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Improved depth estimation for occlusion scenes using a light-field camera / Changkun Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 7 (July 2020) . - pp 443-456 Titre : Improved depth estimation for occlusion scenes using a light-field camera Type de document : Article/Communication Auteurs : Changkun Yang, Auteur ; Zhaoqin Liu, Auteur ; Kaichang Di, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 443-456 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] caméra numérique [Termes IGN] classification pixellaire [Termes IGN] détection de partie cachée [Termes IGN] disparité [Termes IGN] effet de profondeur cinétique [Termes IGN] lentille [Termes IGN] méthode de centrage [Termes IGN] rayonnement lumineux [Termes IGN] reconstruction 3D Résumé : (Auteur) With the development of light-field imaging technology, depth estimation using light-field cameras has become a hot topic in recent years. Even through many algorithms have achieved good performance for depth estimation using light-field cameras, removing the influence of occlusion, especially multi-occlusion, is still a challenging task. The photo-consistency assumption does not hold in the presence of occlusions, which makes most depth estimation of light-field imaging unreliable. In this article, a novel method to handle complex occlusion in depth estimation of light-field imaging is proposed. The method can effectively identify occluded pixels using a refocusing algorithm, accurately select unoccluded views using the adaptive unoccluded-view identification algorithm, and then improve the depth estimation by computing the cost volumes in the unoccluded views. Experimental results demonstrate the advantages of our proposed algorithm compared with conventional state-of-the art algorithms on both synthetic and real light-field data sets. Numéro de notice : A2020-383 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.7.443 Date de publication en ligne : 01/07/2020 En ligne : https://doi.org/10.14358/PERS.86.7.443 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95430 [article]

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Improved camera distortion correction and depth estimation for lenslet light field camera / Changkun Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 3 (March 2019) . - pp 197 - 208 Titre : Improved camera distortion correction and depth estimation for lenslet light field camera Type de document : Article/Communication Auteurs : Changkun Yang, Auteur ; Zhaoqin Liu, Auteur ; Kaichang Di, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 197 - 208 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] caméra numérique [Termes IGN] carte de profondeur [Termes IGN] correction géométrique [Termes IGN] distorsion d'image [Termes IGN] modèle géométrique de prise de vue Résumé : (Auteur) A light field camera can capture both radiance and angular information, providing a novel solution for depth estimation. The paper proposes two improved methods including distortion model optimization and depth estimation refinement for a lenslet light field camera. For distortion model optimization, a novel 14-parameter distortion model that involves sub-aperture images generation is applied to correct the light field camera images. For depth estimation refinement, an algorithm reducing the high influence of outliers on depth estimation in weak texture regions is proposed based on multi-view stereo matching using the cost volume. Experimental results show the projection error has decreased by about 30% and the depth root-mean-squared error on real world images has decreased by about 42% with our distortion correction method and depth estimation method compared with state of art algorithms. It verifies the correctness and effectiveness of our proposed methods and show significant improvement on accuracy of depth map estimation. Numéro de notice : A2019-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.3.197 Date de publication en ligne : 01/03/2019 En ligne : https://doi.org/10.14358/PERS.85.3.197 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92477 [article]

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105-2019031	SL	Revue	Centre de documentation	Revues en salle	Disponible