58 - 74[article]Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions / Xin-Ming Zhu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
[article]
Titre : Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions Type de document : Article/Communication Auteurs : Xin-Ming Zhu, Auteur ; Xiao-Ning Song, Auteur ; Pei Leng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2680 - 2697 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Chine
[Termes IGN] correction atmosphérique
[Termes IGN] hétérogénéité spatiale
[Termes IGN] image Landsat-8
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] température au sol
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi’an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases. Numéro de notice : A2021-219 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3002821 Date de publication en ligne : 26/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3002821 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97211
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2680 - 2697[article]Learning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
[article]
Titre : Learning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery Type de document : Article/Communication Auteurs : Ju Zhang, Auteur ; Qingwu Hu, Auteur ; Jiayuan Li, Auteur ; Mingyao Ai, Auteur Année de publication : 2021 Article en page(s) : pp 1836 - 1847 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] extraction du réseau routier
[Termes IGN] image à haute résolution
[Termes IGN] rastérisation
[Termes IGN] segmentation d'image
[Termes IGN] trace GPS
[Termes IGN] trace numérique
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] Wuhan (Chine)
[Termes IGN] zone urbaineRésumé : (Auteur) Deep learning has achieved great success in recent years, among which the convolutional neural network (CNN) method is outstanding in image segmentation and image recognition. It is also widely used in satellite imagery road extraction and, generally, can obtain accurate and extraction results. However, at present, the extraction of roads based on CNN still requires a lot of manual preparation work, and a large number of samples can be marked to achieve extraction, which has to take long drawing cycle and high production cost. In this article, a new CNN sample set production method is proposed, which uses the GPS trajectories of floating car as training set (GPSTasST), for the multilevel urban roads extraction from high-resolution remote sensing imagery. This method rasterizes the GPS trajectories of floating car into a raster map and uses the processed raster map to label the satellite image to obtain a road extraction sample set. CNN can extract roads from remote sensing imagery by learning the training set. The results show that the method achieves a harmonic mean of precision and recall higher than road extraction method from single data source while eliminating the manual labeling work, which shows the effectiveness of this work. Numéro de notice : A2021-211 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3003425 Date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3003425 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97196
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 1836 - 1847[article]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)
[article]
Titre : Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios Type de document : Article/Communication Auteurs : Xiao Ke, Auteur ; Xinru Lin, Auteur ; Liyun Qin, Auteur Année de publication : 2021 Article en page(s) : n° 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] comportement
[Termes IGN] détection de piéton
[Termes IGN] objet mobile
[Termes IGN] reconnaissance de formes
[Termes IGN] vision par ordinateurRésumé : (auteur) Pedestrian detection and re-identification technology is a research hotspot in the field of computer vision. This technology currently has issues such as insufficient pedestrian expression ability, occlusion, diverse pedestrian attitude, and difficulty of small-scale pedestrian detection. In this paper, we proposed an end-to-end pedestrian detection and re-identification model in real scenes, which can effectively solve these problems. In our model, the original images are processed with a non-overlapped image blocking data augmentation method, and then input them into the YOLOv3 detector to obtain the object position information. LCNN-based pedestrian re-identification model is used to extract the features of the object. Furthermore, the eigenvectors of the object and the detected pedestrians are calculated, and the similarity between them are used to determine whether they can be marked as target pedestrians. Our method is lightweight and end-to-end, which can be applied to the real scenes. Numéro de notice : A2021-455 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01169-7 Date de publication en ligne : 24/02/2021 En ligne : https://doi.org/10.1007/s00138-021-01169-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97900
in Machine Vision and Applications > vol 32 n° 2 (March 2021) . - n° 46[article]Multi-level progressive parallel attention guided salient object detection for RGB-D images / Zhengyi Liu in The Visual Computer, vol 37 n° 3 (March 2021)PermalinkPan-sharpening via multiscale dynamic convolutional neural network / Jianwen Hu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkRecognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)PermalinkUsing geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs / Roholah Yazdan in Geomatica, vol 75 n° 1 (Mars 2021)PermalinkActivity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)PermalinkCorrentropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing / Xiaorun Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkDeep traffic light detection by overlaying synthetic context on arbitrary natural images / Jean Pablo Vieira de Mello in Computers and graphics, vol 94 n° 1 (February 2021)PermalinkFully convolutional neural network for impervious surface segmentation in mixed urban environment / Joseph McGlinchy in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)PermalinkGTP-PNet: A residual learning network based on gradient transformation prior for pansharpening / Hao Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkSemi-supervised joint learning for hand gesture recognition from a single color image / Chi Xu in Sensors, vol 21 n° 3 (February 2021)Permalink