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Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency / Changjiang Liu in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 16 - 26 Titre : Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency Type de document : Article/Communication Auteurs : Changjiang Liu, Auteur ; Irene Cheng, Auteur ; Yi Zhang, Auteur ; Anup Basu, Auteur Année de publication : 2017 Article en page(s) : pp 16 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation d'image [Termes IGN] amélioration du contraste [Termes IGN] analyse multiéchelle [Termes IGN] cohérence des couleurs [Termes IGN] histogramme [Termes IGN] image aérienne [Termes IGN] visibilité [Termes IGN] visualisation 2D Résumé : (Auteur) This paper presents an improved multi-scale Retinex (MSR) based enhancement for aerial images under low visibility. For traditional multi-scale Retinex, three scales are commonly employed, which limits its application scenarios. We extend our research to a general purpose enhanced method, and design an MSR with more than three scales. Based on the mathematical analysis and deductions, an explicit multi-scale representation is proposed that balances image contrast and color consistency. In addition, a histogram truncation technique is introduced as a post-processing strategy to remap the multi-scale Retinex output to the dynamic range of the display. Analysis of experimental results and comparisons with existing algorithms demonstrate the effectiveness and generality of the proposed method. Results on image quality assessment proves the accuracy of the proposed method with respect to both objective and subjective criteria. Numéro de notice : A2017-330 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.02.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85483 [article]

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081-2017061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017063	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2017062	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering / Yi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 120 (october 2016) . - pp 25 - 36 Titre : The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering Type de document : Article/Communication Auteurs : Yi Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 25 - 36 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse en composantes principales [Termes IGN] arbre BSP [Termes IGN] classification floue [Termes IGN] densité des points [Termes IGN] semis de points [Termes IGN] traitement de semis de points [Termes IGN] valeur propre Résumé : (Auteur) The spatial partitioning of massive point cloud data involves dividing the space into a multi-tree structure step by step, so as to achieve the purpose of fast access and to render the point cloud. The current methods are based on spatial regularity and equal division, which is not consistent with the irregular and non-uniform distribution of most point clouds. This paper presents a directional fuzzy c-means (D-FCM) method for irregular spatial partitioning. The distance metric is weighted by a direction coefficient, which is determined by the eigenvalue of the point cloud. The orientation of each node is adaptively calculated by principal component analysis of the point cloud, and Karhunen-Loeve (KL) transform is applied to the points coordinates in node. A binary space partitioning (BSP) tree structure is used to partition the point cloud data node by node, and a directional BSP (D-BSP) tree is formed. The D-BSP tree structure was tested with point clouds of 0.1 million to over 2 billion points (up to 60 GB). The experimental results showed that the D-BSP tree can ensure that the bounding boxes are close to the actual spatial distribution of the point cloud, it can completely expand along the spatial configuration of the point cloud without generating unnecessary partitioning, and it can achieve a higher rendering speed with less memory requirement. Numéro de notice : A2016-795 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.08.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82529 [article]