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A learning-based approach to automatically evaluate the quality of sequential color schemes for maps / Taisheng Chen in Cartography and Geographic Information Science, Vol 48 n° 5 (September 2021)
Titre : A learning-based approach to automatically evaluate the quality of sequential color schemes for maps Type de document : Article/Communication Auteurs : Taisheng Chen, Auteur ; Menglin Chen, Auteur ; A - Xing Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 377-392 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique
[Termes IGN] amélioration des couleurs
[Termes IGN] apprentissage automatique
[Termes IGN] charte de couleurs
[Termes IGN] cohérence des couleurs
[Termes IGN] contraste de couleurs
[Termes IGN] couleur (rédaction cartographique)
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] palette de couleurs
[Termes IGN] saturation de la couleur
[Termes IGN] visualisation cartographiqueRésumé : (auteur) Color quality evaluation is key to judging map quality, which can improve data visualization and communication. However, most existing methods for evaluating map colors are tedious and subjective manual methods. In this paper, we study sequential color schemes, a widely used map color type and propose a learning-based approach for evaluating the color quality. The approach consists of two steps. First, we extract and characterize the cartographic factors for determining the quality of sequential color schemes, such as color order, color match, color harmony, color discrimination and color uniformity. Second, we present a model to predict the color quality based on AdaBoost, a type of ensemble learning algorithm with excellent classification performance and use these factors as input data. We conduct a case study based on 781 samples and train the AdaBoost-based model to predict the quality of sequential color schemes. To evaluate the model’s performance, we calculated the area under the receiver operating characteristic (ROC) curve (AUC). The AUC values are 0.983 and 0.977 on the training data and testing data, respectively. These results indicate that the proposed approach can be used to automatically evaluate the quality of sequential color schemes for maps, which helps mapmakers select good colors. Numéro de notice : A2021-642 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1936184 Date de publication en ligne : 29/06/2021 En ligne : https://doi.org/10.1080/15230406.2021.1936184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98335
in Cartography and Geographic Information Science > Vol 48 n° 5 (September 2021) . - pp 377-392[article]
Titre : Remote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space Type de document : Article/Communication Auteurs : Min Wu, Auteur ; Xin Jin, Auteur ; Qian Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1707 - 1729 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] contraste de couleurs
[Termes IGN] données multiéchelles
[Termes IGN] image en couleur
[Termes IGN] image RVB
[Termes IGN] niveau de gris (image)
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Image colorization technique is used to colorize the gray-level image or single-channel image, which is a very significant and challenging task in image processing, especially the colorization of remote sensing images. This paper proposes a new method for coloring remote sensing images based on deep convolution generation adversarial network. The adopted generator model is a symmetrical structure using the principle of auto-encoder, and a multi-scale convolutional module is specially designed to introduce into the generator model. Thus, the proposed generator can enable the whole model to retain more image features in the process of up-sampling and down-sampling. Meanwhile, the discriminator uses residual neural network 18 that can compete with the generator, so that the generator and discriminator can effectively optimize each other. In the proposed method, the color space transformation technique is first utilized to convert remote sensing images from RGB to YUV. Then, the Y channel (a gray-level image) is used as the input of the neural network model to predict UV channels. Finally, the predicted UV channels are concatenated with the original Y channel as a whole YUV that is then transformed into RGB space to get the final color image. Experiments are conducted to test the performance of different image colorization methods, and the results show that the proposed method has good performance in both visual quality and objective indexes on the colorization of remote sensing image. Numéro de notice : A2021-540 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01933-2 Date de publication en ligne : 28/08/2020 En ligne : https://doi.org/10.1007/s00371-020-01933-2 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98018
in The Visual Computer > vol 37 n° 7 (July 2021) . - pp 1707 - 1729[article]
Titre : Optical Coherence Tomography and Its Non-medical Applications Type de document : Monographie Auteurs : Michael R. Wang, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 226 p. Format : 17 x 23 cm ISBN/ISSN/EAN : 978-1-83880-801-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chatoiement
[Termes IGN] contraste de couleurs
[Termes IGN] déformation d'image
[Termes IGN] empreinte
[Termes IGN] image 3D
[Termes IGN] image à haute résolution
[Termes IGN] métrologie
[Termes IGN] tomographieRésumé : (éditeur) Optical coherence tomography (OCT) is a promising non-invasive non-contact 3D imaging technique that can be used to evaluate and inspect material surfaces, multilayer polymer films, fiber coils, and coatings. OCT can be used for the examination of cultural heritage objects and 3D imaging of microstructures. With subsurface 3D fingerprint imaging capability, OCT could be a valuable tool for enhancing security in biometric applications. OCT can also be used for the evaluation of fastener flushness for improving aerodynamic performance of high-speed aircraft. More and more OCT non-medical applications are emerging. In this book, we present some recent advancements in OCT technology and non-medical applications. Note de contenu : 1- Dynamic Range Enhancement in Swept-Source Optical Coherence Tomography
2- Multi-Frame Super resolution Optical Coherence Tomography for High Lateral Resolution 3D Imaging
3- OCT in Applications That Involve the Measurement of Large Dimensions
4- Low Cost Open-Source OCT Using Undergraduate Lab Components
5- Optical Coherence Tomography for Polymer Film Evaluation
6- Fouling Monitoring in Membrane Filtration Systems
7- Non destructive Characterization of Drying Processes of Colloidal Droplets and Latex Coats Using Optical Coherence Tomography
8- OCT for Examination of Cultural Heritage Objects
9- Quantitative Mapping of Strainsand Young Modulus Based on Phase-Sensitive OCT
10- OCT with a Visible Broadband Light Source Applied to High-Resolution Non destructive Inspection for Semiconductor Optical Devices
11- Optical Coherence Tomography for Non-Contact Evaluation of Fastener FlushnessNuméro de notice : 28530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.81767 En ligne : http://doi.org/10.5772/intechopen.81767 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97350
Titre : A perception-based color recommendation algorithm for hierarchical regions Type de document : Article/Communication Auteurs : Shipeng Sun, Auteur Année de publication : 2015 Article en page(s) : pp 259 - 270 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] carte thématique
[Termes IGN] contraste de couleurs
[Termes IGN] couleur (rédaction cartographique)
[Termes IGN] lisibilité perceptive
[Termes IGN] visualisation cartographiqueRésumé : (auteur) The visualization of hierarchical and nested spatial regions remains a challenge to cartographers. Despite progress in computer algorithms for visualizing general hierarchical data, mapping spatial hierarchical regions, especially with static, noninteractive means, still requires considerable manual efforts. This paper proposes a two-step algorithm that can automatically recommend perception-based colors and help reveal the hierarchical structure embedded in nested regions. It first systematically sorts regions according to their contiguity and containment relations at multiple hierarchical levels. Then, perception-based colors are generated using the order of regions with the goal of maximizing differentiability between top-level regions while retaining the perceived uniformity of the bottom-level regions. With the coloring scheme recommended by this algorithm, the metric color differences among regions mathematically reflect their hierarchical positions and spatial relations. The resultant colors, therefore, can potentially help map-readers perceive the spatially constrained hierarchy structure built in nested regions. Numéro de notice : A2015-243 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2014.991424 En ligne : https://doi.org/10.1080/15230406.2014.991424 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76241
in Cartography and Geographic Information Science > Vol 42 n° 3 (July 2015) . - pp 259 - 270[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2015031 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Coastal digital surface model on low contrast images Type de document : Article/Communication Auteurs : Ana-Maria Rosu, Auteur ; Michel Assenbaum, Auteur ; Ywenn de La Torre, Auteur ; Marc Pierrot-Deseilligny Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2015 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 40-3/W3 Conférence : ISPRS 2015, Geospatial Week : Laserscanning, ISSDQ, CMRT, ISA, GeoVIS, GeoBigData 28/09/2015 03/10/2015 La Grande Motte France ISPRS OA Archives Importance : pp 307 - 312 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] contraste de couleurs
[Termes IGN] image aérienne
[Termes IGN] image captée par drone
[Termes IGN] littoral
[Termes IGN] MicMac
[Termes IGN] modèle numérique de surfaceRésumé : (auteur) Coastal sandy environments are extremely dynamic and require regular monitoring that can easily be achieved by using an unmanned aerial system (UAS) including a drone and a photo camera. The acquired images have low contrast and homogeneous texture. Using these images and with very few, if any, ground control points (GCPs), it is difficult to obtain a digital surface model (DSM) by classical correlation and automatic interest points determination approach. A possible response to this problem is to work with enhanced, contrast filtered images. To achieve this, we use and tune the free open-source software MicMac. Numéro de notice : C2015-062 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprsarchives-XL-3-W3-307-2015 Date de publication en ligne : 19/08/2015 En ligne : https://doi.org/10.5194/isprsarchives-XL-3-W3-307-2015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91907 
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