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A computational introduction to digital image processing / Alasdair McAndrew (2016)
Titre : A computational introduction to digital image processing Type de document : Monographie Auteurs : Alasdair McAndrew Mention d'édition : Second edition Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2016 Importance : 535 p. Présentation : illustrations Format : 18 x 26 cm ISBN/ISSN/EAN : 978-1-4822-4732-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] compression par ondelettes
[Termes IGN] GNU Octave
[Termes IGN] image en couleur
[Termes IGN] Matlab
[Termes IGN] Python (langage de programmation)
[Termes IGN] restauration d'image
[Termes IGN] segmentation d'image
[Termes IGN] transformation de Fourier
[Termes IGN] voisinage (relation topologique)Index. décimale : 35.20 Traitement d'image Résumé : (Editeur) This book explores the nature and use of digital images and shows how they can be obtained, stored, and displayed. Taking a strictly elementary perspective, the book only covers topics that involve simple mathematics yet offer a very broad and deep introduction to the discipline. This second edition provides users with three different computing options. Along with MATLAB®, this edition now includes GNU Octave and Python. Users can choose the best software to fit their needs or migrate from one system to another. Programs are written as modular as possible, allowing for greater flexibility, code reuse, and conciseness. This edition also contains new images, redrawn diagrams, and new discussions of edge-preserving blurring filters, ISODATA thresholding, Radon transform, corner detection, retinex algorithm, LZW compression, and other topics. Based on the author’s successful image processing courses, this bestseller is suitable for classroom use or self-study. In a straightforward way, the text illustrates how to implement imaging techniques in MATLAB, GNU Octave, and Python. It includes numerous examples and exercises to give students hands-on practice with the material. Note de contenu :
1. Introduction
2. Images Files and File Types
3. Image Display
4. Point Processing
5. Neighborhood Processing
6. Image Geometry
7. The Fourier Transform
8. Image Restoration
9. Image Segmentation
10. Mathematical Morphology
11. Image Topology
12. Shapes and Boundaries
13. Color Processing
14. Image Coding and Compression
15. Wavelets
16. Special Effects
Appendix A: Introduction to MATLAB and Octave
Appendix B: Introduction to Python
Appendix C: The Fast Fourier TransformNuméro de notice : 22951 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91638 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 22951-01 35.20 Livre Centre de documentation Télédétection Disponible HYCA: A new technique for hyperspectral compressive sensing / G. Martin in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
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Titre : HYCA: A new technique for hyperspectral compressive sensing Type de document : Article/Communication Auteurs : G. Martin, Auteur ; José M. Bioucas-Dias, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2015 Article en page(s) : pp 2819 - 2831 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] capteur hyperspectral
[Termes IGN] compression d'image
[Termes IGN] coordonnées géographiques
[Termes IGN] corrélation
[Termes IGN] image hyperspectrale
[Termes IGN] reconstruction d'imageRésumé : (Auteur) Hyperspectral imaging relies on sophisticated acquisition and data processing systems able to acquire, process, store, and transmit hundreds or thousands of image bands from a given area of interest. In this paper, we exploit the high correlation existing among the components of the hyperspectral data sets to introduce a new compressive sensing methodology, termed hyperspectral coded aperture (HYCA), which largely reduces the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images, usually termed data cubes: 1) the spectral vectors live on a low-dimensional subspace; and 2) the spectral bands present high correlation in both the spatial and the spectral domain. The former property allows to represent the data vectors using a small number of coordinates. In this paper, we particularly exploit the high spatial correlation mentioned in the latter property, which implies that each coordinate is piecewise smooth and thus compressible using local differences. The measurement matrix computes a small number of random projections for every spectral vector, which is connected with coded aperture schemes. The reconstruction of the data cube is obtained by solving a convex optimization problem containing a data term linked to the measurement matrix and a total variation regularizer. The solution of this optimization problem is obtained by an instance of the alternating direction method of multipliers that decomposes very hard problems into a cyclic sequence of simpler problems. In order to address the need to set up the parameters involved in the HYCA algorithm, we also develop a constrained version of HYCA (C-HYCA), in which all the parameters can be automatically estimated, which is an important aspect for practical application of the algorithm. A series of experiments with simulated and real data shows the effectiveness of HYCA and C-HYCA, indicating their potential in real-world applications. Numéro de notice : A2015-520 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2365534 En ligne : https://doi.org/10.1109/TGRS.2014.2365534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77527
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2819 - 2831[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas / Esteban Aguilera in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)
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Titre : Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas Type de document : Article/Communication Auteurs : Esteban Aguilera, Auteur ; Matteo Nannini, Auteur ; Andreas Reigber, Auteur Année de publication : 2013 Article en page(s) : pp 5283 - 5295 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Allemagne
[Termes IGN] bande L
[Termes IGN] compression par ondelettes
[Termes IGN] données polarimétriques
[Termes IGN] forêt
[Termes IGN] image E-SAR
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] polarimétrie radar
[Termes IGN] tomographie radarRésumé : (Auteur) Synthetic aperture radar (SAR) tomography is a 3-D imaging modality that is commonly tackled by spectral estimation techniques. Thus, the backscattered power along the cross-range direction can be readily obtained by computing the Fourier spectrum of a stack of multibaseline measurements. In addition, recent work has addressed the tomographic inversion under the framework of compressed sensing, thereby recovering sparse cross-range profiles from a reduced set of measurements. This paper differs from previous publications, in that it focuses on sparse expansions in the wavelet domain while working with the second-order statistics of the corresponding multibaseline measurements. In this regard, we elaborate on the conditions under which this perspective is applicable to forested areas and discuss the possibility of optimizing the acquisition geometry. Finally, we compare this approach with traditional nonparametric ones and validate it by using fully polarimetric L-band data acquired by the Experimental SAR (E-SAR) sensor of the German Aerospace Center (DLR). Numéro de notice : A2013-696 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2231081 En ligne : https://doi.org/10.1109/TGRS.2012.2231081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32832
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 12 (December 2013) . - pp 5283 - 5295[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013121 RAB Revue Centre de documentation En réserve L003 Disponible A sparse image fusion algorithm with application to pan-sharpening / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)
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Titre : A sparse image fusion algorithm with application to pan-sharpening Type de document : Article/Communication Auteurs : Xiao Xiang Zhu, Auteur ; Richard Bamler, Auteur Année de publication : 2013 Article en page(s) : pp 2827 - 2836 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] compression d'image
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image Geoeye
[Termes IGN] image Ikonos
[Termes IGN] image panchromatique
[Termes IGN] image Quickbird
[Termes IGN] méthode robuste
[Termes IGN] pansharpening (fusion d'images)Résumé : (Auteur) Data provided by most optical Earth observation satellites such as IKONOS, QuickBird, and GeoEye are composed of a panchromatic channel of high spatial resolution (HR) and several multispectral channels at a lower spatial resolution (LR). The fusion of an HR panchromatic and the corresponding LR spectral channels is called “pan-sharpening.” It aims at obtaining an HR multispectral image. In this paper, we propose a new pan-sharpening method named Sparse Fusion of Images (SparseFI, pronounced as “sparsify”). SparseFI is based on the compressive sensing theory and explores the sparse representation of HR/LR multispectral image patches in the dictionary pairs cotrained from the panchromatic image and its downsampled LR version. Compared with conventional methods, it “learns” from, i.e., adapts itself to, the data and has generally better performance than existing methods. Due to the fact that the SparseFI method does not assume any spectral composition model of the panchromatic image and due to the super-resolution capability and robustness of sparse signal reconstruction algorithms, it gives higher spatial resolution and, in most cases, less spectral distortion compared with the conventional methods. Numéro de notice : A2013-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2213604 En ligne : https://doi.org/10.1109/TGRS.2012.2213604 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32397
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 5 Tome 1 (May 2013) . - pp 2827 - 2836[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013051A RAB Revue Centre de documentation En réserve L003 Disponible Manual of photogrammetry, sixth edition / J. Chris Mcglone (2013)
Titre : Manual of photogrammetry, sixth edition Type de document : Guide/Manuel Auteurs : J. Chris Mcglone, Éditeur scientifique ; George Y.G. Lee, Éditeur scientifique Mention d'édition : sixth edition Editeur : Bethesda [Maryland - Etats-Unis] : American Society for Photogrammetry and Remote Sensing ASPRS Année de publication : 2013 Importance : 1318 p. Format : 18 x 26 cm ISBN/ISSN/EAN : 978-1-57083-099-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] algèbre linéaire
[Termes IGN] analyse en composantes principales
[Termes IGN] analyse texturale
[Termes IGN] appariement d'images
[Termes IGN] archéologie aérienne
[Termes IGN] caméra numérique
[Termes IGN] capteur actif
[Termes IGN] capteur imageur
[Termes IGN] capteur optique
[Termes IGN] compression d'image
[Termes IGN] erreur systématique
[Termes IGN] estimation statistique
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] extraction automatique
[Termes IGN] filtrage numérique d'image
[Termes IGN] fonction de transfert de modulation
[Termes IGN] géométrie projective
[Termes IGN] histoire des sciences et techniques
[Termes IGN] instrument d'optique
[Termes IGN] instrument de photogrammétrie
[Termes IGN] matrice
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation géométrique de prise de vue
[Termes IGN] modélisation radiométrique de prise de vue
[Termes IGN] morphologie mathématique
[Termes IGN] photogrammétrie
[Termes IGN] photogrammétrie analytique
[Termes IGN] photogrammétrie architecturale
[Termes IGN] photogrammétrie métrologique
[Termes IGN] photogrammétrie numérique
[Termes IGN] radar imageur
[Termes IGN] radiomètre
[Termes IGN] réalité virtuelle
[Termes IGN] reconstruction 3D
[Termes IGN] rééchantillonnage
[Termes IGN] restauration d'image
[Termes IGN] station de travail de photogrammétrie numérique
[Termes IGN] système de coordonnées
[Termes IGN] système de numérisation mobile
[Termes IGN] traitement d'image
[Termes IGN] transformation géométriqueIndex. décimale : 33.00 Photogrammétrie - généralités Résumé : (Editeur) Under the leadership of J. Chris McGlone, PhD, CP, as Editor-in-Chief and George Y.G. Lee, PhD, Technical Editor, the Manual covers photogrammetry in depth, as well as its constituent technologies, providing the student, practitioner, or researcher with a single valuable reference resource. The topics addressed within the Manual include: • Mathematics: the perspective geometry which underlies the imaging process and its current usage in computer vision, the statistical modeling of measurement error, and the basic photogrammetric operations of resection, intersection, and triangulation, coordinate transformation • Image acquisition: the physics of optical systems and imaging chips, digital airborne and satellite sensors • Digital photogrammetry: image processing, computer vision, and their applications in photogrammetry • Photogrammetric operations: flight planning and GPS/INS utilization • Photogrammetric products: standard product types and formats and their associated accuracy standards • Current applications: mobile mapping vans, close-range industrial photogrammetry, space measurements, and forensic photogrammetry • Bibliography: each chapter has an extensive bibliography to guide further study. These topics are covered by contributing authors who combine years of experience with many aspects of photogrammetry and familiarity with the state-of-the-art; many of the authors have been pivotal in defining the current state-of-the-art of digital photogrammetry. Note de contenu : 1 A brief history of photogrametry
2 Mathematical concepts in photogrammetry
3 The mathematics of photogrammetry
4 Elements of photogrammetrics optics
5 Digital image processing
6 Basic computer vision techniques
7 Detectors and sensors
8 Cameras and sensing sytems
9 Photogrammetric platforms
10 Analytical photogrammetric operations
11 Measurement and automation practices in photogrammetry
12 Photogrammetric products
13 Photogrammetric applications
14 photogrammetric project and mission planning
Index
Color platesNuméro de notice : 15733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Manuel de cours Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=40738 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 15733-02 33.00 Livre Centre de documentation Photogrammétrie - Lasergrammétrie Disponible 15733-01 33.00 Livre Centre de documentation Photogrammétrie - Lasergrammétrie Disponible 15733-03 DEP-EXM Livre LASTIG Dépôt en unité Exclu du prêt Vers une nouvelle prise en compte de l'esthétique dans la composition de la carte thématique : propositions de méthodes et d'outils / Laurent Jégou (2013)PermalinkImagerie numérique / Christine Fernandez-Maloigne (2012)Permalinkvol 48 n° 11 - November 2010 - Special issue on hyperspectral image and signal processing (Bulletin de IEEE Transactions on geoscience and remote sensing) / Geoscience and remote sensing societyPermalinkAnalyse et traitement des signaux / E. Tisserand (2008)PermalinkIntroduction au traitement d'images / D. Lingrand (2008)PermalinkData compression techniques and formats / R. Wevers in Geoinformatics, vol 9 n° 8 (01/12/2006)PermalinkEnhancing a sequence of facial images combining multiple undersampled and compressed images / G. Scarmana in Photogrammetric record, vol 21 n° 114 (June - August 2006)PermalinkCompression des images hyperspectrales et son impact sur la qualité des données / E. Christophe (2006)PermalinkCours de photographie numérique / R. Bouillot (2006)PermalinkEffect of Jpeg2000 on the information and geometry content of aerial photo compression / J.K. Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 2 (February 2005)Permalink