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A scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1309 - 1345 Titre : A scalable method to construct compact road networks from GPS trajectories Type de document : Article/Communication Auteurs : Yuejun Guo, Auteur ; Anton Bardera, Auteur ; Marta Fort, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1309 - 1345 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] chevauchement [Termes IGN] compensation par faisceaux [Termes IGN] contour [Termes IGN] généralisation automatique de données [Termes IGN] méthode heuristique [Termes IGN] noeud [Termes IGN] réseau routier [Termes IGN] segmentation par décomposition-fusion [Termes IGN] squelettisation [Termes IGN] trajectographie par GPS [Termes IGN] trajectoire (véhicule non spatial) Résumé : (auteur) The automatic generation of road networks from GPS tracks is a challenging problem that has been receiving considerable attention in the last years. Although dozens of methods have been proposed, current techniques suffer from two main shortcomings: the quality of the produced road networks is still far from those produced manually, and the methods are slow, making them not scalable to large inputs. In this paper, we present a fast four-step density-based approach to construct a road network from a set of trajectories. A key aspect of our method is the use of an improved version of the Slide method to adjust trajectories to build a more compact density surface. The network has comparable or better quality than that of state-of-the-art methods and is simpler (includes fewer nodes and edges). Furthermore, we also propose a split-and-merge strategy that allows splitting the data domain into smaller regions that can be processed independently, making the method scalable to large inputs. The performance of our method is evaluated with extensive experiments on urban and hiking data. Numéro de notice : A2021-447 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1832229 Date de publication en ligne : 16/10/2020 En ligne : https://doi.org/10.1080/13658816.2020.1832229 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97859 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2021071	SL	Revue	Centre de documentation	Revues en salle	Disponible

Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective / Mohammad D. Hossain in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 115 - 134 Titre : Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective Type de document : Article/Communication Auteurs : Mohammad D. Hossain, Auteur ; Dongmei Chen, Auteur Année de publication : 2019 Article en page(s) : pp 115 - 134 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse d'image orientée objet [Termes IGN] appariement de données localisées [Termes IGN] apprentissage automatique [Termes IGN] classification hybride [Termes IGN] image à haute résolution [Termes IGN] objet géographique [Termes IGN] segmentation d'image [Termes IGN] segmentation en régions [Termes IGN] segmentation par décomposition-fusion Résumé : (Auteur) Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques. Numéro de notice : A2019-138 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.009 Date de publication en ligne : 23/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92469 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019041	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019043	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019042	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Color image processing and applications / K.N. Plataniotis (2000)

Public Titre : Color image processing and applications Type de document : Guide/Manuel Auteurs : K.N. Plataniotis, Auteur ; A.N. Venetsanopoulos, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2000 Importance : 353 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 3-540-6695361 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] accentuation de contours [Termes IGN] amélioration des couleurs [Termes IGN] classification du maximum a posteriori [Termes IGN] classification par nuées dynamiques [Termes IGN] classification pixellaire [Termes IGN] détection de contours [Termes IGN] espace colorimétrique [Termes IGN] filtrage numérique d'image [Termes IGN] filtre adaptatif [Termes IGN] image en couleur [Termes IGN] image RVB [Termes IGN] restauration d'image [Termes IGN] segmentation fondée sur les contours [Termes IGN] segmentation par décomposition-fusion [Termes IGN] seuillage d'image [Termes IGN] traitement d'image [Termes IGN] transformation en cosinus discrète [Termes IGN] uniformisation d'histogramme Index. décimale : 35.20 Traitement d'image Résumé : (Editeur) In digital signal processing, numerous powerful algorithms, both linear and nonlinear, have been developed during the past three decades. These have given rise to tremendous progress in speech and image processing. But digital processing is not restricted to communications and information processing. It also plays a leading role in such diverse fields as measurement, automatic control, robotics, medicine, biology, and geophysics, to mention just the more important ones. The projected book series will cover the entire field of contemporary digital signal processing, ranging from theory to applications, treating linear and non-linear methods for one- and higher-dimensional signals. Note de contenu : 1. COLOR SPACES 1.1 Basics of Color Vision. 1.2 The CIE Chromaticity-basedModels. 1.3 The CIE-RGB Color Model 1.4 Gamma Correction 1.5 Linear and Non-linear RGB Color Spaces 1.6 Color Spaces Linearly Related to the RGB 1.7 The YIQ Color Space 1.8 The HSI Family of Color Models 1.9 Perceptually Uniform Color Spaces 1.10 The Munsell Color Space 1.11 The Opponent Color Space 1.12 New Trends 1.13 Color Images 1.14 Summary 2 COLOR IMAGE FILTERING 2.1 Introduction 2.2 Color Noise 2.3 Modeling Sensor Noise 2.4 Modeling Transmission Noise 2.5 Multivariate Data Ordering Schemes 2.6 A Practical Example 2.7 Vector Ordering 2.8 The Distance Measures 2.9 The Similarity Measures 2.10 Filters Based on Marginal Ordering 2.11 Filters Based on Reduced Ordering 2.12 Filters Based on Vector Ordering 2.13 Directional-based Filters 2.14 Computational Complexity 2.15 Conclusion 3. ADAPTIVE IMAGE FILTERS 3.1 Introduction 3.2 The Adaptive Fuzzy System 3.3 The Bayesian Parametric Approach 3.4 The Nonpaxametric Approach 3.5 Adaptive Morphological Filters 3.6 Simulation Studies 3.7 Conclusions 4. Color Edge Detection 4.1 Introduction 4.2 Overview Of Color Edge Detection Methodology 4.3 Vector Order Statistic Edge Operators 4.4 Difference Vector Operators 4.5 Evaluation Procedures and Results 4.6 Conclusion 5. COLOR IMAGE ENHANCEMENT AND RESTORATION 5.1 Introduction 5.2 Histogram Equalization 5.3 Color Image Restoration 5.4 Restoration Algorithms 5.5 Algorithm Formulation 5.6 Conclusions 6. Color Image Segmentation 6.1 Introduction 6.2 Pixel-based Techniques 6.2.1 Histogram Thresholding 6.2.2 Clustering 6.3 Region-based Techniques 6.3.1 Region Growing 6.3.2 Split and Merge 6.4 Edge-based Techniques 6.5 Model-based Techniques 6.5.1 The Maximum A-posteriori Method 6.5.2 The Adaptive MAP Method 6.6 Physics-based Techniques 6.7 Hybrid Techniques 6.8 Application 6.8.1 Pixel Classification 6.8.2 Seed Determination 6.8.3 Region Growing 6.8.4 Region Merging 6.8.5 Results 6.9 Conclusion 7 COLOR IMAGE COMPRESSION 7.1 Introduction 7.2 Image Compression Comparison Terminology 7.3 Image Representation for Compression Applications 7.4 Lossless Waveform-based Image Compression Techniques 7.5 Lossy Waveform-based Image Compression Techniques 7.6 Second Generation Image Compression Techniques 7.7 Perceptually Motivated Compression Techniques 7.8 Color Video Compression 7.9 Conclusion 8. EMERGING APPLICATIONS 8.1 Input Analysis Using Color Information 8.2 Shape and Color Analysis 8.2.1 Fuzzy Membership Functions 8.2.2 Aggregation Operators 8.3 Experimental Results 8.4 Conclusions Numéro de notice : 11369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Manuel Accessibilité hors numérique : Non accessible via le SUDOC Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=46094