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Auteur Qian (Chayn) Sun |
Documents disponibles écrits par cet auteur (2)
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A GAN-based approach toward architectural line drawing colorization prototyping / Qian (Chayn) Sun in The Visual Computer, vol 38 n° 4 (April 2022)
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Titre : A GAN-based approach toward architectural line drawing colorization prototyping Type de document : Article/Communication Auteurs : Qian (Chayn) Sun, Auteur ; Yan Chen, Auteur ; Wenyuan Tao, Auteur ; Han Jiang, Auteur ; Mu Zhang, Auteur ; Kan Chen, Auteur ; Marius Erdt, Auteur Année de publication : 2022 Article en page(s) : pp 1283 - 1300 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] architecture
[Termes IGN] bâtiment
[Termes IGN] couleur (variable spectrale)
[Termes IGN] prototype
[Termes IGN] réseau antagoniste génératif
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Line drawing with colorization is a popular art format and tool for architectural illustration. The goal of this research is toward generating a high-quality and natural-looking colorization based on an architectural line drawing. This paper presents a new Generative Adversarial Network (GAN)-based method, named ArchGANs, including ArchColGAN and ArchShdGAN. ArchColGAN is a GAN-based line-feature-aware network for stylized colorization generation. ArchShdGAN is a lighting effects generation network, from which the building depiction in 3D can benefit. In particular, ArchColGAN is able to maintain the important line features and the correlation property of building parts as well as reduce the uneven colorization caused by sparse lines. Moreover, we proposed a color enhancement method to further improve ArchColGAN. Besides the single line drawing images, we also extend our method to handle line drawing image sequences and achieve rotation animation. Experiments and studies demonstrate the effectiveness and usefulness of our proposed method for colorization prototyping. Numéro de notice : A2022-154 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s00371-021-02219-x Date de publication en ligne : 23/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02219-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100292
in The Visual Computer > vol 38 n° 4 (April 2022) . - pp 1283 - 1300[article]Detecting and mapping traffic signs from Google Street View images using deep learning and GIS / Andrew Campbell in Computers, Environment and Urban Systems, vol 77 (september 2019)
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Titre : Detecting and mapping traffic signs from Google Street View images using deep learning and GIS Type de document : Article/Communication Auteurs : Andrew Campbell, Auteur ; Alan Both, Auteur ; Qian (Chayn) Sun, Auteur ; Qian (Chayn) Sun, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] apprentissage profond
[Termes IGN] base de données routières
[Termes IGN] détection d'objet
[Termes IGN] feu de circulation
[Termes IGN] gestion de trafic
[Termes IGN] image Streetview
[Termes IGN] réseau routier
[Termes IGN] signalisation routière
[Termes IGN] système d'information géographique
[Termes IGN] trafic routier
[Termes IGN] vision par ordinateurRésumé : (auteur) Street traffic sign infrastructure remains an extremely difficult asset for local government to manage due to its diverse physical structure and geographical distribution. A spatial registrar of traffic infrastructure is currently a required component of local government councils' mandatory road management plans. Recent advancements of object detection technology in machine learning have presented an automated approach for the detection and classification of street signage captured by Google's Street View (GSV) imagery. This paper explores the possibility of using deep learning to produce an autonomous system for detecting traffic signs on GSV images to assist in traffic assets monitoring and maintenance. By leveraging Google's Street View API, this research offers an economic approach of building purposeful street sign computer vision datasets. A custom object detection model was trained to detect and classify Stop and Give Way signs from images captured at intersection approaches. Considering the output detected bounding box coordinates, photogrammetry approach was applied to calculate the approximate location of each detected sign in two-dimensional geographical space. The newly located and classified street signs can be combined with relevant spatial data for implementation into an asset management system. By combining GIS and the GSV API, the process is completely scalable to any level of street sign classification scope. The experiments conducted on the road network of study area recorded a detection accuracy of 95.63% and classification accuracy of 97.82%. Our proposed automated approach to the detection and localisation of street sign infrastructure has displayed a promising potential for its use by local government authorities. Our workflow can be used to detect other traffic signs and applied to other road sections and other cities. Of primary importance, this approach takes an entirely free and open-source approach throughout. The continuation of Google's Street View program will account for the spatiotemporal representation of street sign infrastructure for the ongoing maintenance and renewal programs of this valuable asset. Numéro de notice : A2019-412 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2019.101350 Date de publication en ligne : 07/06/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101350 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93536
in Computers, Environment and Urban Systems > vol 77 (september 2019)[article]